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Every industry has special attention to environmental preservation; however, it has become an unresolved problem in the industrial center of tofu village. Based on the analysis, there were problems related to liquid waste produced by tofu craftsmen that doesn’t meet the Quality Standards for liquid waste of the Food Products Industry, limited costs, land, and space thus that it didn’t meet the adequacy of space for Wastewater Treatment Plants (WWTP). The PKM (Community Service Program) activity aimed to implement Green Economy Concept on Integrated WWTP Systems and Sensors for the Kediri Tofu Industry as an effort to solve the waste problem in the tofu industry. This activity began with field observations, compatibility and requirements analysis, construction design and designing WWTP systems and sensors, small-scale trials, analysis, and evaluation. The WWTP system used involves components of pre-treatment, neutralization, coagulation and flotation, sedimentation, and filtration. The designed sensors included Arduino Nano-based pH meter and TDS meter sensors that were connected to computers and smartphones by Arduino IDE and Blynk software. It was hoped that this activity might spur business actors to actively participate in solving waste problems and reducing environmental impact pollution with all the space and cost limitations.

The business process of selling products from the Cimanggis Village community is still conventionally with a marketing strategy of offering words of mouth through business partners such as the community, retail, and markets, and the lack of access to information about product sales can hinder the product marketing process. This study aims to create a web-based agro-tourism area information system to help BUMDes and village UMKM overcome problems in ongoing business processes. Observation and interviews are methods used to collect data (needs analysis). Meanwhile, the MVC concept and the agile software development method were used to build an information system for agro-tourism areas in Cimanggis Village. This agro-tourism area information system is tested using black-box testing. The results of this study are data on UMKM activities and business processes in Cimanggis Village, design of information systems for agro-tourism areas, and evaluation of information systems. Data on business activities and processes are displayed in the current system analysis. The design of the information system is displayed in business processes, UML diagrams, and user interface views of the information system. The evaluation results show that the agro-tourism area information system can manage transaction data and information on UMKM products and tourism services with good results.

Pemanfaatan E-Commerce merupakan langkah strategis guna meningkatkan keuntungan maupun pemasaran terutama saat diterapkan di Usaha Kecil Mikro pada sebuah kafe. Tujuan dari penelitian ini adalah mencari sebuah solusi dari permasalahan yang dihadapi oleh Kafe XYZ terkait pemasaran yang masih belum menggunakan E-Commerce serta pelaporan keuangan yang masih belum menggunakan teknologi informasi sehingga terkadang terjadi kehilangan data yang menghambat proses bisnis pada kafe tersebut. Penelitian ini menganalisa strategi teknologi informasi, menganalisa strategi bisnis dan membantu implementasi E-Commerce dengan menggunakan metode SWOT (Strenghts, Weakness, Opportunities, Threats). Kemudian, digunakan metode Balanced Scorecard untuk menganalisa semua faktor internal dari semua perspektif dan menggunakan analisa Mc Farlan yang digunakan untuk mengkategorikan kebutuhan-kebutuhan teknologi informasi yang sesuai prioritas. Selanjutnya, metode yang digunakan untuk pengumpulan data yaitu wawancara dan observasi secara langsung ke kafe tersebut. Dari hasil penelitian, didapatkan sebuah usulan kebutuhan aplikasi dan teknologi yang dapat diterapkan pada Kafe XYZ yang berguna menunjang proses bisnis serta meningkatkan nilai lebih pada kafe tersebut.

Tasawuf merupakan salah satu disiplin ilmu klasik dalam kajian Islam. Artikel ini mengupas pemikiran salah satu tokoh besar tasawuf, Junaydal-Baghda>di>, tentang konsep tauhid. Permasalahan dalam penelitian ini adalah bagaimana konsep tauhid dalam pandangan Junayd al-Baghda>di> dan bagaimana implementasi konsep tauhid Junayd al-Baghda>di> pada era kontemporer. Metode dalam makalah ini adalah penelitian kepustakaan. Ini melibatkan mengidentifikasi dan menemukan sumber yang memberikan informasi faktual. Artikel ini menyimpulkan bahwa konsep tauhid Junayd al-Baghda>di> didasarkan pada kematian (kefanaan). Artinya lenyapnya sifat-sifat manusia, akhlak tercela, dan kebodohan seorang sufi selanjutnya sifat ketuhanan, akhlak mulia, dan ilmu yang abadi dalam dirinya. Dengan demikian, penerapan konsep tauhid-kematian bagi umat awam di era modern ini adalah memadukannya dengan amalan spiritual (riyadhoh) melalui tiga tahapan seperti takhalli, tahalli, dan tajalli. [Sufism is one of the classical disciplines in the Islamic studies. This article explores the thoughts of one of the great figure in Sufism, Junaydal-Baghda>di>, about the concept of monotheism. The problem in this research is how the concept of monotheism in Junayd al-Baghda>di>'s view and how the implementation of Junayd al-Baghda>di>'s concept of monotheism in the contemporary era. The method in this paper is library research. It involves identifying and locating sources that provide factual information. This article concludes that Junayd al-Baghda>di>'s concept of monotheism is based on mortality (kefanaan). It means the disappearance of human traits, despicable morals, and ignorance of a Sufi subsequently the eternal nature of divinity, noble character, and knowledge in him. Thus, the implementation of the concept of monotheism-mortality for ordinary people in this modern era is to combine it with spiritual practice (riyadhoh) through three stages such as takhalli, tahalli, and tajalli.]

Women play many roles in today's world in their societies' economic development. Women have many contributions to a country's welfare in various sectors, such as politics, health, agriculture, and public education. This research used a mixed-method design to analyze Islamic higher education, focusing on implementing gender policy and gender values—moreover, the activities of female lecturers in their daily work. The research also combines qualitative and quantitative data-gathering methods, particularly participant observation, in-depth interviews, numbers data, and figures to analyze how gender equality values for women should be implemented. This research reveals that IAIN Ponorogo is responsive gender. It can be seen from women's involvement in multiple fields like the academic senate member and the journal manager. The research also argues that women should be given equal opportunities as men, including paid work and the decision-making position sectors, to contribute to the institution.

Policy implementation is an equally important process of policy formulation in the context of achieving policy objectives. Although a policy has been well and neatly arranged, the purpose of the policy will never be achieved if the policy is not implemented properly. Policy research is a study of policies intended for the general interests of policies or implemented policies. There are several policy model implementation theories. Some of them are Edward's model, Grindle's model, Mazmanian and Sabatier's model. These policy implementation model has several advantages and disadvantages in its implementation. Using policy analysis triangle framework, these three implementation models can be made as an optimized policy implementation analysis method which has the advantages of the three models and meets the comprehensive and integrative aspects of an organization. The result presented in this study is a design of the policy implementation analysis method based on George Edward III, Marilee S Grindle, and Mazmanian & Sabatier policy implementation model.

One of the attempts to understand the meaning and content of the Quran, the central religious text of Islam, is the topic classification of Quranic verses. Verse topic classification aims to help the reader, so he can easily and quickly find information or knowledge contained in the Quran. In this paper, we build a classification model for the topics of English- translated Quranic verses using Support Vector Machine (SVM). The problem of classification of topics of Quranic verses is categorized as a multi-label classification problem. Hence, we design an SVM-based classifier to solve the multi-label classification of topics of Quranic verses. We also implement several techniques such as preprocessing, feature extraction, and dimensionality reduction to solve this problem. Then, we use Hamming Loss as a performance measure to evaluate our proposed classifier model. We find that our proposed model yields outstanding results.

Rekam jejak pemeliharaan suatu barang atau sarana operasional merupakan suatu hal yang sangat penting di dalam suatu instansi perusahaan. Oleh karena itu dibutuhkan adanya suatu sistem yang terkomputerisasi yang dapat membuat laporan rekam jejak pemeliharaan lebih efektif dan efisien dan diimbangi dengan sumber daya manusia yang memadai. Namun dalam pelaksanaannya, pada BLUD Puskesmas Kecamatan Cengkareng belum memiliki sistem yang dapat mencakup seluruh kegiatan pemeliharaan sarana operasional. Dikarenakan dalam proses pencatatan dan pengolahan data pemeliharaan sarana operasional masih dikerjakan secara semi komputerisasi yang menggunakan Ms. Excel dan catatan dalam sebuah buku sebagai media yang digunakan dan tentunya masih besar kemungkinan terjadinya human error. Sehingga diperlukan perancangan sistem informasi rekam jejak pemeliharaan sarana operasional yang efektif dan efisien. Penelitian ini menggunakan metode analisa PIECES (Performance, Information/Data, Economy, Control, Efficiency, Service), elisitasi kebutuhan sistem, serta pemodelan sistem dengan menggunakan UML (Unified Modelling Language) untuk menggambarkan secara visualisasi, yang selanjutnya diimplementasikan dengan bahasa pemrograman Hypertext Preprocessor (PHP) dengan basis data MySQL sebagai database yang digunakan. Dengan adanya perancangan sistem informasi rekam jejak pemeliharaan sarana operasional, dapat mempermudah bagian pemeliharaan dalam menghasilkan laporan rekam jejak pemeliharaan suatu barang atau sarana yang akurat dengan waktu yang cepat, sehingga menciptakan kinerja yang efektif dan efisien, serta dapat menunjang di dalam melakukan perencanaan kedepannya.

The Quran is a guideline for all Muslims. In the Quran, many things are talked about. In Quranic studies, the Quran is first classified into several topics according to the discussions of the Quranic verses. In this research, a classification model using a Back Propagation Neural Network was built based on the verses of Al-Quran and its multi-labelled topics. This allows the Back Propagation algorithm architecture to issue labels for each class in the form of ‘yes’ or ‘no’ for each output neuron. When using the Back Propagation algorithm, a sentence input that has become a vector is taken. In this way, TF-IDF will be used for feature extraction. Then, the model was evaluated via calculation of Hamming Loss. To ensure an optimal Back Propagation process, a comparison was made between the Stochastic Gradient Descent (SGD) and Adam optimizers. Based on some experiments, the proposed scheme yielded the best performance with a Hamming Loss value of 0.129.

In recent years, coal as well as coal bed methane becomes important energy resources. Therefore, the characterization of coal seam is also important in predicting the quality, porosity and pore’s fluid of CBM’s reservoir. Seismic wave is very important parameter to characterize reservoir’s properties of coal bed methane as well as quality of coal. In this paper, we show methodology to image the subsurface velocity using seismic tomography. It is very useful for characterizing the coal’s seam as well as to detect the position of intrusion body. A case study was carried out in Suban Block, Muara Enim Sumatera. This coal mining block contains igneous rock intrusion which becoming main control of coal’s quality. Coal which is close with intrusion body usually has better quality than far zone. To acquire the data, we used 48 channels of seismic recorder controlled by telemetry for controlling the shot and first break. Then, data are processed by Fresnel interpolated wave-path (FIW) wide-band inversion tomography. The results show that the intrusion body can be imaged clearly and the seam coal can be delineated from well information. The information in well controls are quite match with tomography results.

Indonesia had potential zakat as much as 217 Trillion Rupiah in 2017, but can only be collected 6 trillion or 0.2%, one of the factors is Muzakki’s (Zakat Giver) lack of trust in zakat management institutions so that they prefer to distribute their zakat without going through zakat management institutions directly. To encourage improving management of zakat management institutions requires planning, designing, and building systems that can assist in managing zakat. This study aims to design an architectural enterprise for zakat management institutions in achieving the collection, management, utilization, and distribution of zakat and alms. The methodology used in this study uses the TOGAF ADM approach by collecting data through observation and interviews with one of the private zakat management institutions. The results of this study are in the form of a system architecture design that can be used as a reference for zakat management institutions with a prototype of the zakat reporting system.

Independent learning about moslem worship activities can be done using technology assistance. The purpose of this research is to design android based application about procedures about sunnah prayer for worship activities doing by moslem. Application development uses multimedia development life cycle method and worship material proposed by Syafii priest. The result of this research is design and application about sunnah prayer, it shows information about sunnah prayer activity with multimedia features which help the user to master the material learned. We hope this application can be alternative learning tools for moslem to learn procedures of praying.

We conducted a semantic similarity study of semantic concepts in the context of the Holy Book Quran. Semantic similarity examines the degree of likeness and shared common properties of two concepts. For example, the Quranic concept of Allah and God will result in a high score of semantic similarity, whereas hell and paradise will yield in a low score because of its extremely different attributes and semantic features. Apart from that, we also delivered the Quranic concept semantic similarity standard dataset which consists of some pairs of Quranic concept along with its similarity score, which was manually annotated by human raters. This dataset resulted in the score of inter-annotator agreement 0.63, not far from the the ones yielded by some well-known datasets such as WordSim and Simlex. Furthermore, to measure the semantic similarity score, we chose the knowledge-based approach by utilizing lexical database properties such as the length and depth of a synonym set (synset). We then applied it to Yuhua Li equation, which has been considered to be the baseline among researchers within the problem of semantic similarity. In terms of the result, our system gained Pearson's correlation 0.33 and Spearman's 0.19. By considering inter-annotator agreement 0.63 that our Quranic standard dataset has as the upper bound score, there are still quite large room for improvement to better mimicking Muslim's intuition to measure the degree of similarity of concepts within the domain of Quran.

Quran is an eternal miracle for depicting its linguistic perfection, truth, and validating of the latest scientific research. Every Muslims must conceive and implement the commandments, also avoid the prohibitions mentioned in the Quran. Each verse of the Quran has a different meaning, and one verse in the Quran can depict one or more topics of class that can be studied. To ease learning and to understand the verses of Quran, each of them needs to be classified appropriately on its different topics. In this research, the model of classification was built that is able to identify the topics classes of each verse of Quran by multi-label classification approach. The model was built using Tree Augmented Naive Bayes (TAN). In order to improve performance, Mutual Information (MI) is employed to select dependent variables. The results show that the classification model built using TAN with MI obtained best performance with average Hamming Loss of 0.1121, while the model built using TAN without MI obtained average Hamming Loss of 0.1208.

News topic classification in this research is categorizing or distinguishing news, in textual data format, into a particular category based on information contained in the news. One of methods that can be used for this task is Bayesian Network that is one of uncertainty reasoning methods that uses probabilistic and directed acyclic graph to model conditional dependencies among variables. However, a textual data normally contains a considerable amount of variables and it could be problem for Bayesian Network since a large number of variables results high complexity, especially time complexity, in learning of Bayesian Network both structure and parameters. In addition, a considerable amount of variables could degrade accuracy since some variables might be irrelevant. In this research, we used Mutual Information as text feature selection method to provide relevant features for Bayesian Network classifier. Based on the conducted research, Mutual information as feature selector is able to improve classification performance of Bayesian Network. The highest classification rate obtained by employing Mutual Information is 75.34%, meanwhile the classification rate without Mutual Information is 45.95%, both in micro-average F1-score.

Al-Quran is the holy book as well as guidance for Muslims around the world. Each verse of Quran contains meaning and wisdom that can usually be classified into more than one topic of discussion. This research was conducted on the issue of classification of Quranic verses that can be classified into more than one topic as a multi-label classification problem. Multi-label classification is different from single-label classification, therefore this research provided a new model of classifier to handle multi-label classification. The system was developed using Multinomial Naive Bayes with several stages of preprocessing data such as case folding, tokenization, and stemming. The system also used bag of words as feature extraction method. The best Hamming loss obtained from this research is 0.1247.

Pendidikan ilmu agama, khususnya wudhu dan tayamum sangat penting untuk dipahami oleh anak maupun dewasa sebagai dasar hidup mereka untuk beribadah kepada Allah swt. Media buku sebagai pembelajaran dapat membuat pelajar bosan karena penyajiannya. Waktu belajar dikelas yang tidak lama serta pertemuan antara guru dan pelajar yang terbatas khususnya dalam mata pelajaran agama menjadi kendala dalam mempelajari wudhu dan tayamum. Oleh karena itu, selama ini proses pembelajaran masih menggunakan cara konvensional dimana pembelajaran masih menekankan pada bagaimana guru mengajar dari pada bagaimana siswa belajar. Cara ini masih mendapatkan beberapa kendala dalam pembelajaran. Sebagai solusi atas permasalahan tersebut, maka dibuat sebuah aplikasi penerapan media pembelajaran interaktif “belajar bersuci” untuk siswa Paud AlMubarok Jatiwaringin Kecamatan Mauk, sehingga diharapkan aplikasi ini membantu siswa dalam belajar mengenal bersuci dengan lebih mudah, menarik dan praktis

Information Communication Technology (ICT), especially Internet of Thing (IoT), has received many attentions from many researchers. In healthcare, IoT has proven in improving the quality of health services. Many IoT-based digital systems, such as WIFI digital thermometer, Telemetry Digital Pacemaker and Remote Digital Blood Pressure, have been developed recently. The systems allow physicians in hospitals to remotely monitor the condition of patients continuously. It enhances the quality of caring for the patients to be anytime and everywhere. However, most of the systems have been developed without considering energy issues. As a result, most of the systems may be suspected wasteful of energy consumption due to data transmission occurs frequently and continuously. In the other hand, research reports on the energy consumption of the systems have not been performed. It causes difficulty in obtaining data on the energy consumption of monitoring fever body temperature. To address these issues, this research develops an IoT-based digital thermometer that is called HI-Thermo. HI-Thermo is an adaptive system that is utilized fuzzy logic to optimize the use of energy on the proposed system. Fuzzy logic saves energy by controlling the transmission interval in Hi-Thermo. Several experiments have been conducted to evaluate the performance of HI-Thermo; and rigorous data results have been analyzed from the experiments. The results show that HI-Thermo saves energy of monitoring significantly. For fever body temperature monitoring, the proposed system consumes 15% lower than the existing traditional monitoring of body temperature, which does not implement the fuzzy logic.

Polycystic ovary syndrome (PCOS) is a hormonal endocrine disorder that infect many women in their reproductive cycle. It is a concern in a married couple because it is related fertility rate of women. One of the criteria for diagnosing PCOS are polycystic ovaries (PCO). Polycystic ovaries can be seen from the number and diameter of each follicle on ultrasound image. In previous studies, there are existing PCO classifications done automatically by the system using several methods. However, on those studies its feature extraction of the ultrasound image is still done manually. In this research, we propose a solution where the feature extraction is also done automatically using Convolutional Neural Network. CNN provide the best test performance with micro-average f1-score of 100% and an average of 76.36% on a 5-fold cross-validation.

Reciting Al-Qur'an sometimes becomes hard to do for Indonesian because Al-Qur'an was written in Arabic which is not the native language of Indonesian. The common mistake for Indonesian is pronouncing the Hijaiyah letters. In this paper, we propose to utilize the ability of Speech Recognition to help people learn reciting Al-Qur'an in the right way. This system is built using K-Nearest Neighbor (KNN) Algorithm as the classifier. For the extraction feature, we use Linear Predictive Coding (LPC) and Mel-Frequency Cepstrum Coefficients (MFCC) and compare both. We also compare the result for system with Principal Component Analysis (PCA) and without PCA. The best result when we use LPC is 78,92% and when we use MFCC is 59,87%.

[Id] Kota-kota besar pasti tidak lepas dengan penggunaan rambu lalu lintas untuk meningkatkan keselamatan pengguna jalan. Rambu lalu lintas dirancang untuk pembantu pengemudi untuk mencapai tujuan mereka dengan aman, dengan menyediakan informasi rambu yang berguna. Meskipun demikian, hal yang tidak diinginkan dapat terjadi ketika informasi yang tersimpan pada rambu lalu lintas tidak diterima dengan baik pada pengguna jalan. Hal ini dapat menjadi masalah baru dalam keamanan berkendara. Dalam meminimalisasi masalah tersebut, dapat dibuat suatu teknologi yang mengembangkan sistem yang mengidentifikasi objek rambu lalu lintas secara otomatis yang dapat menjadi salah satu alternatif meningkatkan keselamatan berkendara, yaitu Traffic Sign Detection and Recognition (Sistem Deteksi dan Rekognisi Rambu Lalu Lintas). Sistem ini menggunakan menggunakan deteksi ciri warna dan bentuk. metode Histogram of Oriented Gradient (HOG) untuk ektraksi ciri citra bentuk, colour moment untuk ekstraksi warna dan Support Vector Machines (SVM) untuk mengklasifikasikan citra rambu lalu lintas. Sehingga dapat dianalisa bagaimana Sistem dapat mendeteksi dan mengenali citra yang merupakan objek rambu lalu lintas? Diharapkan dengan adanya paduan metode-metode tersebut dapat membangun sistem deteksi dan rekognisi rambu lalu lintas, dan meningkat performansi sistem dalam mendeteksi dan mengenali rambu lalu lintas. Performansi yang dihasilkan dari sistem adalah 94.5946% menggunakan micro average f1-score. Kata kunci : ekstraksi ciri fitur, ekstraksi ciri warna, klasifikasi, HOG, colour moment, SVM, micro average f1-score. [En] The big cities must not be separated by the use of traffic signs to improve road safety. Traffic signs are designed to aide drivers to reach their destination safely, by providing useful information signs. Nonetheless, undesirable things can happen when information stored in the traffic signs are not received well on the road. It can be a new problem in road safety. In minimizing the problem, can be made of a technology that is developing a system that identifies an object traffic signs automatically which can be one alternative to improve driving safety, the Traffic Sign Detection and Recognition (Detection System and Traffic Sign Recognition). The system uses using the detection characteristics of colors and shapes. methods Histogram of Oriented Gradient (HOG) to extract image characteristic shape, color moment for the extraction of color and Support Vector Machines (SVM) to classify traffic signs image. So it can be analyzed how the system can detect and recognize the image which is the object of traffic signs? Expected by the blend of these methods can build a system of detection and recognition of traffic signs, and increased system performance to detect and recognize traffic signs. Performasi generated in the system is 94.5946% using micro average f1-score.

Hijaiyah letters are the letters that arrange the words in Al Qur’an consisting of 28 letters. They symbolize the consonant sounds. On the other hand, the vowel sounds are symbolized by harokat/marks. Speech recognition system is a system used to process the sound signal to be data so that it can be recognized by computer. To build the system, some stages are needed i.e characteristics/feature extraction and classification. In this research, LPC and MFCC extraction method, K-Means Quantization vector and Hidden Markov Model classification are used. The data used are the 28 letters and 6 harakat with the total class of 168. After several are testing done, it can be concluded that the system can recognize the pronunciation pattern of marked hijaiyah letter very well in the training data with its highest accuracy of 96.1% using the feature of LPC extraction and 94% using the MFCC. Meanwhile, when testing system is used, the accuracy decreases up to 41%.

Product reviews can provide great benefits for consumers and producers. Number of reviews could be ranging from hundreds to thousands and containing various opinions. These make the process of analyzing and extracting information on existing reviews become increasingly difficult. In this research, sentiment analysis was used to analyze and extract sentiment polarity on product reviews based on a specific aspect of the product. This research was conducted in three phases, such as data preprocessing which involves part-of-speech (POS) tagging, feature selection using Chi Square, and classification of sentiment polarity of aspects using Naive Bayes. Based on evaluation results, it is known that the system is able to perform aspect-based sentiment analysis with its highest F1-Measure of 78.12%.

News is a form of text data that must be categorized to facilitate retrieval of information for the reader. One problem that arises when categorizing news is the many topics that news can discuss, which is known as a multi-label condition. To solve this problem, a system that can perform multi-label classification using a Pseudo Nearest Neighbor Rule (PNNR) algorithm—a variant of the k-Nearest Neighbor (k-NNR) algorithm—was developed in this study. This system yielded a cross-validation error of 0,1495, measured using the hamming loss method via Cosine proximity. From the experiment, it can be concluded that the performance of the PNNR algorithm is influenced by the type of proximity used and the number of nearest neighbors.

Bayesian Networks merupakan salah satu metode pemodelan probabilitas pada Probabilistic Graphical Models . Bayesian Networks terdiri dari nodes yang merepresentasikan variabel pada masalah yang dikaji dan edges yang merepresentasikan relasi dependensi antar node . Pada masalah yang sederhana, struktur Bayesian Networks biasanya ditentukan oleh ahli di bidang masalah tersebut atau berasal dari intuisi alami manusia. Perancangan struktur Bayesian Networks secara manual ini akan sulit dilakukan apabila kasus yang dikaji merupakan kasus yang kompleks yang memiliki sangat banyak node dan sangat banyak kemungkinan edges yang menghubungkannya. Pada penilitian ini, dilakukan pengujian dan analisa terhadap proses pencarian struktur Bayesian Networks menggunakan algoritma Novel Modified Binary Differential Evolution . Novel Modified Binary Differential Evolution merupakan algoritma optimasi permasalahan diskrit dengan representasi solusi berbentuk biner yang merupakan pengembangan dari algoritma Differential Evolution . Hasil pengujian terhadap data Alarm, Asia, Carpo, Insurance, dan Water masing-masing diperoleh skor BDeu sebesar -1973.77, -243.68, -2450.54, -2024.17, dan -1621.90.

Proses pencarian dengan cara konvensional akan membuat pengguna I-GRACIAS bingung apabila keyword yang dimasukkan memiliki ejaan kata yang berbeda dengan nama aplikasi yang ada. Semantic similarity adalah suatu pen- dekatan untuk menangani pencarian dengan mengandalkan nilai keterhubungan antar-term yang dibentuk dari Word- net. Pendekatan semantic similarity yang digunakan adalah Path-based dengan Wu and Palmer (WUP) sebagai metode perhitungan semantic similarity. Omiotis merupakan metode yang ditujukan untuk mengukur derajat relevansi antar- dokumen. Terdapat dua komponen utama dari perhitungan Omiotis. Komponen tersebut adalah lexical relevance dan semantic similarity. Dengan demikian, proses pencarian yang awalnya menggunakan cara konvensional diubah den- gan pendekatan Semantic Textual Similarity (STS). Oleh karena itu, pada tugas akhir ini akan digunakan pengukuran Omiotis untuk menghitung kemiripan antar-dokumen dengan menggunakan pendekatan Path-based sebagai metode semantic similairty, yang mana masih memiliki ketergantungan dengan Wordnet. Sehingga mampu membantu menan- gani masalah pencarian aplikasi di I-GRACIAS. Kata Kunci: Semantic Similarity, Lexycal Relevance, Omiotis, PairingWord, Wordnet.

Dalam penelitian ini, dibangun sebuah sistem untuk melakukan tracking pemain sepakbola pada data video. Penelitian ini menggunakan ekstraksi ciri Histogram of Oriented Gradient (HOG) yang cocok untuk digunakan pada kondisi intensitas pencahayaan tidak stabil. Selain melakukan deteksi pemain bola, dalam penelitian ini dilakukan pengklasifikasian tim menggunakan clustering pada vektor ciri color moment. Untuk menjaga performansi deteksi, dilakukan evaluasi tracking menggunakan Kalman Filter. Berdasarkan hasil penelitian, sistem tracking yang dibangun memberikan performansi F1-score tertinggi mencapai 0.87 (skala 0-1) dengan berbagai kondisi pencahayaan video.

Microarray Technology is one of technology which able to read the structure of gen. The analysis is important for this technology. It is for deciding which attribute is more important than the others. Microarray technology is able to get cancer information to diagnose a person's gen. Preparation of microarray data is a huge problem and takes a long time. That is because microarray data contains high number of insignificant and irrelevant attributes. So, it needs a method to reduce the dimension of microarray data without eliminating important information in every attribute. This research uses Mutual Information to reduce dimension. System is built with Machine Learning approach specifically Bayes Theorem. This theorem uses a statistical and probability approach. By combining both methods, it will be powerful for Microarray Data Classification. The experiment results show that system is good to classify Microarray data with highest F1-score using Bayesian Network by 91.06%, and Naive Bayes by 88.85%.

Seismic tomography becomes important tool recently for imaging complex subsurface. It is well known that imaging complex rich fault zone is difficult. In this paper, The application of time domain inverse scattering wave tomography to image the complex fault zone would be shown on this paper, especially an efficient time domain inverse scattering tomography and their run in cluster parallel computer which has been developed. This algorithm is purely based on scattering theory through solving Lippmann Schwienger integral by using Born's approximation. In this paper, it is shown the robustness of this algorithm especially in avoiding the inversion trapped in local minimum to reach global minimum. A large data are solved by windowing and blocking technique of memory as well as computation. Parameter of windowing computation is based on shot gather's aperture. This windowing technique reduces memory as well as computation significantly. This parallel algorithm is done by means cluster system of 120 processors from 20 nodes of AMD Phenom II. Benchmarking of this algorithm is done by means Marmoussi model which can be representative of complex rich fault area. It is shown that the proposed method can image clearly the rich fault and complex zone in Marmoussi model even though the initial model is quite far from the true model. Therefore, this method can be as one of solution to image the very complex mode.

Migration is important issue for seismic imaging in complex structure. In this decade, depth imaging becomes important tools for producing accurate image in depth imaging instead of time domain imaging. The challenge of depth migration method, however, is in revealing the complex structure of subsurface. There are many methods of depth migration with their advantages and weaknesses. In this paper, we show our propose method of pre-stack depth migration based on time domain inverse scattering wave equation. Hopefully this method can be as solution for imaging complex structure in Indonesia, especially in rich thrusting fault zones. In this research, we develop a recent advance wave equation migration based on time domain inverse scattering wave which use more natural wave propagation using scattering wave. This wave equation pre-stack depth migration use time domain inverse scattering wave equation based on Helmholtz equation. To provide true amplitude recovery, an inverse of divergence procedure and recovering transmission loss are considered of pre-stack migration. Benchmarking the propose inverse scattering pre-stack depth migration with the other migration methods are also presented, i.e.: wave equation pre-stack depth migration, waveequation depth migration, and pre-stack time migration method. This inverse scattering pre-stack depth migration could image successfully the rich fault zone which consist extremely dip and resulting superior quality of seismic image. The image quality of inverse scattering migration is much better than the others migration methods.