SEGMENTASI PASAR MENGGUNAKAN K-MEANS CLUSTERING BERDASARKAN KUALITAS, HARGA, FASILITAS, KENYAMANAN, DAN KELENGKAPAN PRODUK

Abstract

This research aims to identify and characterize market segments based on five main variables, namely Quality, Price, Facilities, Comfort, and Completeness, using a quantitative method with the K-Means Clustering technique. Involving 45 respondents, this unsupervised learning algorithm successfully partitioned the data into three optimal clusters after undergoing five iteration stages until convergence was achieved. The results of the study show that Cluster 1 with 15 respondents is a group that emphasizes comfort and completeness, Cluster 2 with 13 respondents is highly selective regarding quality and price, while Cluster 3 with 17 respondents represents the largest segment that prioritizes capable physical facilities as the main attraction. Based on ANOVA analysis, the Facilities and Comfort variables are the strongest differentiating factors in cluster formation, while the perception of Price tends to be uniform across all groups.