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All Journal Algoritme Jurnal Mahasiswa Teknik Informatika
Juseka, Christien Julio
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Computer Science & IT

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Pengelompokan Komoditas Ekspor Laut Indonesia Berbasis K-Means Clustering dalam Sistem Informasi WEB Juseka, Christien Julio; Fibriani, Charitas
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13737

Abstract

This research presents the design of a web-based information system (SILAUTINA) for clustering Indonesia's marine export commodities based on their economic potential. The dataset was obtained from the Ministry of Marine Affairs and Fisheries, containing attributes such as commodity name, export volume (tons), and export value (million USD). Data preprocessing involved cleaning, normalization, and clustering using the K-Means algorithm with K=3, which was determined objectively through the Elbow and Silhouette methods. The results yielded three main clusters: superior, potential, and super superior, classified based on average export volume and value. The system was built using the Flask framework and visualized the results through interactive Scatter plots. Evaluation using Silhouette Score (0.62) and Davies–Bouldin Index (0.49) demonstrated that the applied method effectively maps the economic potential of marine commodities. The SILAUTINA system can be utilized as a data-driven decision-making tool for policymakers in strategic marine export planning. Thus, this research not only provides a validated clustering methodology but also delivers an operational platform that can be readily adopted by stakeholders in the maritime sector.
Co-Authors Charitas Fibriani