Clustering Production of Plantation Crops by Province Using the K-Means Method

Azhari Abdillah Simangunsong (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
Indra Gunawan (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
Zulaini Masruro Nasution (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)

JOMLAI: Journal of Machine Learning and Artificial Intelligence
Vol. 1 No. 4 (2022): December

The purpose of this research is to classify the results of plantation crop production each year based on provinces in Indonesia, so that it can be known which provinces produce the most plantation crop production and which produce less. In this study using the K-Means Algorithm Data Mining technique. The data source for this research was collected based on plantation data obtained from the Indonesian Central Bureau of Statistics (BPS). The data used is data from 2018-2020 which consists of 34 provinces. The results of this study are groupings which are divided into 3 Clusters, namely low Clusters, medium Clusters, and high Clusters. Based on the results of calculations using the K-Means Algorithm, 6 items (Provinces) were obtained for high Clusters, 2 Provinces for medium Clusters and 27 Provinces for low Clusters. The conclusion that can be obtained is that the grouping of plantation crop production in Indonesia can be solved by applying the K-Means algorithm.