Implementasi K-means untuk Clustering pada Industri Non-Agro di Jawa Tengah

Abstract

This study aims to segment the non-agro industry in Central Java Province using the K-Means Clustering method. The approach used refers to the Knowledge Discovery in Database (KDD) stages, which include data selection, preprocessing, transformation, data mining, and interpretation/evaluation. Data were obtained from the Central Java Open Data portal, with variables used including district/city, industry type, business scale, marketing reach, and energy use. The preprocessing stage was carried out through data cleaning, integration of several regional files, and normalization using StandardScaler. The data mining process was carried out using the K-Means algorithm and the determination of the optimal number of clusters using the Elbow Method. The results of the study indicate the formation of several clusters that describe market segmentation patterns in the non-agro industry based on similarities in operational characteristics between regions in Central Java.