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All Journal Journal on Informatics Visualization and Social Computing
Khulika Malkan
Telkom University

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Spatial Analysis and Classification of Traffic Congestion Levels Using a Hybrid Machine Learning Approach in Badung Regency, Bali Rizal Wahyu Pratama; Mikhael Setia Budi; Eka Sahputra; Khulika Malkan; Wisnu Aji Sanjaya; Yoka Romadani
Journal on Informatics Visualization and Social Computing Vol. 2 No. 1 (2026): Journal on Informatics Visualization and Social Computing (JIVSC)
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/jivsc.v2i1.192

Abstract

Background: Traffic congestion in Badung Regency, Bali, presents complex challenges for transportation management in a strategic tourism area, necessitating intelligent monitoring methods that surpass conventional surveys.Aim: This study develops a Hybrid Machine Learning framework that integrates unsupervised and supervised learning techniques to map spatial patterns and precisely predict congestion anomalies.Methods: Initially, the K-Means Clustering algorithm validated the segmentation of traffic data into five optimal clusters (k=5) based on Elbow and Silhouette Score tests, which subsequently served as the ground truth for data labeling. Furthermore, a comparative evaluation of Random Forest, XGBoost, and Gradient Boosting models was conducted to determine the best predictive performance.Result: Experimental results identified Random Forest with strict pruning parameters (max_depth=3) as the optimal model, balancing accuracy and generalization stability while effectively avoiding overfitting through learning curve analysis. This model achieved a testing accuracy of 92.00% and a cross-validation score of 89.69%, with the Travel Time Index (TTI) and average speed identified as the primary determinants.Conclusion: Spatial visualization demonstrated high consistency between the model's prediction maps and actual field conditions, confirming that this approach is effective as a foundation for an Early Warning System to support data-driven traffic management policies in Bali.
Co-Authors Eka Sahputra Mikhael Setia Budi Rizal Wahyu Pratama Wisnu Aji Sanjaya Yoka Romadani