Kohesi: Jurnal Sains dan Teknologi

Articles

Vol. 11 No. 3 (2026): Kohesi: Jurnal Sains dan Teknologi, ISSN 3025-1311

PENGELOMPOKAN DATA PROGRAMA PENYULUHAN PERTANIAN BERDASARKAN KARAKTERISTIK PERTANIAN MENGGUNAKAN METODE CLUSTERING (STUDI KASUS: BPP SELESAI KABUPATEN LANGKAT)

DOI:
https://doi.org/10.2238/baj6jh48
Submitted
August 20, 2026
Published
2026-08-20

Abstract

The Agricultural Extension Office (BPP) of Selesai District, Langkat Regency holds a large volume of Agricultural Extension Program data with varying characteristics of Commodity, Land Area, and Production, which makes manual grouping difficult and hinders the preparation of well-targeted extension programs. This study aims to apply a Data Mining approach using the Clustering technique with the K-Means Algorithm to group Agricultural Extension Program data at BPP Selesai, Langkat Regency, based on the variables of Commodity, Land Area (Ha), and Production (Ton). The dataset consists of 555 records from 2023-2025, processed through the Knowledge Discovery in Database (KDD) stages of data selection, preprocessing, transformation, data mining, and evaluation, and computed using the MATLAB application. The developed program allows the user to select the number of clusters (K), run the clustering process repeatedly, and keep the result with the smallest SSE (Sum of Squared Error) as the best result. With K=3 clusters, the results yield a best SSE of 294.3742, where Cluster 1 consists of 309 records characterized by Corn with low land area and production, Cluster 2 consists of 201 records characterized by Rice with moderate land area and production, and Cluster 3 consists of 45 records characterized by Rice with high land area and production. These clustering results can serve as supporting information for agricultural extension workers in formulating more targeted extension programs.

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