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All Journal JSAI (Journal Scientific and Applied Informatics) Jurnal Pengabdian Masyarakat Sains dan Teknologi Jurnal Komtika (Komputasi dan Informatika)
Cahyono Budy Santoso
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education Engineering Social Sciences Other

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Implementasi Data Warehouse dan Bussiness Intelligence Kasus AIDS di Jawa Barat Cahyono Budy Santoso; Muhammad Mujiburochman; Reyner Shaquille Rachim; Raihan Cikal Herlambang
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7567

Abstract

This study discusses the design of a data warehouse for analyzing AIDS cases in West Java using the Nine Step Methodology. The background of this research is the high prevalence of AIDS cases in West Java during 2018–2019 and the need for an integrated data management system to support data-driven health policies. The objective of this study is to design and implement a data warehouse capable of integrating data from various dimensions, such as region, age group, gender, and year, to support epidemiological analysis of AIDS. The methodology employed includes stages such as data extraction from various sources, data transformation to enhance quality, and data loading into a PostgreSQL-based data warehouse system. The study also utilizes the ETL (Extract, Transform, Load) process to ensure the integrity of the processed data. The results indicate that the designed data warehouse successfully maps the distribution of AIDS cases based on relevant dimensions. Key findings reveal that the productive age group (25–49 years) and males have the highest number of cases, with Bandung City being the region with the most cases. The contribution of this study is the provision of a data platform that supports evidence-based decision-making while identifying high-risk regions and groups for more effective health interventions. Limitations include the scope of data limited to two years and the absence of predictive analytics features. Future research is recommended to expand the time coverage and integrate predictive analysis to enhance the effectiveness of health policy
Perancangan Web E-Commerce Pada Toko XYZ Dengan Fitur Sistem Rekomendasi Hafid Roihan; Cahyono Budy Santoso
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8268

Abstract

This research aims to design and implement a web-based e-commerce system for XYZ Store by providing a fresh vegetable product recommendation feature, using the Apriori algorithm and the Rapid Application Development (RAD) approach. The design process begins with collecting data through interviews to find out the needs of the system. Transaction data for one month was analyzed using the Apriori algorithm to identify patterns of association between products that are often purchased together. The results of this analysis were used as the basis for creating recommendation features. The RAD approach was used because it allows the system to be developed quickly and gradually by involving users in each phase of development. This research analyzes the usability level of the platform using the System Usability Scale (SUS) method. Testing was conducted on 15 participants who had tried the platform prototype in the designed usage scenario. The measurement results indicate that the average SUS score is 78.2, placing it in the “Good” category. The recommendation system feature received a positive response, with the majority of users finding it helpful in finding relevant products. These findings suggest that integrating the recommendation system not only enriches the platform's features but also contributes to improving the overall user experience.
Educational Program on Converting Plastic Waste into Alternative Fuel Using Pyrolysis and Triboelectric Nanogenerators (TENG) for Community Leaders in Kasomalang Kulon Tourism Village, Subang Regency Hayadi Hamuda; Lukman Medriavin Silalahi; Safrizal Safrizal; Cahyono Budy Santoso; Yunus Widjaja; Chaerul Anwar; Teddy Mohamad Darajat; Listiana Satiawati; Sumihar M.L. Tobing; Aditiameri Aditiameri
Jurnal Pengabdian Masyarakat Sains dan Teknologi Vol. 5 No. 2 (2026): Juni: Jurnal Pengabdian Masyarakat Sains dan Teknologi
Publisher : Fakultas Teknik Universitas Cenderawasih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58169/jpmsaintek.v5i2.1066

Abstract

This Community Service (CS) initiative was executed collaboratively by faculty members from five universities—Universitas Pamulang, Universitas Presiden, Universitas Trisakti, Universitas Pembangunan Jaya, and Universitas Borobudur—in Kasomalang Kulon Tourism Village, Subang Regency, West Java, on April 17, 2026. This initiative aimed to instruct village leaders, as prospective catalysts for change in waste management and renewable energy, on the technique for converting plastic trash into pyrolysis-derived alternative fuel and the Triboelectric Nanogenerator (TENG) energy harvesting technology. This activity addresses the critical issues of plastic waste pollution and the exhaustion of fossil fuel reserves. Annually, almost 8 million tonnes of plastic waste infiltrate the oceans, yet merely 9% of all plastic ever manufactured is effectively recycled. The collaborative effort encompassed Electrical Engineering, Computer Systems, Petroleum Engineering, Information Systems, Product Design, and Agrotechnology. The instructional techniques employed comprised lectures and conversations. The activity's results indicated that participants acquired a thorough comprehension of pyrolysis mechanisms, the principles of TENG energy conversion, and its possible applications at the village level. Despite the absence of direct implementation, the cadres shown significant motivation to facilitate technology adoption inside their communities. This PKM model demonstrates that interdisciplinary collaboration across universities is effective in providing innovative technological education to rural populations.
Perbandingan XGBoost, Random Forest, dan MLP untuk Klasifikasi Kesiapan Atlet Bintang Putra Wardana; Cahyono Budy Santoso
Jurnal Komtika (Komputasi dan Informatika) Vol. 10 No. 1 (2026)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v10.i1.16850

Abstract

Athlete readiness classification is critical for optimizing training load and preventing overtraining-related injuries. This study develops and compares three machine learning algorithms XGBoost, Random Forest, and MLP Neural Network to classify athlete readiness into three ordinal categories: Lower, Middle, and Upper. The dataset comprises 1153 instances with nine multidimensional features encompassing physiological parameters (training duration, intensity, interval days, points, recovery) and psychological indicators (mental score, athlete category, consistency). Data preprocessing involved label encoding for categorical variables and standard scaling for numerical features, followed by a stratified 80:20 train-test split. Model performance was evaluated using weighted precision, recall, F1-score, confusion matrix, and one-vs-rest ROC-AUC curves with 5-fold cross-validation. Results indicate that XGBoost achieved the highest predictive performance (F1-score: 0.93, AUC: 0.99), followed by Random Forest (F1-score: 0.91, AUC: 0.98) and MLP Neural Network (F1-score: 0.85, AUC: 0.94). Feature importance analysis revealed that mental score, training intensity, and consistency were the strongest predictors of readiness status. The proposed framework offers a robust, data-driven decision support tool for sports practitioners, enabling objective readiness monitoring and dynamic training adjustments. Future work will focus on real-time wearable integration and automated hyperparameter optimization
Co-Authors Aditiameri Aditiameri Bintang Putra Wardana Chaerul Anwar Hafid Roihan Hamuda, Hayadi Listiana Satiawati Lukman Medriavin Silalahi Muhammad Mujiburochman Raihan Cikal Herlambang Reyner Shaquille Rachim Safrizal Safrizal Sumihar M.L. Tobing Teddy Mohamad Darajat Yunus Widjaja