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All Journal Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika
Mahmudi
Universitas Islam Negeri Syarif Hidayatullah Jakarta, Indonesia
Mathematics

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Forecasting patient visits in primary healthcare using arima, holt's exponential smoothing, prophet, and weighted ensemble models Nayla Saadah Fiddaraeni; Madona Yunita Wijaya; Mahmudi
Jurnal Absis: Jurnal Pendidikan Matematika dan Matematika Vol. 9 No. 1 (2026): Jurnal Absis
Publisher : Program Studi Pendidikan Matematika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/absis.v9i1.3507

Abstract

The variability of patient visits in primary healthcare facilities requires accurate and interpretable forecasting methods to support service planning, resource allocation, and staff scheduling. This study aims to evaluate the forecasting performance of ARIMA, Holt’s exponential smoothing, Prophet, and weighted ensemble models in predicting monthly patient visits at XYZ Primary Clinic in Bogor City. A quantitative time series forecasting approach was applied using monthly patient visit data from January 2020 to December 2023, consisting of 48 observations. The data were divided into 38 training observations and 10 testing observations using an approximately 80:20 split. Model performance was evaluated using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results show that Holt’s exponential smoothing provided the best individual forecasting performance, with the lowest MAPE of 9.39%, RMSE of 169.43, and MAE of 141.60. Among the ensemble configurations, the combination of Holt’s and Prophet produced the best ensemble performance, with a MAPE of 9.56%, RMSE of 171.01, and MAE of 146.11. However, the weighted ensemble models did not outperform the best individual model. These findings indicate that the patient visit data exhibit a relatively stable trend pattern, making Holt’s exponential smoothing more suitable than more complex approaches for this dataset. This study highlights the importance of selecting forecasting models based on data characteristics rather than model complexity alone. The findings provide practical implications for improving patient visit forecasting, resource planning, and service management in primary healthcare settings.
Comparative Performance of SARIMA, Decision Tree, and Hybrid Models for Climate-Informed Rice Production Forecasting Dewi Artha Prescelya; Mahmudi; Nur Inayah
Jurnal Absis: Jurnal Pendidikan Matematika dan Matematika Vol. 9 No. 2 (2026): Jurnal Absis
Publisher : Program Studi Pendidikan Matematika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/absis.v9i2.4240

Abstract

Rice production is essential to national food security, yet climate-related fluctuations create uncertainty in agricultural planning. This study compared Seasonal Autoregressive Integrated Moving Average (SARIMA), Decision Tree, and hybrid SARIMA--Decision Tree models for forecasting monthly rice production in Bojonegoro, Lamongan, and Ngawi Regencies. The dataset comprised 84 monthly observations for each regency from January 2018 to December 2024, including rice production, rainfall, temperature, and sunshine duration. The procedure included Augmented Dickey--Fuller testing, seasonal decomposition, SARIMA parameter optimization, Decision Tree tuning, and residual correction, in which climate variables and temporal features were used to model SARIMA errors. The dominant predictors were sunshine duration in Bojonegoro and temperature in Lamongan and Ngawi. The hybrid model produced the lowest mean absolute percentage error in all three regencies: 32.23% in Ngawi, 36.76% in Bojonegoro, and 46.18% in Lamongan. Although the hybrid approach consistently improved upon the standalone models, the remaining errors indicate limited absolute forecasting accuracy, particularly in Lamongan. The findings support the residual-based integration of seasonal and non-linear models while highlighting the need for richer, location-specific climate and agricultural data.
Co-Authors Dewi Artha Prescelya Madona Yunita Wijaya Nayla Saadah Fiddaraeni Nur Inayah