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All Journal Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Nasional Komputasi dan Teknologi Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Sains dan Teknologi TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Sistem Informasi dan Teknologi Jaringan International Journal of Basic and Applied Science Jurnal Media Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) DISTANCE: Journal of Data Science, Technology, and Computer Science Jurnal Mandiri IT Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Nuansa Informatika Jurnal Sistem Informasi Galuh Jurnal Ilmu Pendidikan dan Humaniora Jurnal Pengabdian Masyarakat Nauli
Manurung, Jonson
Universitas Pertahanan Republik Indonesia, Bogor, Indonesia
Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Physics Social Sciences Transportation

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Journal : Journal of Intelligent Decision Support System (IDSS)

 1 
Comparison of k-means clustering with hierarchical agglomerative clustering for the analysis of food security of rice sector in Indonesia Sinaga, Ryan Fahlepy; M Azhar Prabukusumo; Manurung, Jonson
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.290

Abstract

Indonesia's food security depends on the availability and distribution of rice as a staple food. To support data-driven policies, this study applies K-Means Clustering and Hierarchical Agglomerative Clustering (HAC) to cluster 38 provinces based on rice consumption and production patterns. Data is sourced from BPS with attributes: rice consumption per capita, rice production, rice price per kg, and population. These variables were chosen because they reflect the balance of demand, supply, affordability, and food needs. The optimal number of clusters was determined as three, based on Elbow Method and Silhouette Score for K-Means, and Dendrogram and Cophenetic Correlation Coefficient (CCC) for HAC. The clustering results identify regional characteristics related to food security and support the formulation of more targeted rice distribution policies. This study also compares the effectiveness of both methods in supporting equitable and sustainable food distribution strategies.
Mapping ownership of luxury goods and household assets in cities in Jawa Tengah using logistic regression Hanan, Rohman Ali; Firdaus, Eryan Ahmad; Manurung, Jonson
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.292

Abstract

Ownership of luxury goods and household assets is a crucial issue in the Indonesian economy, particularly in Jawa Tengah, as it reflects complex socio-economic dynamics. This study aims to map the distribution of luxury goods and household assets across regencies and cities in Jawa Tengah and analyze the factors influencing their ownership using logistic regression. Socio-economic disparities in asset ownership are driven by factors such as education, income, and access to information, which contribute to broader social inequality and regional economic development.Using data from the Jawa Tengah Statistics Agency, this study examines variations in asset ownership, including motorcycles, refrigerators, and land, across different regions. Findings indicate that regions with higher motor vehicle ownership tend to exhibit stronger economic welfare compared to those with lower asset ownership. Beyond economic factors, psychological and social aspects, including social status and religious influences, also shape decisions regarding luxury goods acquisition.This research contributes to the literature by addressing the underexplored local context of asset ownership in Indonesia. The findings provide insights for policymakers in designing more inclusive and responsive socio-economic policies, aiming to reduce disparities and promote equitable regional development.
Implementation of TOPSIS method in decision support system for used motorcycle purchase recommendation Putra, Muhammad Ridho Alghifari; Manurung, Jonson; Hidayati, Ajeng
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i2.289

Abstract

The selection of used motorcycles involves evaluating multiple criteria, such as price, production year, transmission type, vehicle type, mileage, fuel consumption, and engine capacity. This complex decision-making process often leads buyers to rely on subjective judgments or third-party recommendations, which may result in suboptimal choices. To address this issue, this research develops a decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision Making (MCDM) method, which ranks alternatives based on their proximity to the ideal solution. The study introduces innovation by applying TOPSIS to the specific context of used motorcycle selection, providing a data-driven, objective approach in contrast to conventional methods. A quantitative approach was employed, with data collected from online marketplaces and authorized dealerships. The results indicate that the 2019 Honda Revo, priced at Rp. 8,600,000, is the most optimal choice, achieving the highest preference score of 0.862887804. The effectiveness of the TOPSIS method in structuring the selection process ensures a more systematic and accurate decision-making process. Furthermore, the study highlights the influence of key criteria, such as fuel efficiency and mileage, in determining the ranking of alternatives. Future research should focus on integrating additional factors, such as maintenance history and vehicle condition, and exploring the development of web-based or mobile platforms to improve real-world implementation and enhance user accessibility. This system contributes to smarter, more informed decision-making in the used vehicle market, offering a significant advancement over traditional selection methods.
Heart disease prediction using machine learning models Vernando, Deden; Manurung, Jonson; Saragih, Hondor
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i2.291

Abstract

Heart disease remains one of the leading causes of death globally, with mortality rates continuing to rise each year. Early detection is critical to reducing the burden of this disease; however, conventional diagnostic methods are often costly, time-consuming, and reliant on specialist expertise. This study aims to evaluate the effectiveness of four machine learning (ML) algorithms—Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—in predicting heart disease using clinical datasets. The methodology involves data preprocessing, feature selection using the Random Forest algorithm, and performance evaluation through metrics such as accuracy, precision, recall, F1-score, and support. Experimental results indicate that KNN achieved the highest accuracy after feature selection, while SVM demonstrated the highest recall despite lower precision. RF offered the most balanced performance, making it a reliable model for real-world medical applications. These findings highlight the importance of selecting appropriate algorithms and features to improve the performance of predictive models. The study suggests that future research should incorporate larger datasets, apply systematic hyperparameter tuning, and explore deep learning techniques to further enhance prediction accuracy.
Co-Authors Agus Firmansyah Agustina Simangunsong Al Hashim, Safa Ayoub Amran Sitohang Andri Budiman, Mohammad Azhar Prabukusumo, Muhammad Barus, Nadela Bosker Sinaga Bosker Sinaga Bosker Sinaga, Bosker Sinaga Br Sitepu, Siska Feronika Br Tarigan, Nera Mayana Dhaifullah, Rendi Hanif Erika Novianti Eryan Ahmad Firdaus Febrian Wahyu Christanto Ferdinand Tharorogo Wau Firdaus Laia Firdaus Situmorang Hanan, Rohman Ali Hardy Priyatno Ambarita Harpingka Sibarani Hasugian , Paska Marto Hidayati, Ajeng Hondor Saragih Johanes Perdamenta Sembiring Kadin Darlianto Tinambunan Kanur L. P. Situmorang Logaraj Logaraj Logaraj, Logaraj M Azhar Prabukusumo Maria Siahaan Maya Theresia Br. Barus Maya Theresia Br. Barus Merlin Helentina Napitupulu Mina Kumari Muthmainnah, Ihmatull Nuriansyah, Agam Pandiangan, Boyner Poltak Sihombing Prabukusumo, Muhammad Azhar Putra, Muhammad Ridho Alghifari Ramen, Sethu Rinaldy Chaniago Sawaluddin Sawaluddin, Sawaluddin Sethu Ramen Sethu Ramen, Sethu Ramen Sidiq, Maulana Sihombing, Agus Putra Emas Sihotang, Amran Silalahi, Monalisa Hotmauli Simangunsong, Humala Sinaga, Ryan Fahlepy Sri Kumala Sari Topik Hidayat Uzitha Ram Vernando, Deden