Penggunaan ARIMA Box-Jenskin dalam Meramalkan Harga Emas Antam Tahun 2025-2027 di Indonesia

  • Sangidatus Sholiha Universitas Muhammadiyah Metro
  • Wardhani Utami Dewi Universitas Muhammadiyah Metro
Keywords: ARIMA, emas antam, peramalan

Abstract

Penelitian ini sangat penting mengingat volatilitas pasar global dan ketidakpastian ekonomi yang semakin meningkat, yang mendorong kebutuhan untuk memiliki alat peramalan yang andal bagi aset-aset lindung nilai seperti emas. Penelitian ini bertujuan untuk meramalkan harga emas Antam di Indonesia untuk periode 2025-2027 menggunakan model ARIMA. Metode kuantitatif dengan desain deret waktu digunakan, dengan data harga emas dari tahun 2021 hingga 2024. Hasil analisis menunjukkan bahwa model ARIMA (1,1,1) adalah yang terbaik dalam meramalkan harga emas Antam, dengan nilai MSE, AIC, dan BIC yang rendah. Peramalan menunjukkan tren kenaikan harga emas dari awal 2025 hingga akhir 2027, mencerminkan kepercayaan pasar terhadap emas sebagai aset lindung nilai yang aman. Kesimpulan dari penelitian ini adalah bahwa peramalan harga emas Antam dapat memberikan wawasan yang penting bagi investor dan pembuat kebijakan untuk merencanakan strategi investasi dan langkah-langkah ekonomi di masa depan.

 

This research is especially important given the increasing global market volatility and economic uncertainty, which drives the need to have reliable forecasting tools for hedging assets such as gold. This research aims to predict the price of Antam gold in Indonesia for the 2025-2027 period using the ARIMA model. A quantitative method with a time series design was used, with gold price data from 2021 to 2024. The analysis results show that the ARIMA (1,1,1) model is the best in estimating Antam's gold price, with MSE, AIC and BIC values ​​that are low . Forecasts show an upward trend in gold prices from the beginning of 2025 to the end of 2027, reflecting market confidence in gold as a safe hedging asset. The conclusion of this research is that Antam's gold price forecasting can provide important insights for investors and policy makers to plan investment strategies and economic steps in the future.

References

Adineh, A. H., Narimani, Z., & Satapathy, S. C. (2020). Importance of data preprocessing in time series prediction using SARIMA: A case study. International Journal of Knowledge-Based and Intelligent Engineering Systems, 24(4), 331–342. https://doi.org/10.3233/KES-200065

Baur, D. G., & McDermott, T. K. (2020). Why is gold a safe haven? Journal of Behavioral and Experimental Finance, 27, 100438.

Ding, J., Tarokh, V., & Yang, Y. (2018). Model selection techniques: An overview. IEEE Signal Processing Magazine, 35(6), 16–34. https://doi.org/10.1109/MSP.2018.2867638

Hartono, S., & Nugroho, Y. (2020). Gold investment as a safe haven during economic uncertainty. Global Finance Journal, 30(2), 198-215.

Indrawati, A., & Santoso, D. (2023). Market sentiment and geopolitical impacts on gold prices. Journal of Financial Economics, 45(2), 123-145.

Jung, Y. (2018). Multiple predicting K-fold cross-validation for model selection. Journal of Nonparametric Statistics, 30(1), 197–215. https://doi.org/10.1080/10485252.2017.1404598

Li, Y., & Umair, M. (2023). The protective nature of gold during times of oil price volatility: an analysis of the COVID-19 pandemic. The Extractive Industries and Society, 15, 101284. https://doi.org/10.1016/j.exis.2023.101284

Liu, Y., Wu, H., Wang, J., & Long, M. (2022). Non-stationary transformers: Exploring the stationarity in time series forecasting. Advances in Neural Information Processing Systems, 35, 9881–9893.

Nugraha, R., & Putri, S. (2024). Technical analysis in forecasting short-term and long-term gold price trends. International Journal of Financial Studies, 52(3), 312-328.

Patel, K., & Desai, R. (2021). Analyzing the impact of macroeconomic factors on gold prices. Journal of Economics and Business Research, 26(2), 35-50.

Putri, A. R. S. E., & Prajawati, M. I. (2024). Analysis of the Accuracy Level of the Balance Model in Stock Investment Prediction in the LQ45 Index. Journal of Accounting Research, Organization and Economics, 7(1), 77–96.

Rahman, M. A., & Khan, M. S. (2023). Long-term investment potential of gold: A comprehensive study. International Journal of Financial Studies, 11(1), 65-79.

Rahman, H., & Taufik, A. (2021). The influence of global monetary policy and exchange rates on gold price volatility in Indonesia. Asian Economic and Financial Review, 11(4), 567-584.

Rhif, M., Ben Abbes, A., Farah, I. R., Martínez, B., & Sang, Y. (2019). Wavelet transform application for/in non-stationary time-series analysis: A review. Applied Sciences, 9(7), 1345.

https://doi.org/10.3390/app9071345

Sikora, M. (2004). Data cleaning and transformation-the first stage of data mining process. Studia Informatica, 25(2), 127-136.

Singh, S., Parmar, K. S., Kumar, J., & Makkhan, S. J. S. (2020). Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19. Chaos, Solitons & Fractals, 135, 109866. https://doi.org/10.1016/j.chaos.2020.109866

Tiwari, R., & Shukla, S. (2022). ARIMA modeling for forecasting commodity prices: A case study of gold prices. Journal of Economic Research, 30(4), 411-425.

Triki, M. B., & Maatoug, A. Ben. (2021). The GOLD market as a safe haven against the stock market uncertainty: Evidence from geopolitical risk. Resources Policy, 70, 101872. https://doi.org/10.1016/j.resourpol.2020.101872

Widjaja, P., & Prasetyo, B. (2022). Interest rates and domestic gold demand: An empirical study. Journal of Economic Policy Research, 38(3), 267-289.

Xiao, D., & Su, J. (2022). Research on stock price time series prediction based on deep learning and autoregressive integrated moving average. Scientific Programming, 2022(1), 4758698. https://doi.org/10.1155/2022/4758698

Published
2024-06-24
How to Cite
Sholiha, S., & Wardhani Utami Dewi. (2024). Penggunaan ARIMA Box-Jenskin dalam Meramalkan Harga Emas Antam Tahun 2025-2027 di Indonesia. Sciencestatistics: Journal of Statistics, Probability, and Its Application, 2(2), 59-69. https://doi.org/10.24127/sciencestatistics.v2i2.5958
Section
Articles