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, 2026)
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.