ANALISIS PERBANDINGAN METODE ARIMA DAN LEAST SQUARE UNTUK PREDIKSI HARGA EMAS : PENDEKATAN PROBABILISTIK DAN STATISTIK

  • Dita Anggelia Universitas Katolik Darma Cendika
  • Yosefina Finsensia Riti Universitas Katolik Darma Cendika
  • Paulus William Siswanto Universitas Katolik Darma Cendika
Keywords: ARIMA, Gold Prices, Least Square, Market Forecasting, Predictive Analysis

Abstract

Gold, a precious metal renowned for its value in various sectors, including investment and jewelry, is often considered a secure asset within investment portfolios. However, its prices exhibit high volatility influenced by economic, geopolitical, and global financial factors. Previous research has focused on predictive methods to anticipate gold price movements. In recent years, heightened complexity and uncertainty, exacerbated by global factors such as economic shifts and the Covid-19 pandemic, emphasizes the urgency of accurate gold price predictions. This study comprehensively analyzes and compares the performance of Autoregressive Integrated Moving Average (ARIMA) and Ordinary Least Squares (OLS) in forecasting gold prices, utilizing statistical and probabilistic approaches. ARIMA excels in handling time series data, identifying complex patterns, and forecasting price changes based on historical trends. Conversely, OLS, a probabilistic method, stands out in adjusting linear models to gold price data, providing detailed insights into influencing factors. The research employs a 5-year gold price dataset (2018-2023) and evaluates the models' performance using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Results indicate OLS outperforms ARIMA, with lower MSE (45.79 vs. 284.83) and MAPE (0.0026 vs. 0.0066). This study contributes nuanced insights for market participants, investors, and researchers to comprehend commodity market behaviour, particularly in gold, emphasizing the importance of accurate prediction methods in strategic decision-making.

References

Aizzah, Z., Intan, P. K., & Utami, W. D. (2022). Prediksi Jumlah Gempa Tektonik di Wilayah Jawa Timur dengan Menggunakan Metode ARIMA Box Jenkins dan Kalman Filter. JRST (Jurnal Riset Sains Dan Teknologi), 5(2), 111. https://doi.org/10.30595/jrst.v5i2.9701

Erfina, A., & Al-shufi, M. F. (2022). Analisis Sentimen Aplikasi Jasa Kurir Di Play Store Menggunakan Algoritma Naive Bayes. Jurnal Sistem Informasi Dan Informatika (Simika), 5(2), 103–110. https://doi.org/10.47080/simika.v5i2.1789

Ghulam, B., Shidiq, A., Furqon, M. T., & Muflikhah, L. (2022). Prediksi Harga Beras menggunakan Metode Least Square. Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(3), 1149–1154.

Guntur, M., Santony, J., & Yuhandri, Y. (2018). Prediksi Harga Emas dengan Menggunakan Metode Naïve Bayes dalam Investasi untuk Meminimalisasi Resiko. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 2(1), 354–360. https://doi.org/10.29207/resti.v2i1.276

Lubis, D. R. P., & Zufria, I. (2023). Perbandingan Metode ARIMA Box-Jenkins dengan Moving Avarage Untuk Peramalan Harga Emas. Jurnal Media Informatika Budidarma, 7(4), 1930–1942. https://doi.org/10.30865/mib.v7i4.6897

Maulana Fauzi, R., & Iskandar Mulyana, D. (2021). Implementasi Data Mining Menggunakan Metode Least Square untuk Memprediksi Penjualan Lampu LED pada PT. Sumber Dinamika Solusitama. Jurnal Sosial Teknologi, 1(8), 907–919. https://doi.org/10.59188/jurnalsostech.v1i8.182

Ristianto, F., Nurmalasari, N., & Yoraeni, A. (2021). Impementasi Metode Naive Bayes Untuk Prediksi Harga Emas. Computer Science (CO-SCIENCE), 1(1), 62–71. https://doi.org/10.31294/coscience.v1i1.201

Sismi, & Darsyah, M. Y. (2018). Perbandingan Prediksi Harga Saham PT.BRI, Tbk dengan METODE ARIMA dan MOVING AVERAGE. Prosiding Seminar Nasional Mahasiswa Unimus, 1(1), 351–360. http://prosiding.unimus.ac.id/index.php/mahasiswa/article/view/170

Sunariadi, N. M., Intan, P. K., Novitasari, D. C. R., & Hariningsih, Y. (2022). Prediksi Produksi Bawang Merah Di Kabupaten Nganjuk Dengan Metode Seasonal Arima (Sarima). Transformasi : Jurnal Pendidikan Matematika Dan Matematika, 6(1), 49–60. https://doi.org/10.36526/tr.v6i1.1672

Ulinnuha, N., & Farida, Y. (2018). Prediksi Cuaca Kota Surabaya Menggunakan Autoregressive Integrated Moving Average (Arima) Box Jenkins dan Kalman Filter. Jurnal Matematika “MANTIK,” 4(1), 59–67. https://doi.org/10.15642/mantik.2018.4.1.59-67
Published
2024-03-13
How to Cite
Anggelia, D., Riti, Y., & Siswanto, P. (2024). ANALISIS PERBANDINGAN METODE ARIMA DAN LEAST SQUARE UNTUK PREDIKSI HARGA EMAS : PENDEKATAN PROBABILISTIK DAN STATISTIK. Jurnal Sistem Informasi Dan Informatika (Simika), 7(1), 95-103. https://doi.org/10.47080/simika.v7i1.3197