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All Journal SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Inferensi Jurnal Darma Agung Quantitative Economics and Management Studies Panrannuangku Jurnal Pengabdian Masyarakat Parameter: Journal of Statistics Journal of Mathematics: Theory and Applications SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam VARIANSI: Journal of Statistics and Its Application on Teaching and Research English Language, Linguistics, and Culture International Journal (ELSTIC-IJ) Ininnawa: Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Dies Natalis Universitas Negeri Makassar Jurnal Pengabdian Masyarakat Indonesia Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) JAMP
Hardianti Hafid
Universitas Negeri Makassar
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Comparison of Farmer Exchange Rate Index Forecasting with Decomposition and Single Exponential Smoothing Method Muthahharah, Isma; Hafid, Hardianti
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5491

Abstract

NTP forecasting is crucial for supporting appropriate policy-making. Therefore, this study aims to address the problem of selecting the most accurate forecasting method for predicting the Farmers’ Terms of Trade Index (FTTI). Specifically, the objective is to compare the accuracy of two time series forecasting methods, namely Decomposition and Single Exponential Smoothing (SES), in forecasting the price index received by food crop farmers for the period 2020 to 2024. Both methods were evaluated using Root Mean Square Error (RMSE) as a measure of forecasting accuracy. The results show that the Decomposition method provides better forecasting accuracy, as indicated by lower RMSE values (RMSE = 1.846) than the SES method, both with α = 0.1 (RMSE = 7.37) and α = 0.6 (RMSE = 3.23). This finding suggests that the Decomposition method is better at capturing seasonal patterns and trends in the FTTI data than the SES method, which tends to produce larger errors. 
Comparison of ARIMA, Random Forest, and Hybrid ARIMA-Random Forest Models in Forecasting Indonesian Crude Oil Prices Yeni Rahkmawati; Selvi Annisa; Hardianti Hafid; Nuramaliyah Nuramaliyah; Emeylia Safitri
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.36540

Abstract

The price of Indonesian crude oil is highly volatile due to global demand fluctuations, energy policies, and geopolitical tensions, making accurate forecasting challenging. This study compares three forecasting models: ARIMA, Random Forest, and Hybrid ARIMA--Random Forest, to identify the most accurate approach. Model performance was evaluated using Time-Series Cross-Validation (TSCV) and metrics including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results indicate that the Random Forest model, tuned with \texttt{mtry = 1} and \texttt{ntree = 200}, outperformed both ARIMA and Hybrid ARIMA--Random Forest, achieving the lowest MAPE, MAE, and RMSE values. This suggests that Indonesian crude oil prices during the study period are predominantly non-linear, and Random Forest alone effectively captures these dynamics. Forecasts based on this model indicate a short-term increase in prices from 61.10 USD/Barrel in December 2025 to 64.29 USD/Barrel in March 2026, followed by a slight decline and modest recovery by June 2026. Overall, Random Forest provides a reliable and accurate tool for forecasting Indonesian crude oil prices, offering valuable insights for policymakers, investors, and market participants navigating volatile oil markets.
Co-Authors Abd. Hafid Abdul Rahman Ahmad Yasir Aras Ahmar, Ansari Saleh Arwini Arisandi Awaluddin Muin Ayu Pebriyanti Bungatang DH, Satriani Emeylia Safitri Fahmuddin S., Muhammad Fahmuddin, Muhammad Fahmuddin, Muhammad Hafid, Abd. Haryono, Andri Herdawati, Herdawati Isma Muthahharah Mar'ah, Zakiyah Mar’ah, Zakiyah Meliyana R, S.Pd, M.Si, Sitti Masyitah Meliyana R, Sitti Masyitah Meyliana, Sitti Masyitah Moeh Zainal Khairul Muh. Fahmuddin Muhammad Fahmuddin S Muhammad Isbar Pratama Muhammad Nusrang Muhammad Nusrang Muhammad Yusuf Muhsin Efendi Muthahharah, Isma Muthahharah, Isma Mutiara Nur Agus Salim, Nur Agus Nuramaliyah Nuramaliyah Nusrang, Muhammad Nusrang, Muhammad Rahmanda, Lalu Ramzy Rahmat H. S. Rahmat Hidayat Rais, Zulkifli Reski Wahyu Yanti Rezky Amalia Hamka Rezky, Akhmad Rezky Ramadhana T Risqi, Shopia Rosmalah Rosmalah Rosmalah Rosmalah, Rosmalah Ruliana Rustam, Sitti Nail Satriani DH Selvi Annisa Selvi Annisa Siswahyudianto Sitti Masyitah Meliyana R. Sitti Nailah Rustam Sudarmin Yati Samsyuddin Yeni Rahkmawati Zakiyah Mar'ah Zakiyah Mar’ah Zulkifli Rais