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Peran sistem pendataan keluarga dalam rangka mendukung pencapaian program BANGGAKENCANA pada BKKBN Propinsi Jawa Barat adalah untuk menyediakan data dasar sebagai perencanaan strategis, peta kerja, maupun pemetaan mekanisme operasional menjadi krusial untuk menopang keberhasilan program. Untuk memastikan delivery produk, layanan, dan dukungan yang dihasilkan perlu dilakukan evaluasi sehingga dapat menghasilkan tata kelola sistem pendataan keluarga yang lebih optimal. Penelitian ini bertujuan untuk memperoleh ukuran capability level proses TI saat ini kemudian dibandingkan dengan yang diharapkan sehingga dapat diperoleh kesenjangan guna penyusunan rekomendasi yang dapat diimplementasikan pada Siklus Sistem Pendataan Keluarga Tahun 2021 dengan menggunakan kerangka kerja COBIT 5. Data yang diperoleh bersumber dari responden yang berwenang dan kompeten berdasarkan pemetaan diagram RACI pada COBIT 5. Dari hasil penelitian, diketahui bahwa dari 6 proses terpilih pada domain DSS terdapat 4 proses yang mencapai level 1 (performed) dan 2 proses yang berhasil mencapai level 2 (managed process). Tingkat kemampuan manajemen TI yang diharapkan berada pada level 3 sehingga GAP muncul di semua domain yang bermakna saat ini organisasi belum sepenuhnya mengimplementasikan proses yang ditetapkan untuk mencapai tujuan proses. Dari seluruh rangkaian penelitian tersebut dapat disimpulkan bahwa diperoleh nilai level kapabilitas sebesar 1,33 dan terdapat GAP sebesar 1,67 untuk mencapai level yang diharapkan. Dalam penelitian ini disampaikan rekomendasi untuk meningkatkan kapabilitas proses sehingga BKKBN Propinsi Jawa Barat dapat mencapai tingkat kapabilitas yang diharapkan.

Many real-world situations such as bad weather may result in hazy environments. Images captured in these hazy conditions will have low image quality due to microparticles in the air. The microparticles light to scatter and absorb, resulting in hazy images with various effects. In recent years, image dehazing has been researched in depth to handle images captured in these conditions. Various methods were developed, from traditional methods to deep learning methods. Traditional methods focus more on the use of statistical prior. These statistical prior have weaknesses in certain conditions. This paper proposes a novel architecture based on PDR-Net by using a pyramid dilated convolution and pre-processing modules, processing modules, post-processing modules, and attention applications. The proposed network is trained to minimize L1 loss and perceptual loss with the O-Haze dataset. To evaluate our architecture's result, we used structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and color difference as an objective assessment and psychovisual experiment as a subjective assessment. Our architecture obtained better results than the previous method using the O-Haze dataset with an SSIM of 0.798, a PSNR of 25.39, but not better on the color difference. The SSIM and PSNR results were strengthened by using subjective assessments and 65 respondents, most of whom chose the results of the restoration of the image produced by our architecture.

The Data Envelopment Analysis (DEA) method is a method commonly used in benchmarking. The Dynamic Data Envelopment Analysis (DDEA) method was proposed to improve the DEA method in the benchmarking process. The DDEA method proposed can determine the effectiveness of the Decision Making Unit (DMU). The disadvantage of the DDEA model is that it cannot handle problems that involve benchmarking for stochastic data. To improve the DDEA method, the Stochastic Data Envelopment Analysis (SDEA) method is proposed which can be used for benchmarking involving stochastic data. The SDEA method itself has weaknesses in dealing with noise and uncertainty problems that will appear in the assessment process. The purpose of the research conducted by the researcher was to use the Hesitant Fuzzy method in optimizing the SDEA method so that the Hesitant Fuzzy model - Stochastic Data Envelopment Analysis (HF-SDEA) could be carried out benchmarking process in a situation where the assessment contained many elements of uncertainty. The results of this study are benchmarking methods that can do benchmarking for stochastic data on conditions that contain elements of uncertainty.

One of the most important and potential problems encountered in an official online letter management application is the recipients' late response. This action research aims to improve the recipients' response time and determine the central core of shyness according to response time categories: less than 24 hours (green zone); between 24 hours to 48 hours (orange zone); and more than 48 hours (red zone) in managing formal online letter management system at Jambi University. Using the social representation theory of shyness as the basis of action adopted from Moscovici theory [1], it is hypothesized that response time displaying will impact response time improvement. A survey questionnaire distributed to 129 respondents showed a significant improvement in the participants' response time, respectively, in cycles 1 and 2. The zone group increased percentage sed from 22.44% to 52.49 % in the first cycle, and it ultimately raised to 62.38% at the end of the second cycle. Such an increase might be due to the users' efforts to avoid the level of shyness, which were classified into: (1) slow to respond and (2) personal or work unit late response time – both marked in red. It is recommended that social shyness incorporated in improving response time in a formal online letter can be further implemented with other social and psychological parameters. The application will illustrate computer systems' interaction on social society to implement regulations both in institutions and in government.

A company maintains and improves its quality services by paying attention to reviews and complaints from users. The complaints from users are commonly written using human natural language expression so that their messages are computationally difficult to extract and proceed. To overcome this difficulty, in this study, we presented a new system for issues feature extraction from users’ reviews and complaints from social media data. This system consists of four main functions: (1) Data Crawling and Preprocessing, (2) Categorization Knowledge Modelling, (3) Rule-based Sentiment Analysis, and (4) Application Environment. Data Crawling and Preprocessing provides data acquisition from users’ tweets on social media, crawls the data and applies the data preprocessing. Categorization Knowledge Modelling provides text mining of textual data, vector space transformation to create knowledge metadata, context recognition of keyword queries to the knowledge metadata, and similarity measurement for categorization. In the Rule-based Sentiment Analysis, we developed our own rules of computatioal linguistics to measure polarity of sentiment. Application Environment consists of 3 layers: database management, back-end services and front-end services. For applicability of our proposed system, we conducted two kinds of experimental study: (1) categorization performance, and (2) sentiment analysis performance. For categorization performance, we used 8743 tweet data and performed 82% of accuracy. For categorization performance, we made experiments on 217 tweet data and performed 92% of accuracy.

Entrepreneurship plays an essential role in the economic growth of a country. These roles include creating jobs, reducing unemployment, increasing people's income, combining production factors (nature, labor, capital, and expertise), and increasing national productivity. For the economy to thrive and healthy, it requires at least 4% of the population who work as entrepreneurs. Due to this vital role, entrepreneurial growth must be maintained. One of the efforts to do this is by monitoring growth directly and continuously. Besides that, another way is to do a simulation. By knowing the condition of entrepreneurship at one time and all the factors that affect entrepreneurial growth, simulations can be carried out to determine or predict future conditions. Based on this simulation, essential steps can be taken, or policies can be made to maintain profitable entrepreneurial growth. This paper presents a mathematical model that can simulate and visualize entrepreneurship's growth in six provinces of Sumatra Island, Indonesia. This mathematical model uses cellular automata as its basis and is called Entrepreneurial Cellular Automata (ECA). One of the advantages of Cellular Automata is that it is easy to visualize. The entrepreneurial model used as a reference is a model from the Global Entrepreneurship Monitoring (GEM). This mathematical model has been implemented in a simulator program. This paper describes the simulator development and the use of simulator to simulate and visualize the entrepreneurial growth of the six provinces.

Universities around the world are actively implementing smart campuses. A smart campus is a campus environment capable of providing efficient technology and infrastructure in providing services to support and improve the teaching process, research, and student experience. It comprises initiatives to better support and enhances the better experience in the teaching and learning process and other services in the campus environment. To successfully implement the initiatives, a framework is required to define the scope of the implementation. Several universities in Malaysia are currently developing initiatives to implement their smart campus. This paper surveyed the literature and resources from universities in Malaysia to identify smart campus initiatives implemented following the smart campus domain. Due to the lack of resources available in the prominent database of indexed journal articles, the main source of review is based on official university sources such as official websites and so on. The result shows that all universities implemented all smart campus domains. Smart Management domain has the highest number of 58% of the overall initiatives. The second highest domain is Smart Learning at 13%, followed by Green Campus at 10%. We also identify that there is new domain of smart campus that was introduced. The new domain is Smart Research. Based on the survey, most universities in Malaysia are actively improving their work processes and the environment by implementing smart campus.

Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters. Fuzzy k-means Algorithm is one of clustering algorithm by partitioning data into k clusters employing Euclidean distance as a distance function. This research discusses clustering categorical data using Fuzzy k-Means Kullback-Leibler Divergence. In the determination of the distance between data and center of cluster uses mutual information known as Kullback-Leibler Divergence distance between the joint distribution and the product distribution from two marginal distributions. Extensive theoretical analysis was performed to show the effectiveness of the proposed method. Moreover, the proposed method's comparison results with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy were also performed employing several datasets from UCI Machine Learning. The experiment results show that the proposed Algorithm provides good results both from clustering quality and accuracy for clustering categorical data as compared to Fuzzy Centroid and Fuzzy k-Partition.

Smart City Mobile Apps is currently being developed by property developers in major cities in Indonesia and is used by businesses to market their products through the application. Technology integration in city governance is possible, thanks to the internet of things, a network of electronic devices interconnected and capable of sending data or following up with minimal human intervention. This study aims to identify the characteristics of Smart City Mobile Apps users in the South Tangerang area. The method used to see how well the implementation of digital marketing in Smart City Mobile Apps combines the TAM (Technology Acceptance Model) and UTAUT (Unified Theory of Acceptance and Use of Technology) models. Questionnaire data were processed using the Structural Equation Modelling (SEM) method. The study results state that the company's digital marketing strategy through Smart City Mobile Apps is already satisfying. It can be seen from the value of adoption of Smart City Mobile Apps users in utilizing this application, which is considerably large at 0.764 based on the coefficient of determination. The attitude variable influences consumers to use the application in finding trade information in the Smart City, with an influence value of 0.412 and the behavior intention to use with an influence value of 0.726, which shows that the intended behavior of the application and the trust in the benefits of the application can encourage users to use this application for purchasing their daily needs.

Agent based simulation (ABS) is a paradigm to modelling systems included of autonomous and interacting agents. ABS has been tremendous growth and used by researchers in the social sciences to study socio-environmental complex systems. To date, various platforms have been introduced for agent-based social simulation. They are rule based in any logic, python based in SPADE and etc. Although those platforms have been introduced, there is still an insufficient to develop a crowd simulation in 3D platform. Having a 3D platform is needed to enabling the crowd simulation for training purposes. However, the current tools and platform still lack features to develop and simulate autonomous agents in the 3D world. This paper introduced a BDI plug in at Unity3D for crowd simulation. BDI is an intelligent agent architecture and it is able to develop autonomous agents in crowd environment. In this paper, we present the BDI plug with a case study of Australia bush fire and discuss a method to support autonomous agents' development in 3D crowd simulation. The tool allows the modeller to develop autonomous agents in 3D world by taking the advantages of Unity3D.

To make a communication with a hearing-impaired person, who is someone who has a problem with hearing ability, a special form of communication using sign language is required in order to make the purpose of the conversation convey properly. It is clearly that providing a proper and appropriate education for hearing impaired person is very important. Android technology is the best and useful solution for hearing-impaired person in learning as today's technological developments. The purpose of this research was to make an Android-based application for hearing impaired person. Scrum method was used to find and utilize existing libraries as well as the needs for application development. This application provides various kinds of subject from videos and documents uploaded by the teachers. It can be downloaded of it, if it is needed. It means that the users can study anytime and anywhere without concerning of limited time and internet access. Other features of the application are quiz, make schedule, event, chat, memory game, and other features to maximize the online learning process for hearing-impaired person.

Class imbalance is one of the main problems in classification because the number of samples in majority class is far more than the number of samples in minority class. The class imbalance problem in the multi-class dataset is much more difficult to handle than the problem in the two class dataset. This multi-class imbalance problem is even more complicated if it is accompanied by overlapping. One method that has proven reliable in dealing with this problem is the Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method which is classified as a hybrid approach which combines sampling and classifier ensembles. However, in terms of diversity among classifiers, hybrid approach that combine sampling and classifier ensembles will give better results. HAR-MI delivers excellent results in handling multi-class imbalances. The HAR-MI method uses SMOTE to increase the number of sample in minority class. However, this SMOTE also has a weakness where if there is an extremely imbalanced dataset and a large number of attributes there will be over-fitting. To overcome the problem of over-fitting, the Hybrid Sampling method was proposed. HAR-MI combination with Hybrid Sampling is done to increase the number of samples in the minority class and at the same time reduce the number of noise samples in the majority class. The preprocessing stages at HAR-MI will use the Minimizing Overlapping Selection under Hybrid Sazmpling (MOSHS) method and the processing stages will use Different Contribution Sampling. The results obtained will be compared with the results using Neighbourhood-based undersampling. Overlapping and Classifier Performance will be measured using Augmented R-Value, the Matthews Correlation Coefficient (MCC), Precision, Recall, and F-Value. The results showed that HAR-MI with Hybrid Sampling gave better results in terms of Augmented R-Value, Precision, Recall, and F-Value.

Cloud computing is one of the enablers of Industrial Revolution 4.0 (IR.40). IR 4.0 is advantageous as it allows companies to increase performance and productivity. However, there are many enablers of IR 4.0, such as big data analytics, cloud computing, machine learning, and blockchain. However, the readiest to be used technology is cloud computing. While the advantages of cloud computing are well understood from the perspective of the literature and companies' point of view, the empirical evidence is still scarce. The research explores the drivers of cloud adoption between small and medium-sized enterprises (SMEs) in Indonesia. The study method is a quantitative method through e-survey data collection analyzed using IBM SPSS and Smart PLS software. The recognition of drivers will allow IT decision-makers to design the right platform for SMEs, increasing their company competitiveness. The findings revealed that cloud flexibility, perceived concern, privacy, relative advantage, perceived cost-benefit, quality of service, and top management support are among the top cloud adoption priorities that need to be improved in the creative industry to ensure the adoption of cloud computing more apparent. The study's contribution revealed that cloud computing is no longer at the infant stage in terms of adoption. Thus, the findings paved the way for scholars to undertake future research focusing on cloud computing implementation. Companies, on the other hand, can learn from this research by improving the adoption aspects.

MDM is a trade cooperative business unit that supplies healthy food options for consumers around the Andalas University campus. So far, the selection of suppliers that provide supply goods to the stores is only based on the trust between both parties, which is the principle of mutual acquaintance and kinship. The problems that may arise from a process like this are the lack of the right supplier, unavailability of goods, relatively higher product prices, late delivery, and low-quality goods. Therefore, we need a GDSS that is capable of overcoming these problems. This GDSS helps in decision-making by determining the right supplier for each of the stores owned by the MDM Cooperative. The methods used are AHP, TOPSIS, and BORDA, involving six criteria and five tested alternatives. The AHP method is used to obtain the weight of each criterion taken from the pairwise comparison matrix. The TOPSIS method is used to determine which suppliers get priority for supply goods. Combining the AHP and TOPSIS methods can reduce the weaknesses of the TOPSIS itself by giving subjective weights. The use of the BORDA method can provide maximum results in selecting this supplier. This GDSS also involves three decision-making bodies: the head of the cooperative, the deputy, and the treasurer. The results of this prototype can show the best alternative selected based on the ranking method.

In this study, the relationship of orienting of attention, motor control and the Stimulus- (SDN) and Goal-Driven Networks (GDN) was explored through an innovative method for fMRI analysis considering all voxels in four experimental conditions: standard target (Goal; G), novel (N), neutral (Z) and noisy target (NG). First, average reaction times (RTs) for each condition were calculated. In the second-level analysis, ‘distracted’ participants, as indicated by slower RTs, evoked brain activations and differences in both hemispheres’ neural networks for selective attention, while the participants, as a whole, demonstrated mainly left cortical and subcortical activations. A context analysis was run in the behaviourally distracted participant group contrasting the trials immediately prior to the G trials, namely one of the Z, N or NG conditions, i.e. Z.G, N.G, NG.G. Results showed different prefrontal activations dependent on prior context in the auditory modality, recruiting between 1 to 10 prefrontal areas. The higher the motor response and influence of the previous novel stimulus, the more prefrontal areas were engaged, which extends the findings of hierarchical studies of prefrontal control of attention and better explains how auditory processing interferes with movement. Also, the current study addressed how subcortical loops and models of previous motor response affected the signal processing of the novel stimulus, when this was presented laterally or simultaneously with the target. This multitasking model could enhance our understanding on how an auditory stimulus is affecting motor responses in a way that is self-induced, by taking into account prior context, as demonstrated in the standard condition and as supported by Pulvinar activations complementing visual findings. Moreover, current BCI works address some multimodal stimulus-driven systems. Supplementary Information The online version contains supplementary material available at 10.1186/s40708-021-00124-6.

Photoacoustic microscopy is an in vivo imaging technology based on the photoacoustic effect. It is widely used in various biomedical studies because it can provide high-resolution images while being label-free, safe, and harmless to biological tissue. Polygon-scanning is an effective scanning method in photoacoustic microscopy that can realize fast imaging of biological tissue with a large field of view. However, in polygon-scanning, fluctuations of the rotating motor speed and the geometric error of the rotating mirror cause image distortions, which seriously affect the photoacoustic-microscopy imaging quality. To improve the image quality of photoacoustic microscopy using polygon-scanning, an image correction method is proposed based on accurate ultrasound positioning. In this method, the photoacoustic and ultrasound imaging data of the sample are simultaneously obtained, and the angle information of each mirror used in the polygon-scanning is extracted from the ultrasonic data to correct the photoacoustic images. Experimental results show that the proposed method can significantly reduce image distortions in photoacoustic microscopy, with the image dislocation offset decreasing from 24.774 to 10.365 μm.

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.

Dinas Tenaga Kerja (DISNAKER) Kota Samarinda merupakan salah satu Organisasi Perangkat Daerah (OPD) yang bertugas untuk melayani masyarakat pada bidangnya. Salah satu layanan di DISNAKER yaitu pembuatan peraturan perusahaan, dimana proses pembuatannya masih secara manual. Diawali perusahaan membawa berkas-berkas yang diperlukan ke DISNAKER, kemudian perusahaan menunggu sekitar 3-4 hari atau bahkan lebih yang dikarenakan tertumpuknya berkas-berkas. Jika dikabarkan kekurangan berkas atau revisi, maka perusahaan tersebut perlu melengkapi atau merevisi dan memberikan kembali ke DISNAKER. Hal ini tentunya membutuhkan waktu yang lama dalam proses pembuatan peraturan perusahaan. Oleh karena itu, dilakukan penelitian untuk merancang dan membangun Sistem Informasi Pembuatan Peraturan Perusahaan (3P) berbasis website dengan menggunakan metode Rapid Application Development (RAD). Metode RAD memiliki 4 fase dalam pembangunan sistem yaitu, fase perencanaan syarat-syarat, fase desain pengguna, fase konstruksi, dan fase pelaksana. Sistem yang telah dibuat berhasil membuat surat keputusan sesuai dengan perusahaan yang mengajukan, dimana surat keputusan merupakan surat yang menyatakan peraturan perusahaan tersebut telah disetujui. Dengan demikian sistem ini diharapkan dapat membantu perusahaan dalam pembuatan peraturan perusahaan tanpa datang ke DISNAKER dan mengurangi biaya mencetak berkas serta dapat membantu DISNAKER mempercepat pembuatan peraturan perusahaan.

Salah satu yang menjadi kunci suksesnya manajemen sediaan adalah tersedianya produk yang sesuai dengan jumlah permintaan (demand), namun juga tetap memperhatikan kapasitas penyimpanan produk. Dengan jumlah demand yang dapat diramalkan sebelumnya, maka pihak manajemen dapat mengatur pengadaan barang secara lebih terencana. Salah satu metode peramalan kuantitatif adalah Simple Moving Average, namun metode ini tidak memperhitungkan faktor event yang bisa menyebabkan berbedanya hasil peramalan. Faktor event yang dimaksud adalah hari besar atau hari libur atau musim tertentu yang menyebabkan lonjakan/penurunan jumlah demand secara tidak wajar. Penelitian ini mengusulkan metode Modified Moving Average (MoMoA), sebagai pengembangan dari metode Simple Moving Average dengan menambahkan perhitungan faktor event. Metode kuantitatif yang akan digunakan sebagai pembanding dalam penelitian ini adalah metode Simple Moving Average, Single Exponential Smoothing, dan Least Square Trend. Evaluasi dilakukan dengan melakukan peramalan menggunakan empat macam metode pada periode terjadinya event hari Raya Idul Fitri dan membandingkan hasilnya dengan data penjualan riil. Hasil evaluasi menunjukkan bahwa metode MoMoA memberikan hasil 58% lebih baik dibandingkan metode lainnya, yaitu hasil peramalan dengan metode ini lebih mendekati riil dibandingkan dengan ketiga metode lainnya. Pada akhirnya, diharapkan penggunaan metode ini dapat membantu perusahaan dalam mengatur sediaan produk secara lebih terencana.

Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter 𝐶(complexity) = 10 dan 𝛾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.

Background: The brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. Researchers in cognitive computational neuroscience generally focused on one specific task, lack this global view and a clear understanding of how their model is inserted in a global cognitive architecture. A systemic description of the brain is presented here, as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. Results: In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the frontal cortex. Conclusions: This framework highlights that responses can be prepared in different ways, from pavlovian reflexes and habitual behavior to deliberations for goal-directed planning and reasoning, and explains that these different kinds of responses coexist, collaborate and compete for the control of behavior. It also lays emphasis on the fact that cognition can be described as a dynamical system of interacting memories, some acting to provide information to others, to replace them when they are not efficient enough, or to help for their improvement. Describing the brain as an architecture of learning systems has also strong implications in Machine Learning. Our biologically informed view of pavlovian and instrumental conditioning can be very precious to revisit classical Reinforcement Learning and provide a basis to ensure really autonomous learning.

Aturan dan komponen penggajian dari setiap perusahaan berbeda-beda disesuaikan dengan kondisi perusahaan. Aturan penggajian yang telah ditetapkan seringkali mengalami perubahan akibat adanya perubahan peraturan pemerintah ataupun kebijakan perusahaan. Hal tersebut mengakibatkan program penggajian yang digunakan oleh perusahaan perlu disesuaikan. Penyesuaian dapat dilakukan dengan memanggil pengembang program sehingga membutuhkan waktu yang relatif lebih lama dan biaya yang lebih besar. Untuk mengatasi permasalahan yang ada, dikembangkan sebuah program penggajian yang dapat dikonfigurasi sendiri oleh pengguna sistem sehingga tidak perlu memanggil pengembang program. Uji coba dilakukan dengan menggunakan metode black box testing dan simulasi. Pengujian dengan black box testing dilakukan dengan menggunakan berbagai macam skenario untuk mewakili setiap kemungkinan output dan mencocokkan hasilnya dengan desain yang telah dirancang sebelumnya. Setelah pengujian dengan black box testing berhasil, pengujian dilanjutkan dengan simulasi. Simulasi dilakukan dengan cara menerapkan fitur konfigurasi pada 3 buah sistem penggajian dari 3 jenis perusahaan yang berbeda. Hasil simulasi menunjukkan bahwa fasilitas konfigurasi pada sistem penggajian memungkinkan program yang dikembangkan memenuhi kebutuhan berbagai macam sistem penggajian dari setiap responden dan memudahkan pengguna dalam mengatur setiap faktor dan komponen penggajian seperti lembur, tunjangan dan potongan yang akan digunakan dalam sistem penggajian perusahaan tersebut. Hal ini menunjukkan adanya kemudahan dalam pengembangan sistem penggajian di kemudian hari secara mandiri sehingga dapat menghemat waktu dan biaya.

Kegiatan rapat merupakan hal yang dilakukan secara berkala di setiap organisasi. Rapat tersebut diselenggarakan dengan tujuan untuk menyampaikan perkembangan tugas setiap divisi yang akan dicatat oleh seorang notulis. Pencatatan tersebut digunakan sebagai arsip organisasi yang biasanya disebut notulen rapat. Namun terdapat beberapa kendala yang dihadapi dalam penyusunan notulen rapat. Salah satu kendala tersebut misalnya notulis berhalangan hadir dalam rapat sehingga dibutuhkan orang yang menggantikan posisi notulis. Kendala lainnya adalah kecepatan mengetik atau menulis dari notulis yang belum tentu dapat menyamai kecepatan pembahasan dalam rapat, sehingga ada resiko informasi yang terlewat. Oleh sebab itu, dikembangkan sebuah sistem asisten rapat berbasis teknologi speech-to-text dari Google Cloud API. Tujuan dari pengembangan sistem ini adalah untuk mempermudah proses pembuatan notulen dengan melakukan ekstraksi kata-kata penting dari suatu rapat. Hasil pengujian menunjukkan bahwa sistem asisten rapat dapat menangani perekaman dan pengambilan percakapan penting secara ekstraktif. Sistem dapat mencapai akurasi tertinggi sebesar 81,3% ketika kualitas percakapan peserta memiliki struktur kalimat yang baik serta konteks pembahasan agenda terstruktur dengan baik.

Natural language processing (NLP) is a branch of computer science concerned with the understanding of human language and communication, and translating these into a computer-comprehensible embedding. Our goal in this paper is to capture meaning from human natural language through NLP and provide an automated solution for aiding the process of service ticket solving, through the intelligent classification of tickets, pattern recognition and similarities between texts. The difficulty of this task lies in translating the human language into a mathematical format: transforming a non-formal language, into a formal one, without losing any details. Also what raises even more complication is the context in which this language appears: service tickets, that come from a technical and specialized jargon of computer science and IT industry, and the brief manner in which the tickets are written. This paper aims to tackle this challenge through multiple methods of text classification and recognition, and data analysis, followed by comparison and interpretation of the results. In completion, we find that our methods yield plausible results to be implemented in helping the service process.

This paper investigates the performance of a class of irregular low-density parity-check (LDPC) codes through a recently published low complexity upper bound on their belief propagation decoding thresholds. Moreover, their performance analysis is carried out through a recently published algorithmic method, presented in Babich et al. 2017 paper. In particular, the class considered is characterized by variable node degree distributions (lambda(x)) of minimum degree (i_1 gt 2): being, in this case, (lambda^{'} (0)=lambda_2=0), this is useful to design LDPC codes presenting a linear minimum distance growth with the block length with probability 1, as shown in Di et al.’s 2006 paper. These codes unfortunately cannot reach capacity under iterative decoding, since the achievement of capacity requires (lambda_2 neq 0). However, in this latter case, the block error probability might converge to a constant, as shown in the aforementioned paper.

In a passive radar system, localizing a target in Cartesian space is achieved by using one of the following bistatic geometries: multiple non-cooperative transmitters with one receiver, one non-cooperative transmitter with multiple receivers, or one non-cooperative transmitter with one receiver. In this paper, we propose a new method for localizing a target in Cartesian space by passive radar having the bistatic geometry “one non-cooperative transmitter and one receiver”. This method depends on using two consecutive particle filters for estimating and analyzing the Doppler frequency and time delay of the target’s echo signal. The theoretical analysis of the proposed method is presented, and its efficiency is verified by simulating the passive radar system with a Digital Video Broadcasting-Terrestrial (DVB-T) transmitter.

Mobile ad-hoc network is a collection of dynamically organized nodes where each node acts as a host and router. Mobile ad-hoc networks are characterized by the lack of preexisting infrastructures or centralized administration. So, they are vulnerable to several types of attacks, especially the Blackhole attack. This attack is one of the most serious attacks in this kind of mobile networks. In this type of attack, the malicious node sends a false answer indicating that it has the shortest path to the destination node by increasing the sequence number and decreasing the number of hops. This will have a significant negative impact on source nodes which send their data packets through the malicious node to the destination. This malicious node drops received data packets and absorbs all network traffic. In order overcome this problem, securing routing protocols become a very important requirement in mobile ad-hoc networks. Multi-path routing protocols are among the protocols affected by the Blackhole attack. In this paper, we propose an effective and efficient technique that avoids misbehavior of Blackhole nodes and facilitates the discovery for the most reliable paths for the secure transmission of data packets between communicating nodes in the well known Ad hoc On-demand multi-path routing protocol (AOMDV). Our proposed technique is implemented and simulated using the well known ns 2.35 simulator. We also compared the performance of the three routing protocols AOMDV, AOMDV under Blackhole attack (BHAOMDV) and the proposed solution to counter the Blackhole attack (IDSAOMDV). The results show the degradation of performance of AOMDV under attack, it also present similarities between normal AOMDV and the proposed solution by isolating misbehaving node which has resulted in increase the performance metrics to the standard values of the AOMDV protocol.

Software companies usually develop a set of product variants within the same family that share certain functions and differ in others. Variations across software variants occur to meet different customer requirements. Thus, software product variants evolve overtime to cope with new requirements. A software engineer who deals with this family may find it difficult to understand the evolution scenarios that have taken place over time. In addition, software identifier names are important resources to understand the evolution scenarios in this family. This paper introduces an automatic approach called Juana's approach to detect the evolution scenario across two product variants at the source code level and identifies the common and unique software identifier names across software variants source code. Juana's approach refers to common and unique identifier names as a software identifiers map and computes it by comparing software variants to each other. Juana considers all software identifier names such as package, class, attribute, and method. The novelty of this approach is that it exploits common and unique identifier names across the source code of software variants, to understand the evolution scenarios across software family in an efficient way. For validity, Juana was applied on ArgoUML and Mobile Media software variants. The results of this evaluation validate the relevance and the performance of the approach as all evolution scenarios were correctly detected via a software identifiers map., Comment: 9 pages, 7 figures, 6 tables

The purpose of the article is to study and consider general trends, problems and prospects for the use of ontological modelling of investment activity. Research methods are methods of semantic analysis of the basic concepts of the considered subject area (investment activity). The article discusses approaches to ontological modelling of investment activities and the development of modern intelligent expert systems that support investment processes. The novelty of the research is the analysis of the use of ontological modelling in the optimization of investment activities The conclusion of the research conducted in the article is as follows: the article considers various aspects related to intelligent technologies based on modeling, in particular on the ontological; the essence of ontological modelling of investment activity is considered. Use of cognitive-ontological model promotes the formation of optimum administrative decisions in the field of investment activity. The set of interrelated management actions for investment activities, their logical sequence constitute a holistic management strategy – a model of process management in the system of investment activities.

Data traffic demand over the Internet is increasing rapidly, and it is changing the pricing model between internet service providers (ISPs), content providers (CPs) and end users. One recent pricing proposal is sponsored data plan, i.e., when CP negotiates with the ISP on behalf of the users to remove the network subscription fees so as to attract more users and increase the number of advertisements. As such, a key challenge is how to provide proper sponsorship in the situation of complex interactions among the telecommunication actors, namely, the advertisers, the content provider, and users. To answer those questions, we explore the potential economic impacts of this new pricing model by modeling the interplay among the advertiser, users, and the CPs in a game theoretic framework. The CP may have either a subscription revenue model (charging end-users) or an advertisement revenue model (charging advertisers). In this work, we design and analyze the interaction among CPs having an advertisement revenue as a non-cooperative game, where each CP determines the proportion of data to sponsor and a level of credibility of content. In turn, the end-users demand for the content of a CP depends not only on their strategies but also upon those proposed by all of its competitors. Through rigorous mathematical analysis, we prove the existence and uniqueness of the Nash equilibrium. Based on the analysis of the game properties, we propose an iterative algorithm, which guarantees to converge to the Nash equilibrium point in a distributed manner. Numerical investigation shows the convergence of a proposed algorithm to the Nash equilibrium point and corroborates the fact that sponsoring content may improve the CPs outcome.

In a cognitive radio network (CRN), the primary users (PUs) do not operate their spectra, full time. Thus, they can sell them to the secondary users (SUs), for a second use, during the free time slots. In this article, we assume that the market is perfect, monopolized by a single PU, and all players are rational. After formulating the PU’s profit, we established a necessary and sufficient condition that guarantees the introduction of the PU into the market. In addition, the expressions of the SUs’ profits, showed us that in non-cooperative form, some ones got zero profit, even after maximizing their profits. Therefore, we have considered to study the effect of cooperation on the profits of this category of SUs. By following this step, we established a cooperation strategy, to avoid zero profits for all SUs. In order to analyze the impact of this cooperation on the PU, we have expressed the profits of the PU in the cooperative and non-cooperative forms; as result, we found that the cooperation between SUs brought better than the non-cooperative form.

Data or Information security in today's digital era is crucial in every organization that needs to pay attention. Management of organizational information is one of the components in realizing Good Corporate Governance. The measure of an adequate level of protection is an indicator of the cybersecurity awareness aspects of an organization's business processes in the short, medium, and long term, especially in the field that deals with information and communication technology (ICT). To make this happen, it requires a security standard that is appropriate and follows its needs to help organizations know the maturity level of cybersecurity in protecting its information security. The ABC organization is one of the Government agencies that manage the critical infrastructure and Indonesian digital economies. The organization has currently implemented several international security standards through its planning, implementation, evaluation document, and ICT activities. However, based on the national information security readiness assessment, information security management readiness results are still not optimal. In this study, an analysis of the NIST, ISO 27002, COBIT, and PCI DSS security standards has been carried out, which are ABC organizational security standards in managing ICT by assigned tasks and functions. Furthermore, the analysis result is used as materials for drafting a cybersecurity maturity framework through the four standard approaches that have become the basis for ICT management. The proposed concept of twenty-one integrated cybersecurity categories is expected to be a capital in measure ICT management performance in ABC organizations.

Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier.

Мета дослідження – розкрити особливості діяльності віртуального музею та його потенціал. Методи дослідження. Вибір методів дослідження зумовлений метою статті й предметом дослідження, зокрема застосовано системний підхід до опрацювання робіт з проблем цифровізації культури, музейної практики, також застосовано метод компаративного аналізу (щодо функціонування традиційного та віртуального музеїв у логіці схожість/відмінність). Наукова новизна. Порушено питання проблематики віртуального музею під впливом цифровізації. Висновки. Віртуальний музей, по-перше, феноменологічно є релевантним цифровій культурі; по-друге, він створює доступні для сприйняття будь-якої людини цифрові артефакти, копії яких розрізняються внутрішньовидовими особливостями. Як форма розвитку інтерактивності в роботі традиційних музеїв, віртуальний музей – важливе доповнення, засіб, що розширює практику творчої комунікації музейних працівників, а також сприяє їх залученню до підвищення власних цифрових компетентностей.

Many efforts had been done to make ICTs available in rural area to increase their incomes and productivities. For example, a community ecommerce is introduced to promote community handicraft and income generation; other projects like deploying VSAT to improve the internet connectivity at rural area. Although the project is developed, it is facing a challenge like how to sustain the ICT4D projects? This paper presents the study of financial sustainability of community network project through e3value modelling and simulation. Financial sustainability is one of the important factor for community projects. As a NGO or researcher that is trying to help the community, is it possible for us to access the feasibility of any projects for communities so that a realistic suggestion or proposal can be planned? To sustain the community commerce, it cannot be denied that a well planning and measurement on financial sustainability of the projects is required. Hence, we believe that e3value can be used as a tool to measure the financial sustainability of community network projects. With the use of 3value model, it can serve as a reference for various parties in resolving the financial sustainability issue.

With the rapid development of IoT technology in various fields such as smart cities and industry 4.0, the need for wireless sensor network-based systems has increased, one of which is the concept of using a vehicle as a mobile sensor network or known as VaaMSN. Many developers use the IoT platform as a cloud computing service in developing the VaaMSN system. However, not all IoT platform service providers provide monitoring features on every device and provide information such as device location, purpose, condition. Therefore, this research aims to develop an IoT Platform that can receive data and provide information on each device, making it easier to process data and control devices. Therefore, this research aims to develop an IoT platform called the SEMAR-IoT-Platform that able to received data and provide information on each device for easier data processing and control devices. The SEMAR-IoT-Platform integrates Big Data, Data Analytics, Machine Learning, using the principles of Extract, Transfer, and Load (ETL) for data processing and provides communication services using HTTP-POST, MQTT, and NATS. The test results show that the system has been successfully implemented to complement a simple IoT system with an average delay time of HTTP, NATS, and MQTT communications of less than 150ms for the data storage process, and for the data visualization process has an average delay time of less than 300ms.

The purpose of the article is to investigate and consider the general trends, problems and prospects of using cloud computing and technologies in data processing, in particular optimization of their storage. The research methodology consists of semantic analysis methods of the basic concepts of the considered subject area (cloud computing and cloud technologies). The article discusses approaches to storing data (including multimedia) in modern systems that support cloud computing and technologies. The scientific novelty of the research is the analysis of cloud computing use and cloud technologies for data storage optimizing. The conclusions of the research conducted in the article are as follows: the paper considered various aspects related to cloud computing and cloud technologies, including various advantages and disadvantages; cloud computing and cloud technologies can offer numerous benefits to various stakeholders; cloud computing and cloud technologies have some problems; it is noteworthy that by supporting the cloud computing and cloud technologies deployment and maintenance models, problems can be either minimized or eliminated since the dynamic characteristics of cloud computing and cloud technologies can always support users.

Метою статті є проведення дослідження історичного розвитку систем у галузі транспортних технологій, а саме систем на залізничному транспорті. Докладно представлені завдання, які виникали з розвитком залізничного транспорту, та описані способи їх розв’язання.Методами дослідження є огляд і проведення аналізу сучасних технологій та інформаційних систем, що призначені для автоматизації робіт у галузі залізничного транспорту, проведення аналізу переваг та недоліків наявних програмних продуктів.Новизною проведеного дослідження є аналіз переваг і недоліків систем, що використовують у галузі залізничного транспорту. У статті наведено докладний аналіз розвитку інформаційних систем у галузі залізничного транспорту, описано, які завдання вирішувалися в різні часи з різних напрямів, а саме з питань транспортування продукції від виробників до споживачів, удосконалення управління експлуатаційною діяльністю залізниць, управління технологічними процесами.Висновки. Викладений у статті матеріал дає змогу сформувати модель розвитку залізничної галузі за певний час. Розглянуто проблеми, що виникали у зв’язку з підвищенням потужності залізничної галузі й обробкою великих обсягів інформації, з питань оперативного управління перевезеннями, обслуговування, управління експлуатаційною діяльністю залізниць, планування ремонту вагонів та обліку всіх видів робіт, а також резервування місць для пасажирів. Докладно представлено розвиток відповідних інформаційних систем, які створювалися для автоматизації робіт і вирішення відповідних завдань., Целью статьи является проведение исследования исторического развития систем в области транспортных технологий, а именно систем на железнодорожном транспорте. Подробно представлены задачи, которые возникали с развитием железнодорожного транспорта, и описаны способы их решения.Методами исследования является обзор и проведение анализа современных технологий и информационных систем, предназначенных для автоматизации работ в области железнодорожного транспорта, проведение анализа преимуществ и недостатков существующих программных продуктов.Новизной проведенного исследования является анализ преимуществ и недостатков систем, используемых в области железнодорожного транспорта. В статье приводится подробный анализ развития информационных систем в области железнодорожного транспорта, описано, какие задачи решались в разное время по разным направлениям, а именно по вопросам транспортировки продукции от производителей к потребителям, совершенствования управления эксплуатационной деятельностью железных дорог, управления технологическими процессами.Выводы. Изложенный в статье материал позволяет сформировать модель развития железнодорожной отрасли за определенное время. Рассмотрены проблемы, возникающие в связи с увеличением мощности железнодорожной отрасли и обработкой больших объемов информации, по вопросам оперативного управления перевозками, обслуживания, управления эксплуатационной деятельностью железных дорог, планирования ремонта вагонов и учета всех видов работ, а также резервирования мест для пассажиров. Подробно представлено развитие соответствующих информационных систем, которые создавались для автоматизации работ и решения соответствующих задач., The purpose of the article is to conduct a study of the systems’ historical development in the field of transport technologies, namely, systems in railway transport. The problems that arose with the development of railway transport are presented in detail, and the ways of their solution are described.The research methods are review and analysis of modern technologies and information systems designed to automate work in the field of railway transport, analysis of the advantages and disadvantages of existing software products.The novelty of the study is the analysis of the advantages and disadvantages of systems used in the field of railway transport. The article provides a detailed development of information systems in the field of railway transport, describes what tasks were solved at different times in different areas, namely, on transportation of products from producers to consumers, improving the management of railways, process control.Conclusions. The material presented in the article allows forming a model of development of the railway industry for a certain time. The problems that have arisen in connection with increasing the capacity of the railway industry and processing large amounts of information, on the operational management of transportation, maintenance, management of railway operations, planning of car repairs and accounting of all types of work, as well as reservations for passengers. The development of the corresponding information systems which were created for automation of works and the decision of the corresponding problems is in detail presented.

Метою статті є дослідження, аналіз і розгляд загальних проблем і перспектив використання онтологій під час моделювання процесів навчання. Методами дослідження є методи семантичного аналізу основних понять цієї предметної сфери (навчальні процеси та процеси управління навчанням). У статті розглянуто наявні підходи до моделювання навчальних процесів. Новизною проведеного дослідження є розв’язання проблем інтелектуалізації процесів навчання на основі формальних онтологічних моделей. Висновки. У роботі проаналізовано наявні проблеми та перспективи застосування онтологій у моделюванні процесів навчання. Враховуючи результати проведеного аналізу, розроблено багаторівневу онтологічну модель, яка має важливе значення для розв’язання проблем підвищення ефективності процесів навчання (особливо дистанційного, online-навчання, e-learning) за допомогою використання комп’ютерних навчальних систем.

Hate speech is one of the most challenging problem internet is facing today. This systematic literature review examine hate speech detection problem and will be used to do an experimental approach on detecting hate speech and abusive language. This work also provide an overview of previous research, including methods, algorithms, and main features used. We use two research questions in this literature review which will be the foundation of the next experimental research. Correctly classifying a piece of text as an actual hate speech requires a lot of correctly labelled data. Most common challenges are different languages, out of vocabulary words, long range dependencies and many more.

Мета статті – проаналізувати наявні в Україні проєкти віртуалізації об’єктів історико- культурної спадщини. Методологія дослідження ґрунтується на застосуванні системного методу, який є основою для вивчення явищ, пов’язаних з віртуалістикою. Наукова новизна полягає в тому, що у статті вперше проаналізовано український досвід VR-реконструкції об’єктів історико-культурної спадщини. Висновки. Звернено увагу на відсутність в Україні переліку предметів культурної спадщини, що підлягають оцифровуванню, а також стандартів створення цифрового ресурсу з метою забезпечення доступності, активного й ефективного його використання, сумісності й обміну даними на локальному і міжнародному рівнях, збереження створенного ресурсу та його актуалізації. Наголошено на можливостях ІТ у сфері забезпечення збереження й управління історико-культурною спадщиною: від створення інформаційних систем, використання різноманітних систем безпеки в збереженні культурної спадщини, контролю та моніторингу стану пам’яток архітектури й інших культурних цінностей, створення і ведення реєстру культурних цінностей, web-технології до застосування технологій візуалізації і технологій віртуальної реальності, які використовують з метою відтворення втрачених чи пошкоджених об’єктів культурної спадщини тощо. Розглянуто сутність та особливості реалізації VR-проєктів, таких як «Кишенькова країна», «Трахтемирів-Digital», «Замки 360: Закарпаття», проєкт з тривимірної конструкції Середнянського замку, «Pinzel.AR», VR-розробки Pixelated Realities та AERO 3D, «Автентична Україна: нематеріальна культурна спадщина», «Тустань_віртуальна», «Цифрові музейні колекції Київської фортеці». Згадано про унікальну діджитал-платформу «Український національний інтелект», покликану надати доступ до оцифрованої культурної спадщини України. Зроблено висновок, що аналіз наявних проєктів з оцифровування об’єктів культурної спадщини наполегливо вимагає розробки стратегії побудови майбутньої цифрової колекції, яка має враховувати планування процесу оцифровування; узгодження юридичних питань; дотримання загальноприйнятих стандартів і принципів управління; підтримку доступу та збереження створеного цифрового ресурсу.

Future communication networks may encounter various issues in order to facilitate heavy heterogeneous data traffic and large number of users, therefore more advanced multiple access (MA) schemes is required to meet the changing requirements. Recently, a promising physical-layer MA technique has been suggested for multi-antenna broadcast channels, namely Rate Splitting Multiple Access (RSMA). This new scheme has the ability to partially decode the interference and partially treat the remaining interference as noise which makes it to cope with wide range of user deployments and network loads. On the other hand, interleave division multiple access (IDMA) has already been recognized as a potential code domain NOMA (non-orthogonal multiple access) scheme, suitable for 5G and beyond communication network. Hence, in this paper, a new approach of multiple access scheme is proposed to get the grip on new challenges in future communication (6G). The proposed framework consists the joint processing of RSMA and IDMA (code domain NOMA), in which the transmitter involves an IDMA as encoder and allows rate splitting to split the message in two parts i.e. common part and private part, before the actual transmission. The mathematical modeling of proposed system is elaborated in the paper and for simulation purpose the downlink communication scenario has been considered where users faced diverse channel conditions. The weighted sum rate (WSR) performance is evaluated for the proposed scheme which validate the quality of service (QoS) of the joint RS-IDMA system.

Метою статті є дослідження, аналіз і розгляд загальних проблем і перспектив використання наявних методів візуалізації статистичної та аналітичної інформації. Методами дослідження є методи семантичного аналізу основних понять цієї предметної сфери (візуалізація статистичних і наукових даних). У статті розглянуто підходи до візуалізації інформації. Новизною проведеного дослідження є аналіз математичного і статистичного апарату, який може застосовуватися для візуалізації статистичних і наукових даних, і опис деяких аспектів візуалізації. Висновки. У роботі досліджено наявні погляди на візуалізацію даних, яка є поданням деяких даних у графічній формі та може бути використана з метою маркетингової діяльності, а також види об’єктів інфографіки та критерії її створення. Враховуючи результати проведеного аналізу, автори прийняли рішення щодо розробки программного продукту візуалізації статистичних і наукових даних, визначили його математичне обґрунтування.

Numerous research and related applications of fuzzy clustering are still interesting and important. In this paper, Fuzzy C-Means (FCM) and Laying Chicken Algorithm (LCA) were modified to improve local optimum of Fuzzy Clustering presented by using UCI dataset. In this study, the proposed FCMLCA performance was also compared to baseline technique based on CSO methods. The simulation results indicate that the FCMLCA method have better performance than the compared methods.

In telecommunication channel modelling the wavelength is small compared to the physical features of interest, therefore deterministic ray tracing techniques provide solutions that are more efficient, faster and still within time constraints than current numerical full-wave techniques. Solving fundamental Maxwell's equations is at the core of computational electrodynamics and best suited for modelling electrical field interactions with physical objects where characteristic dimensions of a computing domain is on the order of a few wavelengths in size. However, extreme communication speeds, wireless access points closer to the user and smaller pico and femto cells will require increased accuracy in predicting and planning wireless signals, testing the accuracy limits of the ray tracing methods. The increased computing capabilities and the demand for better characterization of communication channels that span smaller geographical areas make numerical full-wave techniques attractive alternative even for larger problems. The paper surveys ways of overcoming excessive time requirements of numerical full-wave techniques while providing acceptable channel modelling accuracy for the smallest radio cells and possibly wider. We identify several research paths that could lead to improved channel modelling, including numerical algorithm adaptations for large-scale problems, alternative finite-difference approaches, such as meshless methods, and dedicated parallel hardware, possibly as a realization of a dataflow machine.

Sorong is a city located in the West Papua Province, Indonesia. It has a strategical location because it serves as the gateway of the West Papua Province. In 2019, its population has reached 247.084. Such condition has been deemed risky during the Covid-19 pandemic. The performance of the Sorong City Task Force for Covid-19 handling acceleration (Satgas Covid-19) had been considered ineffective by the citizens because it had not provided a system which would provide valid information regarding the Covid-19 progress in the city, resulting in worry and panic buying among the citizens. To overcome the problem, the knowledge possessed by the Sorong City Covid-19 Task Force should be developed into a Knowledge Management System (KMS). The implementation of the system had been expected to be able to optimize the documentation, dissemination and creation of knowledge as well as its evaluation. The KMS then was built to improve the performance effectiveness of the Sorong City Covid-19 Task Force in managing and disseminating knowledge regarding Covid-19 to the citizens. The methods utilized in this research were the qualitative method and the 10-step knowledge management roadmap. The research produced a conceptual model of KMS which could be utilized by the Sorong City Covid-19 Task Force.

Метою статті є пошук оптимальних методів побудови тренду з метою аналізу епідеміологічної ситуації в разі подальшого розвитку пандемії COVID-19. Докладно розглянуто завдання, які виникли зі зростом випадків інфікування восени 2020 року. Методи дослідження: узагальнення методу найменших квадратів Гауса та методологія Facebook Prophet. Новизною проведеного дослідження є порівняльний аналіз переваг і недоліків систем, що використовують для оперативної обробки статистичних даних, зокрема електронних таблиць MS Excel. Висновки. Викладений у статті матеріал дає наочні приклади виконання статистичних розрахунків з використанням широко розповсюджених програмних продуктів. Розглянуто проблеми, що виникають у зв’язку з правдивістю статистичної інформації. Представлено приклади реальних розрахунків трендів епідеміологічної ситуації.

Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue. Recent reviews state high accuracy to segment image, but depends on user input, say window area size, time steps, level set, magnification factor and so on. To extract the region of interest effectively, the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say, area opening, fill holes and selects most appropriate enhanced image required for further analysis. The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively. The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained. The proposed technique has the following features: (i) technique is context sensitive; (ii) no prior setting of time step, weighted area coefficient parameters is required; (iii) magnification independent; (iv) post-processing operations are self-driven which enhance segmentation results adaptively. The experimental results are compared with four state-of-the-art techniques: fuzzy C-means, spatial fuzzy C-means, spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet. Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.

Fuzzy set and neutrosophic set are two efficient tools to handle the uncertainties and vagueness of any real-world problems. Neutrosophic set is more useful than fuzzy set (intuitionistic fuzzy sets) to manage the uncertainties of a real-life problem. This study introduces some new concepts of single-valued neutrosophic graph (SVNG). The authors have discussed the definition of regular SVNG, complete SVNG and strong SVNG. The shortest path problem is a well-known combinatorial optimisation problem in the field of graph theory due to its various applications. Uncertainty is present in almost every application of shortest path problem which makes it very hard to decide the edge weight properly. The main objective behind the work in this study is to determine an algorithmic technique for shortest path problem which will be very easy and efficient for use in real-life scenarios. In this study, the authors consider neutrosophic number to describe the edge weights of a neutrosophic graph for neutrosophic shortest path problem. An algorithm is introduced to solve this problem. The uncertainties are incorporated in Bellman–Ford algorithm for shortest path problem using neutrosophic number as arc length. They use one numerical example to illustrate the effectiveness of the proposed algorithm.

Kopi merupakan salah satu minuman yang paling sering dikonsumsi di seluruh dunia. Kopi dapat dinikmati dengan banyak cara penyeduhan dan penambahan elemen lainnya, yang dapat menghasilkan cita rasa yang berbeda. Tumbuh pesatnya jumlah penikmat kopi baru tanpa disertai edukasi menyeluruh tentang varian rasa kopi membuat hanya sedikit penikmat kopi yang dapat mengenal dan mengapresiasi rasa kopi dengan cara yang berbeda. Media interaktif dirancang untuk mengenalkan banyak varian minuman kopi pada masyarakat. Untuk meningkatkan keterlibatan pengguna media interaktif, diterapkan teknologi Augmented Reality untuk memvisualisasikan pengenalan varian kopi. Hal ini dilakukan untuk memberikan gambaran jelas terhadap jenis varian kopi agar mempermudah pengguna memahami informasi diberikan serta memberikan pengalaman visual learning yang lebih dalam. Aplikasi media interaktif ini memanfaatkan smartphone berbasis Android yang dikembangkan menggunakan Unity 3D dan Vuforia Qualcomm Augmented Reality Software Development Kit, serta menggunakan Unified Modeling Language (UML) untuk membantu analisis pengembangan sistem. Pengujian dilakukan dengan metode black box testing dan kuisioner yang menyimpulkan bahwa aplikasi Coffee AR ini dapat membantu mengenalkan varian menu minuman kopi secara interaktif. Hal ini dibuktikan dari hasil 30 responden yang menggunakan aplikasi Coffee AR.