Penerapan Algoritma K-Means Dalam Penggunaan Transportasi Online - Surya, Aditya (Jurnal Riset Informatika dan Teknologi Informasi, 2025)
The rapid evolution of information technology over recent decades has revolutionized many aspects of human life, not least in transportation services. Online ride‑hailing platforms have simplified daily mobility—users no longer need to scour the streets or wait at stands, but simply place an order via a smartphone app. Beyond point‑to‑point travel, these platforms have expanded into parcel delivery and food ordering, creating a comprehensive on‑demand ecosystem for modern urban needs. As usage of online transportation diversifies, it becomes crucial for service providers to understand dominant usage patterns so they can tailor operational and marketing strategies effectively. One powerful approach to uncover these patterns is clustering, which groups historical usage data into segments based on similar characteristics. In this study, two primary clusters are defined: C1 for frequent users of online transport, and C2 for those who use it less often or sporadically. This segregation enables a clearer distinction between active and passive user behaviors, leading to more focused insights. Applying clustering to the online travel dataset involves several preprocessing steps—removing duplicate entries, normalizing numeric fields (such as booking frequency and total expenditure), and encoding categorical variables (like service type and booking time)—before running the algorithm. The resulting clusters are then analyzed to pinpoint the factors driving high or low usage intensity, such as average trip duration, driver response times, and preferences for additional services like food or parcel delivery. By understanding the profiles of these two clusters, the research offers data‑driven recommendations to online transport companies: design more precisely targeted promotional packages, optimize route planning and fleet allocation, and boost user retention strategies. Ultimately, this study seeks to answer the question, “What needs and habits most frequently emerge in online transportation usage?”—enabling continuous refinement of services in line with user preferences and fostering sustainable business growth.