Implementation of a Web-Based Expert System for Machine Damage Diagnosis Using Backward Chaining and Certainty Factor
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Abstract
PT Hardo Soloplast is a manufacturing company that produces plastic products using various advanced machinery. A recurring issue in the production process is the sudden malfunction of machines, which disrupts operations and increases repair costs. To address this, this study proposes a web-based expert system for machine fault diagnosis using the Backward Chaining reasoning method and Certainty Factor approach. The system is implemented using PHP, HTML, CSS, and JavaScript, and stores knowledge in JSON format. It is accessible via web browser for field technicians. The system covers five main machines: Extruder Starex 1500, Laminating HL-2000, Printing Roto-Gravure, Slitting Rewinder RS-3000, and Blown Film Extrusion Machine. The knowledge base consists of 65 rules and symptoms, collected from interviews and documentation. Backward Chaining was chosen for its efficiency in goal-driven reasoning, while Certainty Factor is applied using the formula CFcombine = CF1 + CF2 × (1 − CF1), with a threshold of 0.75 for reliable results.Testing was conducted by comparing system diagnoses with actual technician assessments, achieving accuracy between 75% and 92.58% . This system contributes to the digitalisation efforts at PT Hardo Soloplast by accelerating diagnosis, improving maintenance response time, and reducing dependence on manual fault identification.
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References
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