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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi JURNAL DIAN MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal Pilar Nusa Mandiri JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat TEPIAN Jurnal Informatika: Jurnal Pengembangan IT
MY. Teguh Sulistyono
Universitas Dian Nuswantoro
Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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Comparison of Sarima and Exponential Smoothing Methods in Forecasting Exchange Rates for Farmers in Central Java Province Sulistyono, MY Teguh; Annabil, Muhammad Naufal
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11396

Abstract

This study compares the performance of the SARIMA and Exponential Smoothing (Holt-Winters) models in forecasting the Farmer Exchange Rate (NTP) for Central Java Province from 2016 to 2025. The monthly statistical data used was obtained from the Central Java Provincial Statistics Agency. The models were evaluated using MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) on test data for the period January 2016 to September 2025, while forecasting was carried out from October 2025 to December 2027. The results show that the SARIMA (1,1,1) (1,1,1,12) model has an MAE of 6.94 and an RMSE of 7.88, indicating that the model can make accurate predictions with few errors. However, the Exponential Smoothing model has a lower MAE and RMSE, implying that this model is more accurate in forecasting long-term NTP. Both models show comparable seasonal trends, with Exponential Smoothing being more stable and sensitive to seasonal changes.  This study also proposes the use of alternative forecasting approaches, such as ARIMAX, VAR, or machine learning to improve the accuracy of future forecasts.  The results of this study can be used to develop agricultural policies that maintain food price stability, improve farmer welfare, and predict future inflation fluctuations.
Comparison of Multiple Linear Regression and Random Forest Methods for Predicting National Rice Production in Indonesia Nur Cahyo, Sefrico Aji; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11398

Abstract

Rice is a strategic commodity that plays an important role in maintaining national food security. However, rice production in Indonesia still fluctuates due to variations in harvest area, productivity, climate conditions, and differences in regional characteristics. This condition demands a predictive model capable of providing more accurate production estimates to support food policy planning. This research aims to predict national rice production by comparing two methods: Multiple Linear Regression and Random Forest Regression, using data from the Central Bureau of Statistics (BPS) and Nasa Power for the period 2018–2024. The analysis stages include data preprocessing, data exploration, categorical variable transformation, splitting data into training and testing sets, model training, and evaluation using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The research results show that harvested area is the most dominant factor influencing rice production, followed by productivity, year, and province. Based on the evaluation results, Random Forest provided the best performance with an MAE value of 40,599.94, an RMSE of 77,153.07, and an R² of 0.9991. The low error value and the proximity of the prediction to the actual data indicate that this model is better at capturing non-linear patterns and inter-regional variations compared to Multiple Linear Regression. Overall, Random Forest can be an effective method for predicting national rice production and can be further developed in subsequent research by incorporating climate variables or other external factors.
Co-Authors Achmad Teguh Wibowo Adi, Derry Pramono Agung Widodo Agus Hermanto Agustinus Bimo Gumelar Agustinus Bimo Gumelar Aisyiyah Tri Ratna Dewi Alzami, Farrikh Amartya Anugrah Indrawati Lestari Amodia, Stalina Anggraeny Dewi Annabil, Muhammad Naufal Asih Rohmani, Asih Azzami, Salman Yuris Adila Belinda Elsa Aulia Sinatrya Candra Irawan Dyah Ernawati Edi Sugiarto Fahmi Amiq Fikri Budiman Hadi, Heru Pramono Hendriyanto , Novi Kurnia Desita, Raafi Lukman Junaedi Maulana Zaky Muhammad MG Catur Yuantari Nugroho, Via Ardianto Nur Cahyo, Sefrico Aji Nur Sela Ameiliawati Nurhindarto, Aris Prasetya, Rakan Shafy Putra, Davin Hernanda Richard Emmerig S. Hadiati Nugraini Siti Hadiati Nugraini Sudaryanto Sudaryanto Sudaryanto Sudaryanto Wahibullah, Wahibullah Wellia Shinta Sari