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All Journal INOVTEK Polbeng - Seri Informatika
I Made Gede Sunarya
Pendidikan Ganesha University
Computer Science & IT

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Random Forest and LightGBM Comparison for Acute Pain Diagnosis Using SMOTE on an Expert-Labeled Dataset Wayan Andre Pratama; I Made Gede Sunarya; Putu Hendra Suputra
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/x2getn56

Abstract

Limited healthcare personnel may delay early pain assessment and encourage self-medication, increasing medication error risk. However, evidence remains limited regarding whether bagging or boosting is more suitable for multiclass acute pain classification using imbalanced, expert-system-derived symptom data, and whether SMOTE improves performance. This study compared Random Forest as a bagging approach and LightGBM as a boosting approach for classifying nine acute pain diagnostic classes without SMOTE and with SMOTE using k_neighbours=1 and 5. The dataset comprised 2,722 records and 36 discrete symptom features. Of 125 representative symptom combinations reviewed by a medical expert, 115 were considered appropriate; the remaining records were synthetically generated using the same expert-system knowledge base and inference mechanism. Data were divided using stratified 80:20 sampling, while model configuration was evaluated using five-fold cross-validation. SMOTE was applied only to training data within each fold. LightGBM without SMOTE achieved the best performance, with 83.49% accuracy, a macro F1-score of 0.81, and a weighted F1-score of 0.83, compared with 80.18%, 0.77, and 0.80 for Random Forest. With SMOTE, Random Forest achieved 78.35% and 77.61% accuracy, while LightGBM achieved 81.10% and 82.39%. Thus, LightGBM without SMOTE performed best for this dataset. Validation using real clinical data and multiple experts is required.
Performance and User Satisfaction Analysis of Kerobokan Village’s SIPANDU Using IT Balanced Scorecard and EUCS Putu Ade Pranata; Gede Indrawan; I Made Gede Sunarya
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/g3w4mf09

Abstract

Information technology advancements push government agencies to utilise information systems to improve public services. Kerobokan Regional Office has utilised the Complaint Management Information System (SIPANDU) to manage citizen complaints digitally. However, a comprehensive evaluation aligning operational technical performance, organisational contribution, future readiness, and the psychological satisfaction of users has not been conducted. This study evaluates SIPANDU's overall performance and user satisfaction using the integration of the IT Balanced Scorecard (IT BSC) as a macro evaluation framework and End User Computing Satisfaction (EUCS) as a micro instrument focused on the user orientation perspective. This mixed-methods study involved 15 internal end users (village head, secretary, section heads, and neighbourhood heads) selected through saturated sampling. EUCS measures satisfaction based on content, accuracy, format, ease of use, and timeliness. The evaluation results show SIPANDU's performance is highly satisfactory across all IT BSC perspectives with an overall score of 0.918 (91.8%). Corporate Contribution scored 0.900, User Orientation scored 0.964, Operational Excellence scored 0.892, and Future Orientation scored 0.908. User satisfaction measured by EUCS is also very high, averaging 4.82 out of 5. Despite the excellent results, the integration of IT BSC and EUCS revealed gaps primarily in operational excellence and timeliness. Priorities for improvement and the strategic roadmap developed include enhancing system reliability, optimising server performance, and implementing real-time notification features for complaint status updates.  
Co-Authors Gede Indrawan Putu Ade Pranata Putu Hendra Suputra Wayan Andre Pratama