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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Education and Mathematical Science Informatics and Computer Engineering Journal Jurnal Ilmu Komputer dan Informatika
Sipayung, Sardo
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics

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Integration Of Pca And K-Means Clustering For Staple Food Segmentation In Support Of National Food Policy Sipayung, Sardo; Hasugian, Paska Marto
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15343

Abstract

This study aims to develop cross-provincial staple-food segmentation by integrating Principal Component Analysis (PCA) and K-Means to support policy formation. The dataset comprises 2023 staple-food consumption for 34 Indonesian provinces across six indicators from BPS/SUSENAS. All indicators were standardized using z-score, reduced via PCA, and the resulting component scores were used as inputs to K-Means. Three components (PC1–PC3) explained 73.86% of the variance and captured shifts between sweet/animal-based vs. plant foods, fatty or animal-based grains, and the energy contribution of fat. The optimal number of clusters was determined as k = 3, yielding Silhouette = 0.466 and DBI = 0.733, indicating sufficiently compact and well-separated groups. The results reveal three segments: the first group consists of 11 provinces that are predominantly plant-based with low sugar and low animal-based consumption; the second group includes 13 provinces characterized by high animal-based and high-fat consumption; and the third group comprises 10 provinces with low-fat diets and fresh plant-based consumption. Stability checks on initialization and a leave-one-feature-out procedure confirmed consistent assignments. This fills an empirical gap: to our knowledge, no prior research integrates PCA with K-Means for cross-provincial staple-food segmentation in Indonesia while also reporting internal validation. Practically, the study provides operational segmentation to support food-security interventions moving beyond composite indices toward actionable targeting for production support, supply/price stabilization, and improved nutritional access thereby reframing IKP/FSVA from index-ranking to evidence-based segmentation.
Pemodelan Tren Pendapatan Game Gacha Menggunakan Regresi Linier Tambunan, Yosua; Yudi Yohannes; Sipayung, Sardo
Informatics and Computer Engineering Journal Vol 6 No 1 (2026): Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v6i1.11840

Abstract

The rapid growth of the digital gaming industry, particularly games with gacha monetization systems, has resulted in highly fluctuating revenue patterns over time. Game revenue does not always follow a linear trend, as it is influenced by various factors such as in-game events, content updates, and player behavior. This study aims to analyze the effect of time variables (months) on the revenue of three popular gacha games, namely Genshin Impact, Honkai: Star Rail, and Zenless Zone Zero. This research employs a quantitative approach using simple linear regression analysis. The data used in this study are secondary data in the form of monthly revenue collected from AppMagic, covering the period from January to December 2025. The results indicate that all three games have negative regression coefficients, suggesting a declining revenue trend over time. However, the coefficient of determination values are relatively low, indicating that the time variable has a very limited ability to explain revenue variation. These findings suggest that gacha game revenue is not significantly influenced by time alone but is driven by other non-linear factors. Therefore, simple linear regression can only provide a general trend and is insufficient to fully describe the dynamics of gacha game revenue.
Classification of Hydrometeorological Disaster Vulnerability Across Indonesian Provinces Using the KNN Algorithm Based on 2024 Podes Data Jesika, Jesika; Mahoro, Zamiel Alfaro Davido; Sipayung, Sardo
Indonesian Journal of Education and Mathematical Science Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara (UMSU)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/ijems.v7i1.28869

Abstract

Hydrometeorological disasters have increasingly posed significant challenges to regional resilience in Indonesia, driven by climate variability and uneven mitigation capacity across provinces. This study aimed to classify hydrometeorological disaster vulnerability across all Indonesian provinces using a machine learning approach based on the 2024 Village Potential Statistics dataset. A supervised learning framework was implemented using the k-Nearest Neighbor algorithm to integrate physical exposure indicators, including riverbank and slope settlements as well as river proximity, with mitigation capacity variables such as Early Warning Systems and evacuation infrastructure. Provincial-level data were aggregated, normalized, and processed following the Knowledge Discovery in Databases methodology. The classification results categorized provinces into low, medium, and high vulnerability levels, revealing that mitigation capacity played a critical role in moderating disaster vulnerability beyond physical exposure alone. Model evaluation demonstrated strong performance, with a high discriminative capability and balanced accuracy across classes, indicating that the selected k-Nearest Neighbor configuration was suitable for heterogeneous socio-environmental data. The findings highlighted the importance of preparedness infrastructure in reducing disaster risk and provided a transparent, data-driven framework to support evidence-based disaster management and policy planning at the national scale.
Klasterisasi Prestasi Provinsi Dalam Kompetisi Sains Nasional Menggunakan K-Means Hutasoit, Ronald; Hasiholan, Budi Chandra; Sipayung, Sardo; Luahambowo, Angelus Fanotona
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The National Science Olympiad (KSN) is one of strategic indicators to measure the quality of education throughout Indonesia. But historical evidence shows a wide gap in performance between provinces, not least between Java and beyond Java. This research aims to map and categorize provinces in Indonesia on their performance in OSN using K-Means clustering. The data employed was the number of participants and the weighted medal tallies per province. Preprocessing of Data: Z-Score standardization of data is performed to account for variances in scale between features. The best number of clusters is performed by Elbow Method and Silhouette Score. The results revealed three clusters: Cluster 1 (High Achievers) included 3 provinces with the highest Java; Cluster 2 (Potential Achiever), included four provinces with high participation and fair scores, and Cluster 3 (Developing Area), included 31 low-performing provinces. This map shows objective evidence of educational disparity, and can be used by the government as a guide for future development in low performing regions.
Co-Authors Hasiholan, Budi Chandra Hasugian , Paska Marto Hutasoit, Ronald Jesika, Jesika Luahambowo, Angelus Fanotona Mahoro, Zamiel Alfaro Davido Tambunan, Yosua Yudi Yohannes