Article Sidebar

Submitted
Mar 23, 2025
Published
Oct 17, 2025
Download
PDF
Statistic
Read Counter : 1 Download : 4

Main Article Content

Abstract

Perkembangan ekonomi dan pola pikir masyarakat telah menjadikan investasi sebagai salah satu usaha untuk mempersiapkan perekonomian di masa depan.  Investasi emas merupakan salah satu alternatif investasi yang sederhana. Ketika nilai dolar turun, harga emas cenderung akan meningkat sehingga di tengah ketidakstabilan ekonomi global permintaan emas akan naik. Penelitian ini bertujuan untuk membandingkan model ARIMA (Autoregressive Integrated Moving Average) dan Holt’s Double Exponential Smoothing untuk peramalan harga emas Indonesia 2025. Data yang digunakan merupakan harga beli emas di Indonesia dari periode 1 Januari 2024 hingga 31 Desember 2024.  Setelah dilakukan perbandingan, nilai Holt’s Double Exponential Smoothing memiliki nilai RMSE, MAE, dan MAPE yang lebih kecil dari Model ARIMA (0,1,3) sehingga model tersebut memiliki akurasi peramalan yang lebih tinggi dibandingkan model ARIMA.

Keywords

Investasi, Emas, ARIMA, Holt’s Double Exponential Smoothing

Article Details

References

  1. [1] Andriyanto, T. (2017). Sistem Peramalan Harga Emas Antam Menggunakan Double Exponential Smoothing. Jurnal INTENSIF, 1(1).
  2. [2] Asyarofah, W., Vidiati, C., Selasi, D., Gen, G., & Teknologi, F. (2023). Bridging: Journal Of Islamic Digital Economic and Management Pemahaman Berinvestasi Melalui Financial Technology (Fintech) Pada Generasi Gen Z. 1(1), 164–171. https://journal.alshobar.or.id/index.php/bridging
  3. [3] Bandyopadhyay, G. (2016). Gold Price Forecasting Using ARIMA Model. Journal of Advanced Management Science, 117–121. https://doi.org/10.12720/joams.4.2.117-121.
  4. [4] Boucher, C., & Tokpavi, S. (2019). Stocks and Bonds: Flight-to-Safety for Ever? https://doi.org/10.13140/RG.2.2.11184.61446.
  5. [5] Bouoiyour, J., Selmi, R., & Wohar, M. E. (n.d.). Measuring the response of gold prices to uncertainty: An analysis beyond the mean.
  6. [6] Chang, X. (2024). Gold Price Inflation and Its Implications for Financial Markets: Strategies for Mitigation. Advances in Economics, Management and Political Sciences, 119(1), 189–194. https://doi.org/10.54254/2754-1169/119/20242530.
  7. [7] Dewi, D. M., Nafi’, M. Z., & Nasrudin, N. (2022). Analisis Peramalan Harga Emas Di Indonesia Pada Masa Pandemi Covid-19 Untuk Investasi. Jurnal Litbang Sukowati : Media Penelitian Dan Pengembangan, 5(2), 38–50. https://doi.org/10.32630/sukowati.v5i2.235.
  8. [8] Dharmawan, P. A. S., & Indradewi, I. G. A. A. D. (2021). Double exponential smoothing brown method towards sales forecasting system with a linear and non-stationary data trend. Journal of Physics: Conference Series, 1810(1). https://doi.org/10.1088/1742-6596/1810/1/012026.
  9. [9] Fauziah, A., & Surya, M. E. (n.d.). Peluang Investasi Emas Jangka Panjang Melalui Produk Pembiayaan Bsm Cicil Emas (Studi Pada Bank Syariah Mandiri K.C. Purwokerto).
  10. [10] H. Abdelati, M., & Hilal A. Abdelwali. (2024). Optimizing Simple Exponential Smoothing for Time Series Forecasting in Supply Chain Management. Indonesian Journal of Innovation and Applied Sciences (IJIAS), 4(3), 247–256. https://doi.org/10.47540/ijias.v4i3.1591.
  11. [11] Hansun, S., & Subanar. (2016). H-WEMA: A New Approach of Double Exponential Smoothing Method. Telkomnika (Telecommunication Computing Electronics and Control), 14(2), 772–777. https://doi.org/10.12928/telkomnika.v14i2.3096).
  12. [12] Heradhyaksa, B. (2022). Jurnal Hukum Ekonomi Islam (JHEI) Implementasi Investasi Emas Syariah Perspektif Hukum Islam ABST RAK. Jurnal Hukum Ekonomi Islam (JHEI), 6(1), 35–51. www.jhei.appheisi.or.id.
  13. [13] Hilal, Y. N., Nainggolan, G. D. A., Syahputri, S. H., & Kartiasih, F. (2024). Comparison Of Arima And Lstm Methods In Predicting Jakarta Sea Level. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 16(2), 163–178. https://doi.org/10.29244/jitkt.v16i2.52818.
  14. [14] Indah Lestari, & Panjaitan, P. D. (2019). Faktor - Faktor Yang Mempengaruhi Permintaan Tabungan Emas Di Pt. Pegadaian (Persero) Kota Pematangsiantar. Jurnal Ekuilnomi, 1(1), 17–25. https://doi.org/10.36985/ekuilnomi.v1i1.255
  15. [15] Johari, M. (n.d.). TAFAQQUH: Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah. https://www.merdeka.com/uang/ini-perbedaan-krisis-ekonomi-1998-2008-dan-2013-versi-bi.html,
  16. [16] Kahraman, E., & Akay, O. (2023). Comparison of exponential smoothing methods in forecasting global prices of main metals. Mineral Economics, 36(3), 427–435. https://doi.org/10.1007/s13563-022-00354-y
  17. [17] Karadas, K., Celik, S., Eyduran, E., & Hopoglu, S. (n.d.). Forecasting Production Of Some Oil Seed Crops In Turkey Using Exponential Smoothing Methods. In The J. Anim. Plant Sci (Vol. 27, Issue 5).
  18. [18] Lee, Y. W., Tay, K. G., & Choy, Y. Y. (2018). Forecasting Electricity Consumption Using Time Series Model. In International Journal of Engineering & Technology. www.sciencepubco.com/index.php/IJET
  19. [19] Madonna Yuma, F. (2018). Sistem Peramalan Harga Emas Menggunakan Metode Single Exponential Smoothing. Seminar Nasional Royal (SENAR) 2018online) Kisaran, Asahan.
  20. [20] Makala, D., & Li, Z. (2021). Prediction of gold price with ARIMA and SVM. Journal of Physics: Conference Series, 1767(1). https://doi.org/10.1088/1742-6596/1767/1/012022.
  21. [21] Mirdan Faris, R., Kurniaji, K., Budiman, D., Wijaya Kusuma, M., & Lestari, F. (2024). Analisis Peramalan Produksi Tanaman Kelapa Sawit Menggunakan Metode Arima pada PTPN Kebun Sukamaju Article Info ABSTRAK. In Jurnal Bisnis dan Manajemen West Science (Vol. 3, Issue 03).
  22. [22] Osman Gülseven, A. (2016). Uluslararası Ekonomik Araştırmalar Dergisi. In Eylül (Issue 2).
  23. [23] Putro, B., Tanzil Furqon, M., & Wijoyo, S. H. (2018). Prediksi Jumlah Kebutuhan Pemakaian Air Menggunakan Metode Exponential Smoothing (Studi Kasus : PDAM Kota Malang) (Vol. 2, Issue 11). http://j-ptiik.ub.ac.id
  24. [24] Rady, E. H. A., Fawzy, H., & Fattah, A. M. A. (2021). Time series forecasting using tree based methods. Journal of Statistics Applications and Probability, 10(1), 229–244. https://doi.org/10.18576/JSAP/100121
  25. [25] Rizky Aditama, R., Nurkhin Jurusan Pendidikan Ekonomi, A., Ekonomi, F., & Negeri Semarang, U. (2020). Business and Accounting Education Journal Pengaruh Pelatihan Pasar Modal Terhadap Minat Investasi Mahasiswa Di Pasar Modal Dengan Pengetahuan Investasi Dan Manfaat Investasi Sebagai Variabel Intervening. http://journal.unnes.ac.id/sju/index.php/baej
  26. [26] Rizkya, I., Syahputri, K., Sari, R. M., Siregar, I., & Utaminingrum, J. (2019). Autoregressive Integrated Moving Average (ARIMA) Model of Forecast Demand in Distribution Centre. IOP Conference Series: Materials Science and Engineering, 598(1). https://doi.org/10.1088/1757-899X/598/1/012071
  27. [27] Rosita, Y. D., & Moonlight, L. S. (2024). Perbandingan Metode Prediksi untuk Nilai Jual USD: Holt-Winters, Holt’s, dan Single Exponential Smoothing. JTIM : Jurnal Teknologi Informasi Dan Multimedia, 5(4), 322–333. https://doi.org/10.35746/jtim.v5i4.473
  28. [28] Salmah, & Kartini Harahap. (2023). The Effect of Knowledge Investment, Perceived Return and Risk and Technological Advances on Investment Interest of Medan City Students in the Capital Market. Journal of Finance and Business Digital, 2(3), 415–428. https://doi.org/10.55927/jfbd.v2i3.6217
  29. [29] Siagian, R. R. A.-A. (2025). Persepsi Masyarakat Indonesia Terhadap Kenaikan Harga Emas Sebagai Instrumen Investasi Jangka Panjang: Sebuah Tinjauan Literatur. Future Academia : The Journal of Multidisciplinary Research on Scientific and Advanced, 3(1), 72–79. https://doi.org/10.61579/future.v3i1.298
  30. [30] Soebianto, A. (2014). E-Journal Graduate Unpar Part A: Economics E-Journal Graduate Unpar Part A: Economics. E-Journal Graduate Unpar, 1(1), 14-37.
  31. [31] Tripathy, N. (2017). Forecasting Gold Price with Auto Regressive Integrated Moving Average Model International Journal of Economics and Financial Issues Forecasting Gold Price with Auto Regressive Integrated Moving Average Model. International Journal of Economics and Financial Issues, 7(4), 324–329. http:www.econjournals.com
  32. [32] Trivedi, U. B., Vats, T., Somvanshi, S., & Prasad, S. (n.d.). Gold prices prediction: Comparative study of multiple forecasting models. 21, 2022. http://ymerdigital.com
  33. [33] Yonar, H., Yonar, A., Tekindal, M. A., & Tekindal, M. (2020). Modeling and Forecasting for the number of cases of the COVID-19 pandemic with the Curve Estimation Models, the Box-Jenkins and Exponential Smoothing Methods. Eurasian Journal of Medicine and Oncology, 4(2), 160–165. https://doi.org/10.14744/ejmo.2020.28273