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All Journal Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Catur Suharinto
Supporting Services Work Team, Dr. M. Djamil Central General Hospital, Padang, West Sumatera, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Advancing PSA Maturity Level 4 Through a Web-Based PHP–MySQL Predictive Dashboard for Hospital Utilities and Medical Gases Catur Suharinto
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 2 (2026): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i2.329

Abstract

Public hospitals in Indonesia operate under the Public Service Agency (PSA/BLU) governance framework, which requires balanced clinical performance and financial accountability. Indicator 6.2 of the PSA Maturity Rating mandates the transition from fragmented manual reporting toward systematic and predictive digital monitoring to achieve Level 4 (“Predictable”) governance. However, many institutions continue to rely on retrospective reporting systems that impede transparency and data-driven decision-making. This study aims to develop and validate a web-based predictive dashboard to strengthen resource governance at Dr. M. Djamil Central General Hospital (RSUP Dr. M. Djamil Padang), Indonesia. The system integrates four critical resource streams electricity, water, fuel, and medical gases using a bounded Annual Growth Rate (±20%) model combined with a deviation-adjusted hybrid forecasting approach and a Sugeno-type Fuzzy Inference System for priority classification. A two-year longitudinal validation (2024–2025) was conducted using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. The results demonstrate high predictive stability, with a weighted-average MAPE below 10%, and electricity forecasts classified as “Highly Accurate.” Water and Liquid O2 emerged as high-priority operational pressures, while other parameters remained within controlled growth thresholds. The proposed framework operationalizes Indicator 6.2 by institutionalizing a transparent and reproducible predictive monitoring mechanism. This study contributes a scalable digital governance prototype for emerging healthcare institutions seeking to advance toward Predictable maturity while strengthening risk-informed resource allocation.
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