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All Journal Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Information System for Educators and Professionals : Journal of Information System JIEET (Journal of Information Engineering and Educational Technology) Jurnal Sisfokom (Sistem Informasi dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset Informatika INTI Nusa Mandiri Jurnal Infortech Computer Science (CO-SCIENCE) JUSTIAN - Jurnal Sistem Informasi Akuntansi J-SAKTI (Jurnal Sains Komputer dan Informatika) Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Abdimas Komunikasi dan Bahasa TECHSI - Jurnal Teknik Informatika Jurnal Insan Indonesian Journal Computer Science (ijcs) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Pengabdian Kreatif Cemerlang Indonesia Bianglala Informatika
Yesni Malau
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Pemodelan dan Prediksi Harga Emas Menggunakan Metode ARIMA pada Data Time Series Yesni Malau; Eni Pudjiarti; Fintri Indriyani; Riswandi Ishak; Asep Sayfulloh; Wahyutama Fitri Hidayat
Bianglala Informatika Vol. 14 No. 1 (2026): Maret 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v14i1.12455

Abstract

This study aims to analyze and predict gold price movements using a time series approach with the Autoregressive Integrated Moving Average (ARIMA) model. The data used in this research are historical daily gold closing prices from 2020 to 2026 obtained from Investing.com, consisting of 1,568 data. The research stages include data collection, preprocessing, stationarity testing using the Augmented Dickey-Fuller (ADF) test, parameter identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis, parameter estimation, diagnostic checking, and model accuracy evaluation. The results indicate that the data are stationary with a p-value < 0.05. Based on the identification and model selection process, the ARIMA (3,0,3) model was identified as the best model with an Akaike Information Criterion (AIC) value of 15449.326. Model evaluation results show an RMSE of 120.86, MAE of 95.02, and MAPE of 5.48%. The MAPE value below 10% indicates that the model has good accuracy in predicting gold prices. Therefore, the ARIMA model can be used as an effective approach to predict gold price movements based on historical data.
Co-Authors Adi Surya Putra Ari Ariyanto Adi Surya Putra Ari Ariyanto - STMIK Nusa Mandiri Ahmad Setiadi Asep Sayfulloh Eka Wulansari Fridayanthie, Eka Wulansari Eni Pudjiarti Fintri Indriyani Harsih Rianto Hasan Basri Julianto, Muhammad Fahmi Kartika Handayani khairul rizal Maulida, Ulfa Mochammad Imron Awalludin Mohammad Syamsul Azis Muhammad Fahmi Julianto Oky Irnawati Pradita Pusfitaningrum Pratama, Aldi Hermawan Pusfitaningrum, Pradita Rachmat Adi Purnama Riswandi Ishak Rizki, Achmad Guntur Al Sri Watmah Syaiful Anwar Syaputra, Syahiena Maulana Tati Mardiana, Tati Tubagus Aom Somadiningrat D.W.K Wahyutama Fitri Hidayat Wawan Nugroho Yoseph Tajul Arifin