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All Journal Prosiding Seminar Nasional Official Statistics
Utih Amartiwi
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Analisis Perbandingan Model Arima, RNN, dan N-BEATS pada Prediksi Harga Emas Ainul Mardhiyyah; Utih Amartiwi
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2637

Abstract

Gold is a strategic investment instrument that serves as a hedge against inflation, although its price movements are characterized by high volatility and complex patterns. This study develops and compares the performance of the conventional statistical approach, Autoregressive Integrated Moving Average (ARIMA), and the deep learning approach, Recurrent Neural Network (RNN) and Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in forecasting the weekly average gold prices in Jakarta during the 2022–2024 period. The two methods are evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (sMAPE) metrics. The results indicate that N-BEATS outperformed ARIMA in forecasting performance. This is attributed to its basis expansion architecture, which effectively captures nonlinear dynamics and adapts to extreme volatility, making it more accurate and robust than RNN and ARIMA.
Peramalan Harga Minyak Mentah Menggunakan Kombinasi Model ARIMA dan Machine Learning pada Konflik Iran–Amerika Serikat Tahun 2026 Titi Pramudita Wijayanti; Nabilla Andini Putri; Sekar Afifa Cettastami; Utih Amartiwi
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2686

Abstract

As an energy source, crude oil price forecasting is crucial for a country's policy decisions. However, crude oil price volatility is influenced by many factors, one of which is geopolitical conditions. During the Iran-United States conflict, which led Iran to close the Strait of Hormuz, an international oil distribution route, oil prices reached a spike that could not be considered by univariate time series models. This study proposes a framework that combines the Autoregressive Integrated Moving Average (ARIMA) model with a machine learning algorithm. ARIMA is used to predict the dollar index and divided into two models: normal conditions and wartime conditions. Furthermore, the Decision Tree, Random Forests, and Extreme Gradient Boosting (XGBoost) machine learning models are used to predict oil prices based on historical information on oil prices and the dollar index, as well as the closure and opening status of distribution routes. The results show that this integration is capable of predicting oil prices with MAE less than 10 in this geopolitics situation.
Co-Authors Ainul Mardhiyyah Nabilla Andini Putri Sekar Afifa Cettastami Titi Pramudita Wijayanti