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All Journal Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika
Madona Yunita Wijaya
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.
Forecasting indonesia's non-oil and gas exports and imports using the vector autoregressive integrated moving average (VARIMA) model Sifha Desti Wulandari; Madona Yunita Wijaya; Irma Fauziah
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.v9i1.3746

Abstract

Indonesia’s international trade, particularly in the non-oil and gas sector, plays a significant role in the national economy. This study aims to model and forecast Indonesia’s non-oil and gas exports and imports using the Vector Autoregressive Integrated Moving Average (VARIMA) model, a multivariate time series method that considers dynamic interdependence between variables. The data used in this study were monthly data from January 2018 to December 2024, consisting of 84 observations divided into 58 training data and 26 testing data. Model order identification was carried out using the Matrix Autocorrelation Function (MACF) for the MA component and the Matrix Partial Autocorrelation Function (MPACF) for the AR component. Parameter estimation was conducted using Maximum Likelihood Estimation (MLE) and refined through a restriction process to retain only statistically significant parameters. Diagnostic tests showed that the residuals met the assumptions of white noise and multivariate normality. Among several candidate models, VARIMA(2,1,2) was selected as the optimal model based on the balance between information criteria, model complexity, parameter stability, and forecasting reliability. The model produced MAPE values of 11.10% for imports, categorized as good, and 8.81% for exports, categorized as very good. The forecast results showed relatively stable fluctuations, with imports projected to range from US$14.32 million to US$18.48 million and exports from US$18.38 million to US$26.41 million. These findings indicate that the VARIMA(2,1,2) model provides adequate forecasting performance and can serve as a quantitative basis for supporting foreign trade policy planning, particularly in anticipating the dynamics of Indonesia’s non-oil and gas exports and imports.
Co-Authors Irma Fauziah Mahmudi Nayla Saadah Fiddaraeni Sifha Desti Wulandari