Evaluasi Komparatif Forecasting PNBP BLU Berbasis SARIMAX terhadap Target Renstra - Roberto; Abdul Hamid Arribathi (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
This study evaluates forecasting methods for predicting BLU X non-tax state revenue and examining its alignment with the 2025-2029 strategic plan targets. The dataset consists of monthly PNBP transactions from January 2021 to April 2026 aggregated by month, Chart of Account (COA), and department. Four approaches are compared: Holt-Winters, SARIMAX, XGBoost, and bottom-up forecasting based on dominant COA groups. Model performance is assessed on the January 2025-April 2026 test period using Mean Absolute Error, Root Mean Squared Error, and Mean Absolute Percentage Error. The results indicate that SARIMAX(1,1,1)x(0,0,0,0) performs best with a MAPE of 17.97%. The selected model is then refitted using the complete historical dataset and applied to forecast revenue through December 2029. The comparison with strategic plan targets reveals a narrow gap in 2025-2026, while 2027-2029 projections are slightly above the target. These findings show that forecasting can support evidence-based budgeting, target realism assessment, and monitoring of dominant revenue services.