IMPLEMENTASI DATA MINING PENINGKATAN PRODUKSI BERAS MENGGUNAKAN METODE K-MEANS CLUSTERING

Abstract

Increasingly high growth rates in the world, especially in developing countries like Indonesia, have reduced the area of agricultural land, thereby reducing food production. Rice is one of the staple food ingredients which is an important commodity, especially in Indonesia because rice is the main consumption ingredient for people to obtain carbohydrate intake. This study discusses the implementation of data mining in increasing rice production in North Sumatra province using the K-Means Clustering algorithm as a solution to solving cases. The source of data in this study was obtained from BPS North Sumatra with 32 processed data. Data analysis in this study used 2 (two) cluster levels, namely the high cluster (C1) and the low cluster (C2). The research results obtained are that there are 3 cities/regencies that are included in the high cluster (C1) and there are 29 other cities/regencies that are included in the low cluster (C2). It is hoped that the research results can become input, suggestions and efforts for the North Sumatra provincial government to pay more attention to increasing rice production in each region so that it can meet the basic needs of the community so that it can increase food security more optimally.