<![CDATA[The increasing adoption of Internet of Things (IoT) technology in smart home environments generates large volumes of dynamic and heterogeneous multi-sensor data, creating challenges in accurately detecting anomalous conditions. This study aims to implement and evaluate the Random Forest algorithm for anomaly detection in IoT-based smart home systems using multi-sensor data. The dataset consists of temperature, humidity, light, motion, carbon monoxide (CO), liquefied petroleum gas (LPG), smoke, door/window status, and energy consumption collected from three IoT devices. Data preprocessing included cleaning, labeling, and Min-Max Scaling normalization, followed by an 80:20 training-testing split. Model performance was evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The experimental results achieved an accuracy of 96.75%, precision of 94.65%, recall of 93.82%, and F1-score of 94.23%, demonstrating that Random Forest is effective for identifying anomalous conditions in smart home IoT environments.]]>
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