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All Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Febiani Putri Ardhana
Universitas Mulawarman
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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Forecasting Indonesian Nickel Reference Mineral Prices Using a Hybrid Intervention-Neural Network Model Febiani Putri Ardhana; Meiliyani Siringoringo; Sri Wahyuningsih; Desi Yuniarti; Erlyne Nadhilah Widyaningrum
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 02 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm501

Abstract

Time series data influenced by external events often exhibit abrupt changes and nonlinear patterns, making them difficult to model using a single forecasting approach. Hybrid models have emerged as an alternative by combining the strengths of linear and nonlinear models to improve forecasting accuracy. One approach that can be applied is the hybrid Intervention–Neural Network (NN) model. This study aims to obtain the best hybrid Intervention–NN model for forecasting Indonesian Nickel Reference Mineral Prices (Harga Mineral Acuan/HMA) using monthly data from January 2018 to February 2025, which were affected by the Russia–Ukraine conflict in February 2022. Modeling was conducted using intervention analysis with a step function combined with NN models consisting of 1, 2, 3, and 4 hidden neurons. The results indicate that the optimal model is the hybrid intervention ARIMA(1,1,0) step function model combined with a two-neuron NN, yielding an RMSE value of 1,074.3652 and a MAPE value of 4.7735%. Forecasts for the period from March 2025 to February 2026 indicate a downward trend in nickel prices.
Co-Authors Desi Yuniarti Erlyne Nadhilah Widyaningrum Meiliyani Siringoringo Sri Wahyuningsih