Analisa Pola Penyebaran Pengguna Layanan Transjakarta dengan Metode K-Means Clustering - Reynaldi , Reynaldi; Faiz Djarot, Raihan Jamal; Wahyudi, Mochamad; Sumanto , Sumanto; Budiman, Ade Surya (Journal Software, Hardware and Information Technology, 2025)
This study analyzes the spatial distribution patterns of Transjakarta service users in Jakarta using the K-Means Clustering algorithm. The dataset, obtained from the Kaggle platform, consists of 189,501 passenger transaction records, including tap-in and tap-out locations, travel times, and user-related information. The research process involves data collection, preprocessing to remove missing values, application of the K-Means Clustering algorithm, and determination of the optimal number of clusters using the elbow method. Based on the analysis, the optimal number of clusters is identified as four (K=4). A scatter plot visualization presents user distribution patterns based on geographic coordinates and service usage times. Each cluster represents a group of users with similar travel characteristics. This analysis results in a segmentation that reflects variations in Transjakarta passenger mobility patterns and illustrates how travel activity is distributed across spatial and temporal dimensions within the urban area of Jakarta.