SEGMENTASI KEPUASAN PENGGUNA APLIKASI TRANSPORTASI ONLINE MENGGUNAKAN METODE K-MEANS CLUSTERING (STUDI KASUS PENGGUNA GOMOVE)

Abstract

The rapid development of digital-based transportation applications has led to significant changes in public mobility behavior. Increasing competition among online transportation companies requires service providers not only to focus on acquiring new users, but also to enhance user satisfaction and retention. User satisfaction is inherently heterogeneous, as it is influenced by differences in individual experiences, perceptions, and preferences toward the services received. Therefore, an analytical approach that is capable of grouping users based on similarities in their service evaluations is essential. This study aims to identify user satisfaction segmentation of the GoMove online transportation application using the K-Means Clustering method. Data were collected through an online survey of active GoMove users who evaluated six main service aspects, namely ease of application use, driver response speed in accepting orders, travel safety, price affordability, vehicle comfort, and driver communication quality. The analysis results indicate the formation of three user clusters with distinct satisfaction characteristics. The ANOVA test confirms that all service aspects exhibit statistically significant differences in mean scores across clusters. Meanwhile, the Chi-Square test reveals a significant relationship between cluster membership and satisfaction with application features and driver performance, but no significant relationship with overall service satisfaction. These findings provide strategic implications for companies in designing more targeted and segmented service improvement and promotional strategies.