Application of Machine Learning in Clustering Maize Producing Regions in Indonesia

Eliyani Eliyani; Saruni Dwiasnati; Sutan Mohammad Arif; Reza Avrizal; Nona Fatimah

doi:10.18495/comengapp.v13i2.455

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Affiliations

Eliyani Eliyani
Universitas Mercu Buana

Saruni Dwiasnati
Universitas Mercu Buana

Sutan Mohammad Arif
Universitas Indraprasta PGRI

Reza Avrizal
Universitas Indraprasta PGRI

Nona Fatimah
Universitas Mercu Buana

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Application of Machine Learning in Clustering Maize Producing Regions in Indonesia

Eliyani Eliyani ,
Saruni Dwiasnati ,
Sutan Mohammad Arif ,
Reza Avrizal ,
Nona Fatimah

Vol 13 No 2 (2024)

DOI 10.18495/comengapp.v13i2.455

Submitted: Sep 12, 2023

Published: Jun 1, 2024

Abstract

Maize is considered an important commodity with promising market prospects. Given the importance of maize, there is a need to increase maize production to meet people's needs and maintain price stability. This study aims to group maize production in Indonesia by region, with the hope of finding areas that have the potential to become maize production centers to reduce dependence on imports. The data used in this research was obtained from the Central Statistics Agency, covering information from 34 provinces during the 2017-2021 period. This analysis uses the K-Means method with the Python programming language. The number of groups is determined using the Elbow Method. The results of this research show that there are three categories of maize production regions: regions with low maize production (below average), regions with medium maize production, and regions with high maize production. A total of 25 provinces are in the low production category, eight provinces are in the medium category, and only East Java is in the high production category.