Pengelompokkan Produksi Tanaman Jagung di Sumatera Utara Menggunakan Algoritma K-Medoids

 (*)Safruddin Safruddin Mail (Universitas Asahan, Kisaran, Indonesia)
 Joni Wilson Sitopu (Universitas Simalungun, Pematangsiantar, Indonesia)
 Azwar Anas Manurung (Universitas Asahan, Kisaran, Indonesia)
 Indra Satria (Universitas Asahan, Kisaran, Indonesia)
 Anjar Wanto (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)

(*) Corresponding Author

Submitted: January 20, 2023; Published: January 31, 2023

Abstract

Corn is a strategic commodity with bright marketing prospects, especially in North Sumatra. Therefore efforts to increase corn production need great attention because, with sufficient availability, it is hoped that the community's need for corn can be fulfilled and the selling price remains stable. This study aims to classify corn production in North Sumatra based on districts/cities so that districts/cities can be identified and developed into corn production centers to reduce food imports, specifically corn crops. This research uses a corn production dataset based on districts/cities in North Sumatra consisting of 25 regencies and eight cities in 2019-2021 obtained from the Food Crops and Horticulture Service of North Sumatra Province. The algorithm used is the K-Medoids algorithm with Rapid Miner Studio tools. The results of this study were grouping corn production which was divided into 5 (five) groups, including Group 1 was an area with very high corn production consisting of 1 Regency, Group 2 was an area with high corn production consisting of 2 Regencies, Group 3 was an area with moderate corn production consisting of 4 regencies, Group 4 is an area with low corn production consisting of 3 regencies, and Group 5 is an area with very low corn production consisting of 15 regencies and seven cities. Based on these results, Karo, Dairi, and Simalungun districts can be used as centers for corn production in North Sumatra because these three districts alone produce corn production of 65.7% of the total corn production in North Sumatra.

Keywords


Data Mining; Corn; K-Medoids; Mapping; Clustering; Production

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