Penerapan GMM dan ARM untuk Analisis Mobilitas Penumpang TransJakarta
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Abstract
TransJakarta passenger mobility shows dynamic patterns influenced by travel time, duration, service-use intensity, and route connectivity. This study constructs probabilistic passenger mobility segments and analyzes inter-corridor travel associations within each segment. The data consist of TransJakarta tap-in and tap-out transactions processed through data quality assessment, time transformation, filtering, feature engineering, missing value imputation, data cleaning, and variable normalization. Passenger segmentation was performed using Gaussian Mixture Model with model selection based on Bayesian Information Criterion, while travel association patterns were analyzed using Association Rule Mining. The final dataset consisted of 168,132 observations, and the selected GMM formed five mobility clusters. The ARM results show that the most numerous and stable association rules appeared in commuter segments, especially Evening Commuter and Early Morning Commuter. These findings indicate that combining GMM and ARM provides a structured understanding of passenger behavior segmentation and inter-corridor travel relationships.
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