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All Journal Malcom: Indonesian Journal of Machine Learning and Computer Science
Hutagalung, Naek Parulian
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Computer Science & IT

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Cluster-Based Random Forest Regression for Internet of Things Bandwidth Prediction Using Leakage-Free Features Hutagalung, Naek Parulian; Tutuko, Bambang; Zarkasi, Ahmad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2894

Abstract

Heterogeneous Internet of Things (IoT) deployments generate highly variable network traffic, yet most existing bandwidth management approaches rely on static provisioning or device-agnostic models that fail to capture per-device traffic heterogeneity. This study proposes a hybrid machine learning framework combining K-Means clustering and Random Forest regression to enable cluster-aware, leakage-free bandwidth consumption prediction for IoT environments. Using 994,145 flow records from the UNSW HomeNet dataset, the Log1p transformation was applied to address heavy-tailed traffic distributions, and direct bandwidth-derived features were strictly excluded to prevent data leakage. Devices were segmented into three traffic profiles (Low, Medium, and High), validated by a Silhouette Score of 0.4153 and a Davies–Bouldin Index of 1.0357. Within the structural constraints of this single-dataset, controlled setting, Random Forest regression achieved R² values above 0.99 across all clusters, with stable cross-validated performance, substantially outperforming Linear Regression, which achieved R² values as low as 0.0374 in the Medium cluster. Feature importance analysis revealed that Inter-Arrival Time is the primary driver of bandwidth consumption, superseding raw payload volume, with direct implications for temporally-driven feature design. These results indicate that cluster-aware, leakage-free models offer a viable architecture for IoT bandwidth management, with cross-domain generalization as a primary direction for future work.
Co-Authors Ahmad Zarkasi Bambang Tutuko