Pengelompokan Komoditas Ekspor Laut Indonesia Berbasis K-Means Clustering dalam Sistem Informasi WEB - Juseka, Christien Julio; Fibriani, Charitas (Program Studi Teknik Informatika Universitas Multi Data Palembang, 2026)
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.