Data Mining Framework for EDC Terminal Repair Protocol: Combining Apriori and PrefixSpan

Electronic Data Capture (EDC) terminals are vital for financial transactions, but their repair processes often lack standardization, causing inefficiencies. Data mining techniques like Association Rule Mining (ARM) and Sequential Pattern Mining (SPM) can extract hidden patterns from service logs to inform maintenance strategies. This research addresses the limited use of these techniques within Electronic Data Capture (EDC) repair centers. Specifically, it applies Association Rule Mining (ARM) using the Apriori algorithm, and Sequential Pattern Mining (SPM) using the PrefixSpan algorithm, to optimize repair protocols based on historical repair data from PT. XYZ Indonesia. The study aimed to discover frequent fault-action-component associations and repair sequences to formulate standardized procedures. A quantitative case study analyzed 56,629 repair transactions. After data cleaning and transformation, Apriori (evaluated by support, confidence, lift) mined association rules, while PrefixSpan found frequent sequential patterns (evaluated by minimum support). Several high-confidence rules emerged: "Battery Not Charging" almost always led to "Replace Battery Pack" (≈95% confidence, lift ≈6.0), and error "2000000" (tamper indication) strongly correlated with detampering procedures and internal battery replacement (≈96% confidence, lift ≈4.9). PrefixSpan uncovered consistent repair sequences, including length-3 patterns for complex issues, with "Replace CMOS → Reinstall OS" for error "7FFFFF" being a prominent shorter sequence. Integrating these data-driven patterns into protocols and aligning inventory can improve service efficiency, reduce repair time, and enhance EDC reliability.