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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Yunus Widjaja
Universitas Pembangunan Jaya, Tangerang Selatan
Computer Science & IT Decision Sciences, Operations Research & Management

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Pengelompokan Tanaman Perkebunan Berdasarkan Produktivitas dan Luas Lahan dengan K- Means Clustering Ethaniel Williano Adhi Putra; Yunus Widjaja
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9518

Abstract

Plantation data in West Java was grouped based on land area and crop productivity using the K-Means method. This data was obtained from Open Data Jabar from 2022 to 2024 and analyzed using a quantitative approach. Three groups can be identified based on the clustering results: one group has high productivity but relatively limited land area, another has large land area but suboptimal productivity, and the last group has equally low productivity and land area. The results indicate that land area does not always correlate with productivity. This study emphasizes the importance of selecting relevant variables and using methods consistently to produce more accurate and understandable analyses.
Analisis Pengelompokan Wilayah Berdasarkan Frekuensi Kejadian Banjir Menggunakan K-Means Clustering Cinta Aurelya; Yunus Widjaja
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.880

Abstract

Floods are among the most frequent natural disasters occurring in West Java Province and have significant impacts on social and economic conditions. Although the government provides flood incident frequency data down to the village and sub-district levels, its utilization for detailed vulnerability analysis remains limited. This study aims to classify regions based on the frequency of flood events using the K-Means Clustering method as an analytical approach to produce a more comprehensive risk mapping. The dataset consists of flood incident records from 2022 to 2024 obtained from official government sources. The analytical process follows the stages of Knowledge Discovery in Database with a primary focus on the implementation of the K-Means algorithm, while model evaluation is conducted using the Elbow Method and Silhouette Score to determine the optimal number of clusters. The results indicate that three clusters provide the most structured grouping of flood risk. The low-risk cluster consists of 12,454 regions that experienced zero flood events. The medium-risk cluster includes 3,404 regions with flood frequencies ranging from 1 to 6 events. Meanwhile, the high-risk cluster comprises 76 regions with flood occurrences between 7 and 33 events. These findings are expected to support flood mitigation planning, spatial planning strategies, the development of flood-control infrastructure, and to assist communities in evaluating the safety of potential residential areas.
Co-Authors Cinta Aurelya Ethaniel Williano Adhi Putra