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All Journal Jurnal Simetris International Journal of Science, Engineering and Information Technology
Rifa'i, Emon
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Improving Root Cause Analysis of Production Defect Using AI: A Case Study in an Automotive Manufacturing Plant Najib, Muhammad; Rifa'i, Emon
International Journal of Science, Engineering, and Information Technology Vol 9, No 2 (2025): IJSEIT Volume 09 Issue 02 July 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/ijseit.v9i2.31226

Abstract

In automotive manufacturing, repetitive defects often occur across different time periods, creating a valuable historical dataset containing defect names and their corresponding root causes. Traditionally, identifying the root cause of a production defect relied heavily on human analysis, requiring significant time and on-site inspection. This often led to delayed countermeasures, increased production downtime, and additional issues such as line stops. This study presents an AI-based approach to assist root cause analysis using historical defect data, aiming to reduce the analysis time and improve feedback accuracy. The implementation focused on enabling faster and more accurate identification of root causes by integrating a machine learning model into the factory’s defect recording system (ATPPM, Analisa Tindakan Penanggulangan dan Pencegahan Masalah). The development process involved data preprocessing, model training, and API deployment. The original dataset consisted of 3,128 records, which were cleaned and reduced to 1,449 labeled entries, each annotated with one of 161 unique root cause labels. Eleven machine learning models were evaluated, including Logistic Regression, Random Forest, SVM, and RNN. Initial evaluation using F1-score, precision, and recall showed Logistic Regression achieving the best F1-score of 0.83. Further validation using 5-Fold Cross Validation identified the Support Vector Machine (SVM) as the best-performing model, with an average accuracy of 89.1%. This model was deployed via a Python Flask API and integrated into the existing ATPPM system. The AI-powered system significantly accelerated the root cause analysis process, reducing the average analysis time by 228 minutes. Potential future enhancements involve automating the model’s training process on a regular schedule (weekly or daily), integrating additional data sources including big data and quality management systems, and scaling the current API implementation to multiple production lines for wider impact.
Job Safety Analysis of Confined Space on Fertilizer Division on Seasoning Industries Rifa'i, Emon; Yusron, Rifky Maulana; Kam, Booi
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.15861

Abstract

Confined space activities in fertilizer production present significant risks, including toxic gas exposure, oxygen deficiency, fire and explosion hazards, and physical or ergonomic challenges. This study applies the Job Safety Analysis method to systematically identify hazards, assess risks, and establish effective control measures during the repair and maintenance of the FRP Pond 22000 Kl at the fertilizer division on seasoning industry. The methodology consisted of six structured stages: job selection, task breakdown, hazard identification, risk control, verification, and monitoring. Hazard identification was carried out through field observation, consultation with Health, Safety, and Environment officers, and adherence to national regulatory standards. Results revealed that atmospheric testing prior to entry was essential, with oxygen levels recorded at 20.8% and toxic gases, within safe limits. The JSA identified potential risks at each task stage and applied control measures following the hierarchy of controls, including engineering controls ventilation, and gas detection. While administrative measures section permit towork system, time restrictions, and health certificates. On energy isolation is installation Lock Out Tag Out. The essential is mandatory of personal protection equipment. Collaborative inspections by contractors, supervisors, and Health, Safety, and Environment staff reinforced compliance and improved communication. The study concludes that job safety analysis is not only a compliance requirement but also a practical framework for hazard anticipation, safety culture reinforcement, and operational continuity. It is recommended that future practice integrates digital job safety analysis systems, real-time monitoring, and enhanced training to strengthen confined space safety in fertilizer production and other high-risk industries.
Co-Authors Kam, Booi Muhammad Najib Rifky Maulana Yusron