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Karst landscape, characterised by drought-prone areas, limited water retention and nutrient-poor soils, pose significant challenges for the sustainability of small-scale agricultural systems. This study investigated the impact of Smart Precision Irrigation (SPI) technology, which integrates drip irrigation (DI) and organic fertilisation, on the growth, physiological performance and water use efficiency of chilli plants cultivated in a karst landscape in Gunungkidul District, Yogyakarta, Indonesia. This experiment involved a combination of drip irrigation (D), non-drip irrigation (ND), organic fertiliser (F), and no fertiliser (NF), with a semi-automatically installed SPI system to monitor and adjust soil moisture and watering requirements. The results showed that the combination of drip irrigation and organic fertiliser (D + F) significantly increased plant growth parameters, including plant height, leaf area, and chlorophyll content, and improved physiological traits such as photosynthetic rate, stomatal conductance, and leaf water use efficiency (LWUE). These improvements were attributed to the optimised water distribution and nutrient availability provided by the DI system, which minimised water loss and reduced drought stress, as evidenced by lower proline accumulation and reduced antioxidant enzyme activity in plants. In addition, the D+F treatment resulted in the highest biomass production, fruit yield and water use efficiency, underlining its potential as a sustainable agricultural practice in water-scarce karst environments. The study concludes that adopting organic matter irrigation and fertilisation strategies can improve the productivity and resilience of horticultural crops in areas facing similar environmental constraints.

Accurate rainfall information is crucial for various applications, including river flow estimation, water resource management, and flood warning system development. Traditional rain gauge networks, however, suffer from limited spatial coverage, leading to incomplete and biased data for large areas. This study proposes a novel approach for surface rainfall estimation using weather radar data and a MultiLayer Perceptron (MLP) Regressor machine learning model. Grid search was employed to explore model performance across different windowing configurations: no windowing, n-1 windowing, and n-2 windowing. The results demonstrate that n-1 windowing outperforms other configurations, achieving an average RMSE of 0.987, MAE of 0.263, and R-squared of 0.242 across five locations. This suggests that n-1 windowing effectively captures the temporal dynamics of rainfall patterns while improving the model's sensitivity to regularization. However, a tendency for underestimating high-intensity rainfall events remains. This research highlights the effectiveness of n-1 windowing with MLP Regressors for enhanced surface rainfall estimation using weather radar data. Further investigation is needed to address the underestimation bias, particularly for high rainfall events.

This study aims to enhance the performance of the K-Nearest Neighbors (KNN) algorithm by optimizing the hyperparameter K using the Particle Swarm Optimization (PSO) algorithm. In contrast to prior research, which typically focuses on a single dataset, this study seeks to demonstrate that PSO can effectively optimize KNN hyperparameters across diverse datasets. Three datasets from different domains are utilized: Iris, Wine, and Breast Cancer, each featuring distinct classification types and classes. Furthermore, this research endeavors to establish that PSO can operate optimally with both Manhattan and Euclidean distance metrics. Prior to optimization, experiments with default K values (3, 5, and 7) were conducted to observe KNN behavior on each dataset. Initial results reveal stable accuracy in the iris dataset, while the wine and breast cancer datasets exhibit a decrease in accuracy at K=3, attributed to attribute complexity. The hyperparameter K optimization process with PSO yields a significant increase in accuracy, particularly in the wine dataset, where accuracy improves by 6.28% with the Manhattan matrix. The enhanced accuracy in the optimized KNN algorithm demonstrates the effectiveness of PSO in overcoming KNN constraints. Although the accuracy increase for the iris dataset is not as pronounced, this research provides insight that optimizing the hyperparameter K can yield positive results, even for datasets with initially good performance. A recommendation for future research is to conduct similar experiments with different algorithms, such as Support Vector Machine or Random Forest, to further evaluate PSO's ability to optimize the iris, wine, and breast cancer datasets.

Limited customer information in sales data on e-commerce in Indonesia hinders companies in determining targeted marketing strategies, especially in targeting groups of potential customers to make repeat purchases. Sales data in the form of customers' names and cellphone numbers has been hidden by e-commerce, and only data is available in the form of products purchased, number of purchases, and customer addresses. So far, the methods used to determine potential customers mostly use more complete data features. Research that uses limited e-commerce data to determine potential customers is scarce. Several algorithms for predicting repeat purchases in e-commerce also have been widely used. However, the comparison of the performance of these methods in the context of e-commerce in Indonesia with limited data has yet to be discovered. In this research, the Decision Tree, Naive Bayes, and Random Forest methods were compared to classify potential customers using Maschere brand sales data from two e-commerce sites, namely Tokopedia and Shopee. The research results show that the Decision Tree algorithm achieved an accuracy of 90.91%, Naive Bayes achieved an accuracy of 37.50%, and Random Forest achieved the best level of accuracy, namely 93.94%. These results show that the Random Forest method is the best method for classifying customers' probability of repeat purchases. In the future, the results of this research can be developed again as a decision-making system to determine potential customers.

Machine learning’s prowess in extracting insights from data has significantly advanced fluid rheological behavior prediction. This machine-learning-based approach, adaptable and precise, is effective when the strategy is appropriately selected. However, a comprehensive review of machine learning applications for predicting fluid rheology across various fields is rare. This article aims to identify and overview effective machine learning strategies for analyzing and predicting fluid rheology. Covering flow curve identification, yield stress characterization, and viscosity prediction, it compares machine learning techniques in these areas. The study finds common objectives across fluid models: flow curve correlation, rheological behavior dependency on variables, soft sensor applications, and spatial–temporal analysis. It is noted that models for one type can often adapt to similar behaviors in other fluids, especially in the first two categories. Simpler algorithms, such as feedforward neural networks and support vector regression, are usually sufficient for cases with narrow range variability and small datasets. Advanced methods, like hybrid approaches combining metaheuristic optimization with machine learning, are suitable for complex scenarios with multiple variables and large datasets. The article also proposes a reproducibility checklist, ensuring consistent research outcomes. This review serves as a guide for future exploration in machine learning for fluid rheology prediction.

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.

Burnout atau kelelahan kerja merupakan sebuah fenomena yang sering dihadapi oleh profesional dalam berbagai bidang, termasuk programmer. Dampak negatif dari burnout mencakup penurunan kesejahteraan individu dan produktivitas kerja. Penelitian ini bertujuan untuk mengembangkan sebuah model prediktif untuk identifikasi dini dan intervensi burnout pada programmer menggunakan teknik pengenalan pola. Data yang digunakan dalam penelitian ini diperoleh dari kuesioner yang mencakup pertanyaan terkait pola kerja, kebiasaan individu, dan indikator burnout berdasarkan kriteria Maslach Burnout Inventory (MBI). Metodologi yang diterapkan melibatkan pengumpulan dan pra-pemrosesan data, ekstraksi fitur, dan aplikasi algoritma pengenalan pola untuk konstruksi model. Hasil penelitian menunjukkan bahwa model yang dikembangkan mampu mengidentifikasi risiko burnout dengan akurasi yang memadai, dan teknik pengenalan pola terbukti efektif dalam menggali pola dan insight yang relevan untuk identifikasi dan intervensi burnout pada programmer.

Every job in a coffee shop has a salary. Salary is a form of recognition or imbalance for the results achieved. Salary is often also called wages, which is an imbalance in the services provided regularly to employees. Fuzzy has several methods, one of which is Fuzzy Mamdani which is used to make inferences or take the best decision in a problem that has subtle values. In 1975, Ebrahim Mamdani proposed the Fuzzy Mamdani method. Fuzzy Mamdani is a method that uses linguistic rules and has a fuzzy algorithm so that it can be explained mathematically and is easy to understand. The input values for the criteria for length of work are 8, experience 8, and dependents 4. The output is based on Fuzzy Mamdani's calculations, the employee gets a salary of IDR 2.21 million. The results of this study research that the wage income offered by Warkop IN`DA to its employees is good and not far from the minimum wage of the coffee shop. This third variable has a big influence on the final calculation results. The system created is quite good because the MAE result is 0.567 and the MAPE result is 36.720%.

The existence of endemic animals typical of Central Kalimantan, such as orangutans and hornbills, is included in the protected species situation because the population numbers have increased slightly along with the clearing of land for the plantation sector, making orangutan and hornbill areas that should be beautiful and natural disappear. Because of this, a medium for conveying information and a unique introduction to the public is needed to know how important it is to know and know the various endangered species in Central Kalimantan to preserve animals. Therefore, this research aims to create an application for Central Kalimantan endemic animals using Mobile-based Augmented Reality to introduce Central Kalimantan's rare animals, starting from the area of residence, characteristics, leading food, and information about animal habitats. The application development process uses the Markerless Augmented Reality (AR) method, which displays 3D objects without using unique markers such as photos or images. The application development stage includes the planning, design, data collection, 3D object creation, application development and application testing using Blackbox testing with the Text Case method, which produces application testing descriptively explaining the application work process. The application for displaying 3D objects was tested in 5 trials, with an average of 5 seconds, and the marker appeared to display 3D objects. This application can have an impact on progress in the field of informatics as a medium for delivering information and learning media using Augmented Reality Markerless.

To reduce the discrepancy between the results of the expected data and the actual data, prediction is a procedure that is calculated systematically based on owned historical and present information. For the creation of solar energy projects and for decision-making in other connected domains, solar radiation intensity prediction is essential. This study aims to create a predictive model on monthly global horizontal irradiance data. The method used is the Simple Moving Average algorithm, Exponentially Weighted Moving Average and Single Exponential Smoothing. The stages carried out in this study include data collection, data preprocessing, testing of predictive models, interpretation of data visualization, and performance evaluation. The results of calculating the error value and correlation produce an evaluation of the performance of the prediction model. The SES method, which obtained an MAE value of 7.13, a MAPE of 0.02%, an MSE of 88.07, an RMSE of 9.38, and an R2 of 0.94, was determined to be the best prediction model by the calculation of the prediction model performance evaluation. A MAE value of 9.45, a MAPE of 0.02%, an MSE of 150.16, an RMSE of 12.25, and an R2 of 0.91 were obtained by the EWMA method, which is also the method that produced the second-best result. A MAE value of 14.38, a MAPE of 0.04%, an MSE of 367.59, an RMSE of 19.17, and an R2 of 0.77 were obtained by the SMA method, which is the third-best result.

Nilai Indeks Pembangunan Manusia (IPM) diperoleh dari beberapa aspek kehidupan yang realitanya tidak sepenuhnya terlaksana. Ketidakmerataan infrastruktur pembangunan merupakan salah satu permasalahan akibat tidak terlaksananya program peningkatan pembangunan manusia, khususnya di Kabupaten Wonosobo. Permasalahan tersebut akan tentu akan memberikan dampak kualitas hidup masyarakat. Pada penelitian ini, pembangunan sistem dengan algortima backpropagation dilakukan untuk memprediksi nilai IPM di Kabupaten Wonosobo guna mempermudah preiksi kualitas manusia di Kabupaten Wonosobo. Percobaan yang dilaakukan yaitu dengan melakukan pelatihan, validasi, dan pengujian data IPM diperoleh dari BPS Kabupaten Wonosobo dengan rasio 80:20. Berdasarkan hasil penelitian, arsitektur 5-12-1 menggunakan LR 0,0001 dan epoch sebanyak 10000 menghasilkan nilai MSE sebesar 2.812623341 fungsi aktivasi sigmoid, arsitektur 5-3-1 menggunakan LR 0,0001 dan epoch sebanyak 10000 menghasilkan nilai MSE sebesar 0.2121786277 pada fungsi aktivasi ReLU, arsitektur 5-6-1 menggunakan LR 0,01 dan epochs 10000 menghasilkan nilai MSE sebesar 2.050127723 fungsi aktivasi TanH, dan arsitektur 5-2-1 menggunakan LR 0,01 dan epoch sebanyak 1000 menghasilkan nilai MSE sebesar 3.040008631. Hasil penelitian menunjukkan bahwa algoritma backpropagation menggunakan fungsi aktivasi TanH menghasilkan akurasi terbaik untuk prediksi nilai IPM di Kabupaten Wonosobo. Hasil penelitian yang dihasilkan diharapkan dapat berguna untuk mengembangkan sistem prediksi yang lebih mutakhir kedepannya.

Diabetes is a chronic, non-communicable disease, and a long-term health condition that affects how the body uses glucose, the type of sugar that gives energy. In Indonesia, diabetes ranks as the sixth highest cause of death, following conditions related to childbirth. In 2021, Indonesia has a total of 19.5 million diabetes patients, making it the fifth-highest in the world. Some machine learning research has used data from the PIDD (PIMA Indian Diabetes Dataset) to predict diabetes. In this research, in addition to prediction accuracy, data complexity is also important. This research analyzes important features in the PIMA Indian database using the KNN (k-nearest neighbor) method for classification. The results show that using KNN with k=22 value results in the highest accuracy of 83.12%. The analysis also found that the important features required by the KNN method to achieve high accuracy from the PIMA Indian database, in order of importance, are glucose, age, insulin, blood pressure, Body Mass Index, pregnancy, skin thickness, and diabetes pedigree function. However, when used in the KNN classification method, the diabetes pedigree function feature was found to be unnecessary, not relevant, and can be reduced.

This paper examines the challenges of and facilitating strategies for linking disaster risk reduction (DRR) and climate change adaptation (CCA) in addressing urban floods, drawing from Indonesia’s experience. The fragmentation between efforts to implement DRR and CCA leading to unnecessary duplication could increase confusion at both the community and wider governance levels and reduce the effectiveness of urban flood management. Through the lens of collaborative governance, this paper analyzes the barriers for integrating DRR and CCA and options to better align their practices in the context of a megapolitan city, Jakarta, Indonesia. The key findings of this study confirmed that institutional fragmentation, in concert with inconsistent facilitation and collaboration mechanisms, were the strongest barriers to aligning DRR and CCA action. The absence of accountable leadership was a key impediment for successful partnership-building processes to support political and technical collaboration. Leadership in these contexts plays an important role in (1) developing sustainable relationships, (2) convincing potential stakeholders to collaborate, (3) persuading partners to commit to sharing resources, and (4) agreeing/sharing a common vision of the partnership actions needed to mitigate harm and reduce urban vulnerability. These factors are critically important for reducing the direct and indirect impacts of flooding in Jakarta. Such lessons from Indonesia on linking DRR and CCA offer valuable insights to inform the development of policies and strategies to deal with urban floods for global cities faced with similar challenges.

Penundaan pemilu Indonesia menjadi isu terhangat dan kontroversial baru-baru ini. Isu ini dilontarkan pertama kali oleh ketua umum PKB, Muhaimin Iskandar setelah menerima pelaku UMKM, pengusaha dan para analis ekonomi di ruang Delegasi DPR RI, Nusantara III, Jakarta pada Rabu, 23 Februari 2022 [14]. Isu penundaan pemilu ini menyebabkan sentimen publik terpecah menjadi tiga, yaitu sentimen negatif, netral, dan positif. Untuk mengetahui persentase sentimen terbesar, diperlukan suatu analisis sentimen terhadap data cuitan twitter pada periode waktu 1 Januari 2022 sampai dengan saat penelitian ini dilakukan (27 Mei 2022). Dengan menggunakan model Naive Bayes Classifier, kita mencoba memetakan analisis sentimen sebelum Muhaimin Iskandar mengutarakan isu (31.840 data cuitan) dan setelah Muhaimin Iskandar mengutarakan isu (151.878 data cuitan). Analisis sentimen di durasi waktu sebelum Muhaimin Iskandar mengutarakan isu diperlukan untuk mengetes, apakah klaim penundaan pemilu adalah keinginan rakyat adalah benar. Sedangkan analisis sentimen di durasi waktu setelah Muhaimin Iskandar mengutarakan isu diperlukan untuk melihat polarisasi di twitter, seberapa besar dukungan maupun penolakan terhadap isu tersebut. Hasil penelitian yang dilakukan menunjukkan masyarakat baru membahas isu penundaan pemilu setelah dibahas oleh Muhaimin. Sedangkan sentimen terhadap isu penundaan pemilu setelah dilontarkan oleh Muhaimin cenderung menunjukkan sentimen positif sebesar 60.581 cuitan unik, dengan 58.998 cuitan unik yang menunjukan sentimen negatif, sedangkan sisanya, yaitu sebesar 32.291 menunjukkan sentimen netral.

Child-centred disaster risk reduction and climate change adaptation have gained traction through projects and programs implemented by various actors worldwide. However, there remains a lack of understanding of their longer-term impact and influence on policy and practice at different levels of governance. This longitudinal research examines the processes of mainstreaming child-centred disaster risk reduction (DRR) and school safety programs at various levels. The data collection methods included participatory workshops, focus group discussions, and participant observations collected in 2008 and 2019. The findings suggest that the existence of local disaster regulation and mainstream institutions does not serve as a legitimate predictor for how likely governments adopt child-centred DRR and sustain school safety policy implementation. By adopting hybrid and combining approaches to DRR institutionalisation, NGOs and governments have collaboratively combined various strategies, including local regulatory change, incentives, nudging, and coercive and discursive approaches.

Here the authors use in situ mooring data in the Maluku Channel and the Lifamatola Passage of the Indonesian seas to show that a western boundary current transports Antarctic water from the South Pacific to the Indian Ocean.

Clinical use of cefadroxil, particularly in Bangka Tengah Hospital, is proven beneficial to overcome mild to moderate infections which especially occur in soft tissues such as skin, upper respiratory tract, pharyngitis, tonsillitis and urinary tract. For this reason, it is necessary to procure cefadroxil to be available enough for the treatment of cases of these diseases. The cefadroxil used by the Central Bangka Hospital was obtained from several pharmaceutical industries with different prices and distributions, due to the possibility that the active raw materials and ingredients had different origins, so there was concern that the microbiological quality would be different. Drug procurement is carried out using the e-catalog or non-e catalog method. This study aimed to examine the microbiological quality of six preparations (A, B, C, D, E, and F) in terms of their inhibitory activities against Gram-positive and Gram-negative bacteria. The bioassay was carried out by diffusion agar method using Escherichia coli ATCC 29522 and Staphylococcus aureus ATCC 29523 as the bacterial test, and nutrient agar as the test medium. The inhibitory activities were compared to cefadroxil standard for measuring the ratio potency. The results showed that all samples fulfilled USP 41 requirements with potential ratio of 90% to 120% and minimum inhibitory concentration of ≤ 8 ppm and ≤ 2 ppm against Escherichia coli and Staphylococcus aureus respectively. The potency ratios to cefadroxil standard were 95.9%, 99.1%, 100.0%, 96.7%, 96.2% and 98.2% against Staphylococcus aureus while the potency ratios of 95.6%, 99.3%, 103.8%, 97.1%, 95.7% and 100.4% were achieved against Escherichia coli for A, B, C, D, E, and F samples, respectively.

The communication method using sign language is very efficient considering that the speed of information delivery is closer to verbal communication (speaking) compared to writing or typing. Because of this, sign language is often used by people who are deaf, speech impaired, and normal people to communicate. To make sign language translation easier, a system is needed to translate symbols formed from hand movements (in the form of images) into text or sound. This study aims to compare performance such as accuracy and computation time of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) with Pyramidal Histogram of Gradient (PHOG) for feature extraction, to know which one is better at recognizing sign language. Yield, both combined methods PHOG-SVM and PHOG-KNN can recognize images from hand movements that form certain symbols. The system built using the SVM classification produces the highest accuracy of 82% at PHOG level 3, while the system built with the KNN classification produces the highest accuracy of 78% at PHOG level 2. The total computation time of the fastest training and testing by the SVM model is 236.53 seconds at PHOG level 3, while the KNN model is 78.27 seconds at PHOG level 3. In terms of accuracy, PHOG-SVM is better, but in terms of computation time, PHOG-KNN takes the place.

Penjadwalan adalah hal yang umum dilakukan terutama di instansi pendidikan seperti perguruan tinggi. Salah satu kegiatan yang memerlukan proses penjadwalan di perguruan tinggi adalah penjadwalan ujian pendadaran proyek tugas akhir. Faktor-faktor seperti jumlah mahasiswa, ketersediaan dosen, dan ketersediaan ruangan juga membuat proses penjadwalan menjadi lebih kompleks dan memakan waktu jika dilakukan secara manual. Metode Genetic Algorithm (GA) dapat dimanfaatkan untuk mengatasi masalah di dalam proses penjadwalan secara manual. Penelitian ini bertujuan untuk menghasilkan sebuah aplikasi yang diberi nama Automatically Generated Scheduling (ALGEN Scheduling) yang mampu melakukan proses penjadwalan ujian pendadaran dengan lebih mudah. “ALGEN Scheduling” dibangun menggunakan bahasa C# dan menerapkan GA yang telah dimodifikasi dengan pendekatan repetitive random sebagai inti untuk melakukan proses penjadwalan. Pembuatan ALGEN Scheduling dimulai dari perancangan kromosom, implementasi GA termodifikasi dalam bahasa C#, pembuatan GUI dengan tools visual studio, dan integrasi inti penjadwalan dengan GUI. Aplikasi yang dibangun bisa membantu mahasiswa dan dosen penguji dalam menginfokan jadwal ujian pendadaran. Untuk mengetahui kinerja Aplikasi “ALGEN Scheduling”, peneliti melakukan evaluasi dengan menyebar kuesioner kepada responden yang terdiri dari mahasiswa dan dosen. Peneliti berhasil mendapatkan tanggapan sebanyak 101 responden. Berdasarkan pengolahan data responden didapatkan 68% menilai bahwa Aplikasi “ALGEN Scheduling” memiliki tampilan yang bagus dan sangat mudah digunakan untuk menghasilkan jadwal ujian pendadaran dengan tanpa adanya bentrokan jadwal. Sedangkan 19% responden mengatakan bahwa Aplikasi “ALGEN Scheduling” tidak terlalu mudah digunakan tetapi juga tidak terlalu sulit untuk dipelajari. Sisanya, responden menganggap perlu adanya pelatihan khusus sebelum aplikasi benar-benar diterapkan

Purpose survey research is to answer for dilemma of public service facilities to assess how much benefits of revitalizing the pedestrian bridge without a roof in Sudirman Jakarta, given the problem for the pedestrian bridge cannot protect its users from the heat and rain. In addition, this building structure can be adjusted to the aesthetic advantages layout of the modern city. The quantitative survey method uses the Analytical Hierarchy Process model approach, which is a decision-making method by comparing the selection criteria in pairs and the pairwise comparison between the available options. The results showed that respondents expected the pedestrian bridge to return to its function of safety and comfort to be the top priority. In addition, there are also advantages in improving the aesthetics of urban planning which is the attraction of public facilities. Thus the decision as a reference that revitalization pedestrian bridge must prioritize its function to cross, not enjoy the city view, besides that, the pedestrian bridge without a roof needs to adjust to the season and weather in Jakarta which has an impact on public concerns about the dangers of bridge conditions for its users

Pesatnya perkembangan di dunia teknologi informasi memberikan dampak terhadap cara berbisnis. Saat ini, banyak orang lebih menyukai berbelanja secara online dibandingkan harus datang secara fisik ke toko. Aplikasi marketplace berbasis android menjadi pasar digital dengan jumlah transaksi yang sangat besar setiap harinya. Namun kebanyakan aplikasi marketplace yang ada saat ini masih terbatas pada produk berupa barang. Padahal jika dilihat lebih dalam lagi, kebutuhan akan produk jasa seperti jasa reparasi barang elektronik juga memiliki porsi yang cukup tinggi. Beberapa penyedia jasa telah membuat aplikasi sendiri yang membuat para calon pengguna jasa harus memasang banyak aplikasi jika ingin menggunakan jasa dari penyedia jasa yang beragam. Oleh sebab itu pada penelitian kali ini akan dibahas pembuatan marketplace di bidang jasa berbasis android dengan nama ServiceIn. Aplikasi marketplace yang dibuat berfokus pada jasa reparasi elektronik sebagai salah satu jasa yang paling banyak dicari semua orang. Evaluasi menggunakan kuesioner juga dilakukan pada peneltian ini setelah tahapan pembuatan aplikasi selesai. Evaluasi bertujuan untuk melihat tanggapan calon pengguna aplikasi terhadap kemudahan yang ditawarkan aplikasi. Berdasarkan hasil evaluasi, 56% jawaban “sangat setujuâ€, dan 38% jawaban “setuju†membuktikan bahwa aplikasi yang dibuat mampu membantu seseorang untuk memenuhi kebutuhan akan jasa reparasi barang elektronik dengan cara yang lebih mudah.

The Government of Special Capital Region of Jakarta until the end of 2019 seeks to revitalize tourism program by launching double-decker bus. But this implementation, there are still problems with a low level of passenger services, shelters without seats, small bus AC rooms, and responsiveness of PLB officers is still low. Purpose of this study is to explore values bus transportation services that are predicted for quality and service satisfaction. The approach used is survey methodology with results show that bus transportation service predicted values for service quality is transparency aspect; accountability; and conditional; all service products used by passengers. While, participatory aspect; equal rights; and the balance of rights and obligations, predicted to impact in service satisfaction received—recommendations for improvements in service quality, especially provision facilities and ease accessibility. While improvement service satisfaction is improved, safety on buses and empathy PLB officers in responding services.

Mapping mangroves using satellite imagery has been done for decades. It helps reduce obstacles in inaccessible places caused by the mangroves’ intricate root system, thick mud, and loss of position signals. There is an urgent need to produce a mangrove map that automatically and accurately covers the mangroves with the density index of the canopy as visually represented in satellite imagery. The research was conducted through an analytical desk study of the mangrove features from space. The study aims to develop a simple formula for automatically tracing, capturing, and mapping mangroves and determining the canopy density index from open access of satellite data to eliminate manual digitization work, make it easy to use, and save cost and time. The goal is to monitor, assess, and manage the condition of mangroves for anyone interested in mangroves, including the central government, local authorities, and local communities. As a result, the authors proposed an algorithm: (ρNIR − ρRed)/(ρRed + ρSWIR1) ∗ (ρNIR − ρSWIR1)/(ρSWIR1 − 0.65 ∗ ρRed). Experimental results in many mangrove forests using Landsat 5 TM, Landsat 7 ETM, Landsat 8 OLI, and Sentinel 2 imageries show satisfactory performance. The maps capture the spatial extent of the mangroves automatically and match the satellite imagery visually. The index correlates significantly with the Normalized Difference Water Index (NDWI), with R2 reaching 0.99. The research will apply the formula of the Musi Delta mangrove complex in South Sumatra, Indonesia. The advantage of the algorithm is that it works well, is easy to use, produces mangrove maps faster, informs the index, and efficiently monitors the change in mangrove conditions from time to time.

Vehicle make and model classification is crucial to the operation of an intelligent transportation system (ITS). Fine-grained vehicle information such as make and model can help officers uncover cases of traffic violations when license plate information cannot be obtained. Various techniques have been developed to perform vehicle make and model classification. However, it is very hard to identify the make and model of vehicles with highly similar visual appearances. The classifier contains a lot of potential for mistakes because the vehicles look very similar but have different models and manufacturers. To solve this problem, a fine-grained classifier based on convolutional neural networks with a multi-task learning approach is proposed in this paper. The proposed method takes a vehicle image as input and extracts features using the VGG-16 architecture. The extracted features will then be sent to two different branches, with one branch being used to classify the vehicle model and the other to classify the vehicle make. The performance of the proposed method was evaluated using the InaV-Dash dataset, which contains an Indonesian vehicle model with a highly similar visual appearance. The experimental results show that the proposed method achieves 98.73% accuracy for vehicle make and 97.69% accuracy for vehicle model. Our study also demonstrates that the proposed method is able to improve the performance of the baseline method on highly similar vehicle classification problems.

As the usage of the internet grows, more and more information is obtained, thus presenting challenges, especially for users and website owners. Website users often have difficulty finding products or services that are relevant to their needs caused by abundant amounts of products and services delivered on a website. Website owners often find it difficult to convey information about the right products and services to certain target users. Based on the problem given above, we can conclude that a recommendation system approach that can improve personalization on their website is needed. The recommendation system approach must be able to provide navigation on the website to make it more adaptive towards the interests and information needed by the user. This study uses Association Rules formed from Microsoft web access log data by finding visitor patterns based on frequently visited web site pages. From the results of the research conducted, the performance of the method used has a precision value of 0.896, 0.058 recall, and F-measure 0.104. Whereas the measurement of the accuracy value resulted in a performance recommendation of exactly 3%, an acceptable rate of 87%, and 10% incorrect. This research shows that the Association Rules method can increase the effectiveness of website personalization to provide relevant information recommendations for visitors. For further research, it can concentrate on improving existing methods thus website personalization becomes more adaptive.

The COVID-19 situation and school closure has brought intense impact to millions of students and teachers. However, there is a growing pressure from parents, teachers, and children for schools to reopen and the national government has developed guidelines if schools going to reopen. This study is conducted to assess the perspective of teachers and other education personnel regarding the current situation and the outlook when schools reopen in the future. A combination of survey, focus group discussions, and interviews were conducted among school personnel (i.e. teachers, school administrator, and school principals), local education office officials, and representatives from teacher's professional associations in Indonesia. A total of 27,046 school personnel participated in the survey, making it one of the largest surveys ever conducted with school personnel in Indonesia. In addition, 53 participants were involved in the FGDs and interviews in 5 areas. Findings suggest that 76% teachers were concerned if schools reopen due to the health risks and 95% teachers preferred having a blended learning or continue using full distance learning. Nevertheless, if schools reopen, teachers expressed the needs for greater health protection among teachers and children, strengthened coordination and collaboration with local stakeholders, and further capacity strengthening to ensure that the learning process can be safe, comfortable, and effective. Specific analysis on the perspective and needs for teachers working with special needs learners and disadvantaged areas are further analysed.

Bahasa Jawa merupakan salah satu dari sekian banyak bahasa daerah di Indonesia yang memiliki sistem keaksaraan khusus, yaitu aksara Jawa. Meski aksara Jawa tersebar di seluruh provinsi di Pulau Jawa, namun belum banyak kalangan masyarakat di Pulau Jawa yang paham dengan aksara Jawa, termasuk para siswa yang mendapatkan mata pelajaran aksara Jawa sekalipun. Aksara Jawa dinilai sebagai pelajaran yang sulit bagi siswa. Penelitian ini membahas tentang pembelajaran aksara Jawa untuk siswa Sekolah Dasar menggunakan media board game. Tujuan penelitian ini adalah merancang board game “Tepok Aksara” sebagai media belajar dan penumbuhan minat terhadap aksara Jawa. Metode deskriptif, pendekatan kombinasi, dan strategi linear digunakan dalam penelitian ini. Didapatkan hasil bahwa siswa menjadi lebih menguasai dan berminat terhadap aksara Jawa setelah memainkan board game “Tepok Aksara”.

The focus of this work is to investigate the effect of emulsifier-free emulsion fuel via steam emulsification (SD) to the diesel engine through physical properties, combustion performance, and exhaust analysis, and compare with conventional emulsion fuel with water percentages of 5% and 10% (E5 and E10) and biodiesel blend (B5). The SD was prepared using a custom 200 mL glass mixing column. The B5 fuel quantitatively was filled in the column, and then the steam was injected from the bottom of the mixing column through the porous frit glass with the pores ranging from 40 to 100 µm. The average water droplet size of SD is 0.375 µm with the average water percentage of 6.18%. The brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE) of SD improved 4.19% and 3.92%, respectively, as compared to B5. The in-cylinder pressure (ICP) was lower than B5, however, yielding close to the B5 at 4 kW engine load. As for the exhaust emission test, NOx and PM for SD were reduced significantly with a percentage reduction of 25.22% and 10.68%, respectively, as compared to neat B5. The steam emulsification method offers a huge potential to be explored further as the concept offers the alternative method of making emulsion fuel without the use of conventional mechanical mixers.

The objective of the paper was to understand the impact of sub regional economic cooperation, known as the Indonesia-Malaysia-Thailand Growth Triangle (IMT-GT), on trade sector in Indonesia. The approach of research based on export macro information by provinces and commodities. The method used in the analytical framework was a fixed effect method. The regional study covered Nanggroe Aceh Darussalam, North Sumatera, West Sumatera, and Riau provinces, and the commodities involved CPO, coffee and rubber, with 1990-2008 data series. Based on pooled regression, there was a significant impact on export from the four provinces to Malaysia and Thailand for all based years. One might focus on commodity level that, in fact, CPO was the only one commodity that had a significant impact within the IMTGT region. In addition, Thai Bath and Malaysian Ringgit, with respect to GDP for both countries, had significant influenced on export, especially after the IMT-GT endorsed.

Peningkatan jumlah kendaraan bermotor yang terus terjadi di Indonesia tiap tahunnya, membuat kebutuhan akan sistem yang mampu mengidentifikasi kendaraan secara otomatis atau sering disebut Sistem Lalu Lintas Cerdas juga ikut meningkat.Sistem ini dapat digunakan antara lain untuk menemukan kendaraan yang dicuri, pembayaran tiket parkir otomatis, dan menindak para pelanggar lampu merah. Kemampuan utama dari sistem tersebut adalah pengenalan plat nomor.Pada penelitian kali ini akan digunakan metode Momentum Backpropagation Neural Network untuk mengenali karakter dari suatu citra plat nomor kendaraan di Indonesia. Namun sebelumnya, citra plat nomor akan diubah menjadi citra biner. Citra biner kemudian disegmentasi untuk mengisolasi karakter-karakter yang akan dikenali. Terakhir dimensi citra hasil segmentasi akan direduksi menggunakan Haar Wavelet.Uji coba pada penelitian kali ini melibatkan 276 karakter yang terdiri dari huruf dan angka pada plat nomor kendaraan di Indonesia. Hasil uji coba menunjukkan 268 karakter diantaranya mampu dikenali dengan benar. Dengan kata lain metode yang digunakan memiliki tingkat akurasi hingga 97,10%.

The objective of this research was to acquired the best percentage of using cheese in the chicken nuggets processing. Materials used for this research were nuggets made from chicken meat, Gouda cheese and spices. Experiment done with Random Block Analysis as experiment design by addition Gouda Cheese ( 0% (F0), 5% (F1), 10% (F2), 15% (F3), 20% (F4) ). The results showed that chicken nuggets with Gouda cheese addition gave a highly significant effect (P0.05) on pH and Organoleptic. The best result was nuggets which made with addition of 15 percent of Gouda cheese.The conclusion of this research was the addition of Gouda Cheese to Chiken Nuggets increased fat content, protein content, ash content, WHC and tend to decreased mouistured content, textured. Result of panelist not trained using cheese in manner chicken nuggets are like made with addition of 15 percent of Gouda cheese. The best result was nuggets which made with addition of 15 percent of Gouda chees Keywords : Chicken nuggets, Chicken meat, Gouda cheese

Rayleigh wave or ground roll is a noise in seismic body waves. However, how to use this noise for soil characterization is very interesting since Rayleigh wave phase velocity is a function of compression-wave velocity, shear-wave velocity, density and layer thickness. In layered-medium Rayleigh wave velocity also depends on wavelength or frequency, and this phenomenon is called dispersion. Inversion procedure to get shear-wave velocity profile needs a priori information about the solution of the problem to limit the unknown parameters. The Lagrange multiplier method was used to solve the constrained optimization problems or well known as a smoothing parameter in inversion problems. The advantage of our inversion procedure is that it can guarantee the convergence of solution even though the field data is incomplete, insufficient, and inconsistent. The addition of smoothing parameter can reduce the time to converge. Beside numerical stability, the statistical stability is also involved in inversion procedure. In field experiment we extracted ground roll data from seismic refraction record. The dispersion curves had been constructed by applying f-k analysis and f-k dip filtering. The dispersion curves show the dependence of Rayleigh wave phase velocities in layered media to frequency. The synthetic models also demonstrate the stability and the speed of inversion procedure.

Water-in-diesel emulsion fuel (W/D) is considered to be a potential alternative fuel that can reduces nitrogen oxides (NOx) and particulate matter (PM). W/D is normally produced with addition of surfactant to prolong its stability. However, the dependency on surfactant leads to higher production cost. A concept that can eliminate dependency on the surfactant was introduced by strong mixing concept and direct supply the non-surfactant W/D to a diesel engine. Therefore, the objective of this study is to investigate the stability behavior of non-surfactant W/D using microscopic observation. Water and diesel fuel were mixed using a combination of high-shear mixer and an ultrasonic transducer. The amount of water injected into the system are 5% and 10% by volume, which were labelled as E5 and E10 respectively. The produced non-surfactant W/D was then placed into a petri dish for microscopic observation. The results showed that the average diameter of water droplets observed in E5 and E10 at sedimentation stage were 15.38 and 22.41 μm respectively. The stability period of E5 and E10 before it sediment were 25 and 67 seconds correspondingly. Overall, it is observed that microscopic observation is a reliable approach to determine the stability behavior of non-surfactant W/D.