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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal Information System Development ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Sistem Informasi Kaputama (JSIK) Building of Informatics, Technology and Science Majalah Ilmiah Warta Dharmawangsa JTIK (Jurnal Teknik Informatika Kaputama) JUKI : Jurnal Komputer dan Informatika Jurnal Manajemen Informatika Jayakarta Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Journal of Vision and Ideas (VISA) Jurnal Pengabdian Masyarakat IPTEK EXPLORER Bulletin of Multi-Disciplinary Science and Applied Technology Journal Of Human And Education (JAHE) Journal of Information Systems and Technology Research Sci-Tech Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Ulead : Jurnal E-pengabdian Journal of Engineering, Technology and Computing (JETCom) Journal of Mathematics and Technology (MATECH) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) JOURNAL OF ICT APLICATIONS AND SYSTEM Jurnal Teknik, Komputer, Agroteknologi dan Sains Zadama: Jurnal Pengabdian Masyarakat TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi International Journal of Health, Engineering and Technology Jurnal Penelitian Sistem Informasi Indonesian Journal of Education And Computer Science Indonesian Journal of Science, Technology, and Humanities Pengabdian Pendidikan Indonesia (PPI) Jurnal Ilmu Komputer dan Sistem Informasi Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Switch: Jurnal Sains dan Teknologi Informasi Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi KETIK : Jurnal Informatika International Journal of Information Technology and Business Ulil Albab Pascal: Journal of Computer Science and Informatics Journal of Computer Science Artificial Intelligence and Communications Jurnal Ilmu Komputer dan Teknik Informatika Jurnal Pengabdian Masyarakat Berdampak Global Science: Journal of Information Technology and Computer Science Journal of Data Science and Informatics Engineering Journal of Information Technology and Systems Engineering
Rusmin Saragih, Rusmin
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Application of Decision Tree Algorithm Method to Analyze Traffic Accident Patterns Rusmin Saragih; Marto Sihombing; Anton Sihombing; Rivalri Kristianto Hondro
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp200-202

Abstract

Traffic accidents are complex problems that involve many variables such as weather conditions, vehicle type, location, and driver behavior. With the development of data processing technology, it is possible to analyze accident data in more depth to find significant hidden patterns. The Decision Tree algorithm is applied to predict the likelihood of an accident occurring and identify the factors that contribute most to the accident. The data used consists of accident records collected from various sources, including official reports and traffic statistics. The Decision Tree algorithm was chosen due to its ability to handle both categorical and numerical data, as well as the ease of interpretation of the analysis results. The results of this study show that factors such as vehicle speed, time of occurrence, and road conditions have a significant influence on the probability of an accident occurring. The results of news extraction are analyzed by creating decision rules to determine the pattern of accidents that occur. This decision rule is in the form of a decision tree with a dataset that uses data with the highest fatalities with the imputation feature mode by concept as a method of handling missing values and toll roads as attributes, resulting in an f1-score value of 60.00% and an accuracy value of 70.40%.
Improving Operational Efficiency in Digital Printing Services Through a Web-Based Information System: An Empirical Case Study Rusmin Saragih; Eka Pandu Cynthia; Maulidania Mediawati Cynthia
Journal of Information Technology and Systems Engineering Vol. 1 No. 1 (2026): June 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/jitse.v1i1.2

Abstract

The increasing demand for efficiency and accuracy in service industries has encouraged the adoption of digital technologies, particularly in small and medium-sized enterprises (SMEs) such as printing services. However, many organizations still rely on manual or semi-digital processes, leading to inefficiencies, data inconsistencies, and communication gaps across operational units. This study aims to improve operational efficiency in digital printing services through the design and implementation of a web-based information system. A qualitative case study approach was employed at CV. Raskha Media Group, with data collected through observation, interviews, and documentation. The system was developed using the Waterfall model and modeled with Unified Modeling Language (UML) to ensure structured development. The implementation integrates order management, inventory control, payment processing, and production workflows into a unified platform. System evaluation was conducted using Black Box and White Box testing, as well as ISO/IEC 25010 and System Usability Scale (SUS). The results show a 100% functional success rate, a SUS score of 74.1 indicating good usability, and an overall quality score of 89%, reflecting high system performance. These findings demonstrate that the proposed system effectively reduces operational errors, enhances inter-departmental communication, and improves overall efficiency. This study contributes to the advancement of information systems research by providing empirical evidence of the role of integrated web-based systems in supporting digital transformation and business process optimization in service-oriented SMEs.
Pelatihan Peningkatan Kompetensi Guru dalam Penggunaan Canva untuk Pembelajaran Berdiferensiasi pada Kurikulum Merdeka Imeldawaty Gultom; Rusmin Saragih; Emma Martina Pakpahan
Pengabdian Pendidikan Indonesia Vol. 2 No. 01 (2024): Artikel Periode April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ppi.v2i01.4528

Abstract

Pelatihan peningkatan kompetensi guru dalam penggunaan Canva untuk pembelajaran berdiferensiasi di SMP Swasta Primbana Medan bertujuan untuk memperkuat keterampilan guru dalam memanfaatkan teknologi untuk mendukung implementasi Kurikulum Merdeka. Kegiatan ini berangkat dari kebutuhan untuk meningkatkan kualitas materi pembelajaran dan keterlibatan siswa melalui penggunaan alat digital yang inovatif. Pelatihan ini melibatkan sesi teori dan praktik yang dirancang untuk mengenalkan fitur Canva dan aplikasinya dalam pembuatan materi ajar yang visual dan interaktif. Metodologi yang digunakan dalam pelatihan ini mencakup pendekatan kualitatif dan kuantitatif. Data dikumpulkan melalui wawancara, kuesioner, observasi, dan analisis dokumen untuk menilai peningkatan keterampilan guru dan kualitas materi pembelajaran yang dihasilkan. Hasil dari pelatihan menunjukkan peningkatan signifikan dalam keterampilan guru dalam menggunakan Canva, yang berdampak positif pada kualitas materi pembelajaran. Guru-guru di SMP Swasta Primbana Medan kini dapat menciptakan materi yang lebih menarik dan sesuai dengan kebutuhan siswa, yang pada gilirannya meningkatkan keterlibatan dan motivasi siswa. Pelatihan ini juga berhasil membentuk komunitas belajar di kalangan guru, di mana mereka saling berbagi pengetahuan dan pengalaman dalam penggunaan Canva. Temuan ini menunjukkan bahwa integrasi teknologi seperti Canva dapat mendukung pembelajaran berdiferensiasi dan pelaksanaan Kurikulum Merdeka dengan lebih efektif. Rencana tindak lanjut meliputi pendampingan berkelanjutan dan pengembangan modul pelatihan tambahan untuk memperkuat kemampuan guru dalam memanfaatkan teknologi pendidikan.
Benchmarking Machine Learning Models for Large-Scale Loan Default Prediction Using Real Data Yudo Devianto; Rusmin Saragih; Yana Cahyana
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 1 (2026): March: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i1.181

Abstract

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.
Rancang Bangun Sistem Monitoring Emisi Gas Buang Pada Ruang Parkir Bawah Tanah Gedung Perkantoran Menggunakan Internet of Things (IoT) Muhammad Raihan; Novriyenni; Rusmin Saragih
Indonesian Journal of Education And Computer Science Vol. 3 No. 1 (2025): INDOTECH - April 2025
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v3i1.1183

Abstract

Penelitian ini bertujuan merancang dan membangun sistem monitoring emisi gas buang di ruang parkir bawah tanah gedung perkantoran dengan memanfaatkan teknologi Internet of Things (IoT). Sistem ini mengintegrasikan sensor MQ-7 dan MQ-135 untuk mendeteksi gas berbahaya seperti karbon monoksida (CO), sulfur dioksida (SO₂), dan nitrogen oksida (NOₓ). Data hasil deteksi dikirim secara real-time melalui modul ESP32 ke aplikasi Blynk, sehingga memungkinkan pemantauan kualitas udara secara terus-menerus dan jarak jauh. Selain itu, sistem ini juga dilengkapi dengan indikator visual berupa LED dan buzzer sebagai peringatan dini apabila konsentrasi gas melebihi ambang batas yang ditentukan. Hasil pengujian menunjukkan bahwa sistem ini mampu mendeteksi dan memantau emisi gas secara akurat serta memberikan notifikasi yang dapat digunakan sebagai dasar pengambilan tindakan preventif. Dengan demikian, sistem ini dinilai efektif dan andal dalam menjaga kualitas udara di area parkir tertutup. Kesimpulannya, implementasi sistem monitoring berbasis IoT ini berpotensi besar untuk meningkatkan keselamatan dan kesehatan pengguna ruang parkir bawah tanah melalui pemantauan kualitas udara yang efisien, real-time, dan responsif terhadap kondisi lingkungan.
Multi-Objective Reinforcement Learning for Supply Chain Optimization in Indonesia’s Free Nutritious Meal Program Budi Yanto; Rusmin Saragih; Adyanata Lubis; Elyandri Prasiwiningrum; Romy Wahyuny
International Journal of Information Technology and Business Vol. 8 No. 2 (2026): April : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.822026.18-27

Abstract

Indonesia’s Free Nutritious Meal Program (MBG) requires an efficient and adaptive supply chain system to ensure timely distribution, cost efficiency, and adequate nutritional delivery for a large number of beneficiaries. However, conventional supply chain approaches are generally static and unable to respond effectively to dynamic demand, supply uncertainty, and logistical constraints. This study proposes a Multi-Objective Reinforcement Learning (MORL) model to optimize the MBG supply chain by simultaneously considering distribution cost, delivery timeliness, service level, nutritional adequacy, and food waste reduction. The model is developed using a simulation-based environment representing real-world supply chain conditions, including demand variability, transportation limitations, and kitchen capacity constraints. The results show that the proposed approach achieves cost reductions of 15–22%, improves delivery timeliness by 18–25%, maintains a service level above 90%, increases nutritional fulfillment by 12–18%, and reduces food waste by 10–15% compared to baseline methods. Sensitivity analysis further demonstrates the robustness of the model, with minimal performance degradation under disruption scenarios. These findings indicate that Reinforcement Learning provides a scalable and adaptive solution for optimizing large-scale public food distribution systems. The proposed model contributes both theoretically by integrating multi-objective optimization within an RL framework and practically by supporting data-driven decision-making for improving the effectiveness of the MBG program in Indonesia.
The Use of Knowledge Management Systems to Improve Decision-Making in Local Government Rusmin Saragih; Yuyun Dwi Lestari; Yessi Fitri Annisah Lubis; Divi Handoko
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 1 (2025): May 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i1.22

Abstract

Effective and data-driven decision-making has become an urgent need for local governments in facing the challenges of public service complexity, socio-economic dynamics, and demands for transparency and accountability. One of the strategic approaches to support this process is through the implementation of a Knowledge Management System (KMS). This research aims to explore the role and impact of KMS implementation on the improvement of decision-making quality in regional government organizations. A qualitative approach is used in this study with a case study method on several regional government agencies in Indonesia that have implemented KMS, combined with an analysis of related academic literature. Research results show that KMS is capable of improving the efficiency of storage, distribution, and access to organizational knowledge, both tacit and explicit. KMS supports faster, more accurate, and participatory decision-making because strategic information can be obtained and used promptly by policymakers. The findings also indicate that the success of KMS implementation is greatly influenced by organizational culture, leadership support, and the capacity of human resources in managing and sharing knowledge. This study recommends the comprehensive integration of KMS into the government work system, with an emphasis on training aspects, digital infrastructure, and internal policies that support the knowledge-sharing process. The theoretical and practical implications of these findings are an important contribution to the development of knowledge-based governance at the regional level.
Implementation of Mechine Learning Eligibility for Customer Credit Payments at Bank BTN Using the K – Nearst Neighbor Algorithm Ema Sari Suwandi; Relita Buaton; Rusmin Saragih
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.246

Abstract

Credit is the provision of money or bills that can be equated with that, based on a loan agreement or agreement between a bank and another party that requires the borrower to pay off the debt after a certain period of time with interest (Government of Indonesia, 1998). In its initial development, credit had a function in stimulating mutual assistance aimed at meeting needs, both in the field of business and meeting daily needs.In developing applications, it is necessary to predict applications at Bank BTN Medan accurately, accurate prediction results are very important in showing the right rating and decision-making in selecting customers. When customers experience arrears, the system used by Bank BTN Medan is still manual and has not applied predication in credit arrears to customers of Bank BTN Medan. Tests carried out in this test use a credit customer dataset from Bank BTN Medan. This study predicts the eligibility of customer credit payments at Bank BTN with the K – Nearst neighbor algorithm. The prediction of the level of smoothness of credit payments is made using K-Nearest Neighbor in order to be able to predict the smoothness of future credit payments.
Clustering Disease on Settlements Inhabitant In place seedy With Use Clustering Method Ruine Buana Br Sitepu; Achmad Fauzi; Rusmin Saragih
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.275

Abstract

Residents living in slum areas often face serious problems related to public health, where the prevalence of disease tends to be high and its spread is difficult to control. The impact of the formation of slums for the community is that safety is threatened, health deteriorates, and social conditions worsen, causing many diseases for people living in slums. Therefore, this study aims to identify patterns and clusters of diseases that exist in residential areas in slums Binjai city using clustering method. The K-Means Algorithm clustering method was chosen because it is able to group data based on similar characteristics, so that it can help identify diseases in a more focused and efficient manner, using the MATLAB application is also very appropriate in this problem so that it can produce output from data mining that can be used in decision making. future decisions. By utilizing the data mining process using the clustering method, clustering can be a problem of grouping diseases in slum settlements. Based on the results of trials with 20 sample data conducted with MATLAB obtained in cluster 1 DHF cases with high slums, Cluster 2 cases of vomiting with moderate slums and cluster 3 cases of diarrhea with moderate slums. The results of this study are expected to provide in-depth insight into disease patterns and clusters in residential areas in slums.
Expert System To Determine Psychological Disorders In Chronic Kidney Failure (CKD) Patients Undergoing Hemodialysis Therapy Using Certainty Factor Method SELVY SELVY ANGGRAINI; Rusmin Saragih; Husnul Khair
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.280

Abstract

Chronic Kidney Failure (CKD) is damage to the kidneys both in structure and/or function that lasts for 3 months or more. Hemodialysis is a prolonged therapy that can significantly impact the physical and psychological well-being of patients with chronic kidney disease. This therapy has a big effect on sufferers. The psychological impact that appears can affect the success of therapy so it is important to recognize these symptoms and provide appropriate treatment to overcome them. Based on research at Delia General Hospital, patients who will undergo Hemodialysis therapy must come to the hospital to receive comprehensive therapy by a doctor. Long patient queues when undergoing therapy can make patients tired and remember the patient's condition in order to get information and therapy. Handling of these problems can be overcome by building a system that can determine psychological disorders in patients. Expert systems are computer-based systems that use knowledge, facts and reasoning techniques in solving problems that usually can only be solved by an expert in a particular field. Certainty Factor (CF) is a method capable of defining the degree of certainty of a rule or fact in describing an expert's belief in the problem at hand. With an expert system, it can help identify and determine early on psychological disorders in patients. From the results of trials conducted by expert systems to determine psychological disorders in patients with kidney failure using the Certainty Factor method, the highest value is depression with a percentage of 94.59%.
Co-Authors , Eka Putra ., Novriyenni Abdul Azan Abdul Azan Abdullah Hamid Abdullah Husein Achmad Fauzi ACHMAD FAUZI Adyanata Lubis Ahmad Jurnaidi Wahidin Alfina Damayanti Ambarita, Indah Andini Andini Andre Adrian Andrean Samuel Siahaan Aprilianda, Dinda Arianta Bangun Arnes Sembiring Asih, Munjiat Setiani Barany Fachri Boyke Gunawan Manurung Br Sitepu, Dinda Isabella Buaton, Relita Budi Yanto Chairul Rizal Charles Jhony Mantho Sianturi, Charles Jhony Mantho Cindy Primadona Siahaan Damayanti, Fera Dandi Satria R Darmawan Ginting Deni Apriadi Dewantara, Nowell Dimas Prayogi Dinda Firdawati Simamora Divi Handoko Divi Handoko Eka Pandu Cynthia Eka Pandu Cynthia Eka, Muhammad Elyandri Prasiwiningrum Ema Sari Suwandi Enda Ribka Meganta P Erbin Sitorus Erlita Sulistiati Fany Juliawati Fatimah Fatmaira, Zira Fauzi, Achmad frans ikorasaki Fuzy Yustika Manik Fuzy Yustika Manik, Fuzy Yustika Gea, Fide Evianti Gultom, Imeldawaty Herdiansyah Harahap Herdiansyah Harahap Hesty Vitara I Gusti Prahmana Ikhsan Arif Indra Prasetia, Indra Irfan Yusuf Ismi Asmita Jesayas Sembiring Khair, Husnul Khalidy, Furqan Lili Musarofah Lili Musarofah M. Yogi Riyantama Isjoni Magdalena Simanjuntak Mardiah Marto Sihombing Marto Sihombing Maulidania Mediawati Cynthia Meisaroh Melda Pita Uli Sitompul Mhd Ferdiansyah Putra Mili Alfhi Syari Muhamad Furqon Muhammad Danil Syahputra Muhammad Danil Syahputra Muhammad Eka Muhammad Eka Muhammad Noor Hasan Siregar Muhammad Raihan Muhammad Reza Habibi Muhammad Zen, Muhammad Munadi Munadi Nadia Nurhafiza Nasril Hidayat Nico Kurniawan Purba Nikous Soter Sihombing Novriyenni Novriyenni Novriyenni, Novriyenni Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurlaila Nurlaila Nuryahati - Pakpahan, emma martina Pakpahan, Victor Maruli Pardede, Akim Manaor Hara Pasaribu, Tioria Petrus Loo Rafli Fitriawan Rahayu Utami Rahmadani Rahmadani Rahmawati Rahmawati, Rahmawati Ramadani, Suci Ramli Ramli Ramos Parulian Ambarita Ratih Puspadini Rianty Zabitha Siregar Ricky Ramadhan Harahap Rivalri Kristianto Hondro Rizki Kurniawan Romy Wahyuny Ruine Buana Br Sitepu Ryan Hidayat Saripurna, Darjat Satria R, Dandi SELVY SELVY ANGGRAINI Sihombing, Anton Sihombing, Marto Simanjuntak, Magdalena Simanjuntak, Magdalena Sinaga, Ayu Puspita Sari Sirait, Win Gomgom Parsaulian Siswan Syahputra Sonadi Perangin Angin Suci Pratiwi, Kiki Supiyandi Supiyandi Syahputra, Siswan Syari, Milli Alfhi Tantia Azzahra Tata Mustika Dewi tata, tatamustikadewi Theodora MV Nainggolan Tiwuk Widiastuti Ulandari, Seri Wati, Sri Kesuma Yani Maulita Yekolya Anatesya Yessi Fitri Annisah Lubis Yudo Devianto Yulia Ningsih Yusuf Afani Yuyun Dwi Lestari Yuyun Dwi Lestari