Insulated Piercing Connector (IPC) Torque Prediction Using Random Forest (RF) Model and Anomaly Detection Using Isolation Forest for Low Voltage Overhead Distribution Networks

Farrah Masyitah Mohd ShuibAsnawi JohariWan Zakiah Wan IsmailSawal HamidAzrul Azlan HamzahMarinah OthmanIrneza IsmailFauzun A SuhaimiAzhar KhalidAhmad Nizar AzharMuhammad 'Azmun Amin Aziz2025-10-082025-10-082025Farrah Masyitah Mohd Shuib, Asnawi Johari, Wan Zakiah Wan Ismail, Sawal Hamid, Azrul Azlan Hamzah, Marinah Othman, Irneza Ismail, Fauzun A Suhaimi, Azhar Khalid, Ahmad Nizar Azhar & Muhammad 'Azmun Amin Aziz. (2025). Insulated Piercing Connector (IPC) Torque Prediction Using Random Forest (RF) Model and Anomaly Detection Using Isolation Forest for Low Voltage Overhead Distribution Networks. Seminar Antarabangsa Islam Dan Sains 2025 (SAIS 2025), 292–307. https://drive.google.com/drive/folders/1SN6JaJp4OjO-505O0GGa06Ro1JnzHAeH3083-872Xhttps://drive.google.com/drive/folders/1SN6JaJp4OjO-505O0GGa06Ro1JnzHAeHhttps://oarep.usim.edu.my/handle/123456789/27710Seminar Antarabangsa Islam dan Sains 2025 (SAIS 2025) / “Transcending Generations: Naqli and Aqli asPillars of Ummah Transformation”. Editor: Siti Rubaini Mat , Umi Hamidaton Mohd Soffian Lee, Nur Ilyana Ismarau Tajuddin, Noorfajri Ismail, Azman Ab Rahman 9 September 2024 Anjuran: Persatuan Kakitangan Akademik Universiti Sains Islam Malaysia (PKAUSIM),Fakulti Syariah dan Undang-Undang (FSU), Institut Fatwa dan Halal (iFFAH), USIMEfficient electrical connections are critical for maintaining the reliability of aerial bundled cable (ABC) systems, where Insulation Piercing Connectors (IPC) play a vital role. This study focuses on predicting torque values in IPC installations using machine learning techniques to enhance mechanical performance and reduce failure risks. Three mechanical tests were conducted, including Shearhead, Continuity, and Body tests, using IPC samples provided by TeknoBumi. The data were analyzed using Random Forest, Decision Tree, and Artificial Neural Network models.Data preprocessing included normalization, and model performance was evaluated using R-squared, Mean Squared Error, Root Mean Squared Error, and Mean Percentage Error. Among the models, Random Forest showed the highest accuracy across all tests. In addition, anomaly detection methods including Isolation Forest, One-Class Support Vector Machine, and Local Outlier Factor were applied to identify abnormal torque values that could indicate potential installation issues or defects. Isolation Forest proved to be the most consistent and reliable method for detecting outliers. The findings of this study demonstrate that integrating AI-based predictive modeling and anomaly detection can support early identification of mechanical inconsistencies, improve IPC installation quality, and enhance the overall reliability of low-voltage overhead distribution systems.en-USInsulation Piercing ConnectorTorque PredictionMachine Learning ModelsAnomaly DetectionLow Voltage Distribution Network.Insulated Piercing Connector (IPC) Torque Prediction Using Random Forest (RF) Model and Anomaly Detection Using Isolation Forest for Low Voltage Overhead Distribution Networkstext::conference output::conference proceedings::conference paper292307