Analisis Prediksi Rasio Elektrifikasi Rumah Tangga Indonesia Menggunakan Algoritma Prophet Pendekatan Logistic Growth
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1237Keywords:
Electrification; Forecasting; Logistic Growth; Prophet; Time Series; IndonesiaAbstract
The Indonesian government continues striving for 100% household electricity access, yet regional disparities remain significant, particularly in eastern regions and New Autonomous Regions. Methodologically, forecasting the electrification ratio faces the challenge of data scarcity and the need to keep predictions below the absolute 100% ceiling. Conventional models and standard non-linear approaches such as penalized Logistic Regression have limitations in handling very small univariate time series and often fail to capture trends without a dynamic saturation point. This study therefore proposes the Prophet algorithm with a Logistic Growth approach to forecast the electrification ratio across 38 provinces for the 2026–2030 period. Prophet was selected for its robustness to minimal historical data and missing values, while Logistic Growth sets a logical maximum capacity (cap = 100.5%) so that predictions do not exceed the 100% asymptotic limit. The evaluation results show the model performs with precision in regions with mature historical data, evidenced by a MAPE of 0.41% and RMSE of 0.62 in DKI Jakarta. Conversely, predictions for DOB provinces such as Central Papua show high uncertainty, with errors reaching 34.30% due to inadequate initial data ranges. Projections through 2030 confirm that all provinces on Java remain stable at a 100.00% ratio, while an anomaly is detected in Southwest Papua, which is projected to decline sharply to 34.65%. The main contribution of this study is the first Prophet-Logistic Growth forecasting framework applied to 38 Indonesian provinces. This approach offers a mathematically stable forecasting framework as a basis for government decision-making on energy infrastructure allocation, particularly when combined with field-data verification in data-scarce regions.
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