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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JTAM (Jurnal Teori dan Aplikasi Matematika)
Yeni Rahkmawati
Department of Statistics, Universitas Lambung Mangkurat
Mathematics

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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, Maulana Malik Ibrahim State Islamic University of Malang

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

Abstract

The price of Indonesian crude oil (ICP) is highly volatile due to fluctuations in global demand, energy policies, and geopolitical tensions, making accurate forecasting challenging. This study compares three forecasting models: ARIMA, Random Forest, and Hybrid ARIMA–Random Forest. The models are evaluated using Time-Series Cross-Validation (TSCV) with MAPE, sMAPE, and RMSE as performance metrics. The results indicate that the Hybrid ARIMA–Random Forest model achieves the lowest MAPE and sMAPE, while Random Forest attains the lowest RMSE, and ARIMA exhibits the highest forecast errors. Diebold–Mariano (DM) tests confirm that ARIMA’s predictive accuracy is significantly lower than both machine-learning-based models, whereas no significant difference is found between Random Forest and the hybrid model. Out-of-sample forecasts for January–June 2026 show relatively stable price movements within 59–63 USD per barrel, with short-term fluctuations reflected in wide prediction intervals. These findings suggest that Indonesian crude oil prices contain both linear and non-linear components, which are effectively captured by the hybrid approach. Overall, the Hybrid ARIMA–Random Forest model provides the most accurate forecasts in percentage-based metrics, offering a robust and reliable tool for policymakers, investors, and market participants navigating volatile oil markets.
Analysis of Food Inflation in Indonesia using the Nonlinear Autoregressive Distributed Lag Approach Nur Salsabila; Yeni Rahkmawati; Agus Muslim; Mizan Ikhlasul Rahman
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.35352

Abstract

Food Inflation remains one of the most persistent sources of price volatility in Indonesia and poses a significant challenge for macroeconomic stability and household welfare. This study conducts quantitative empirical time series research to examine the asymmetric effects of Money Supply (M2) and Farmers Terms of Trade (FTT) on Food Inflation. The analysis uses monthly data from 2011 to 2023 obtained from Bank Indonesia and Statistics Indonesia and applies the Nonlinear Autoregressive Distributed Lag (NARDL) model, which is appropriate for capturing asymmetry and accommodating variables integrated at different orders. The selection of M2 is based on monetary theory which states that changes in liquidity influence aggregate demand and inflation, while the use of FTT is supported by agricultural and development literature showing that farmers purchasing power affects food production capacity and food price dynamics. The results reveal significant asymmetric effects in both the short and long run. Increases and decreases in M2 both raise Food Inflation, and the stronger effect during declining M2 reflects downward price rigidity and the dominance of quasi money in Indonesia. A decline in FTT significantly increases long run inflation through constraints on agricultural input access and reduced food supply. The findings also confirm inflation persistence. These results imply that liquidity management and policies that strengthen farmer purchasing power are essential to stabilize food prices. The study recommends integrating monetary policy with agricultural support measures to mitigate future food inflation pressures.
Co-Authors Agus Muslim Emeylia Safitri Hardianti Hafid Mizan Ikhlasul Rahman Nur Salsabila Nuramaliyah, Nuramaliyah Selvi Annisa