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All Journal Dinamik Jurnal Teknologi Informasi dan Ilmu Komputer JOIN (Jurnal Online Informatika) JMM (Jurnal Masyarakat Mandiri) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Madaniya Jurnal Teknik Informatika (JUTIF) Bulletin of Information Technology (BIT) Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Pelita Pengabdian Jurnal Ilmu Komputer dan Informatika TECHNOMEDIA : Informatics and Computer Science
Antika Zahrotul Kamalia
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Journal : TECHNOMEDIA : Informatics and Computer Science

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Pemodelan Prediksi Harga Saham Emas ANTAM Menggunakan Gated Recurrent Unit dan Regresi Linear Berganda pada Time Series Kamalia, Antika Zahrotul; Wahyu Tri Utami; Arif Susilo
Tekompedia : Jurnal Ilmiah Ilmu Komputer Vol 3 No 1 (2026): Januari
Publisher : CV Nature Creative Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/technomedia.v3i1.185

Abstract

This study investigates the prediction of PT Aneka Tambang Tbk (ANTM) stock prices using time-series data by comparing two approaches: the Gated Recurrent Unit (GRU) model and multiple linear regression. The dataset consists of daily historical ANTM data collected from Yahoo Finance spanning 2014–2024, which was preprocessed (including cleaning/normalization) and split chronologically into training (70%) and testing (30%) sets to preserve realistic forecasting conditions. Model performance was assessed using R-squared (R²), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), supported by visual comparisons between actual and predicted values. The results indicate that the GRU model achieves superior predictive performance and better captures the dynamic and non-linear behavior of stock price movements compared to multiple linear regression. These findings suggest that GRU is more suitable for ANTM stock price forecasting in a time-series setting, while multiple linear regression remains useful as a simple and interpretable baseline model.
Co-Authors Agus Suwarno Agus Suwarno, Agus Ahmad Amrullahalhafizh Ahmad Turmudi Zy Amali Amali Amali, Amali Ananto Tri Sasongko Andi Al Zaroni Andriani Arief Wibowo Arif Susilo Aswan Supriyadi Sunge Buana, Aliman Fijar Choiriyatun Nisa Latansa Dedi Afandi Deni Mahdiana Edy Widodo Elvan Aan Pradana Hemdani Rahendra Herlianto Ikhsan Romli Imawan, Alifia Putri Ismamudi, Ismamudi Khalis Sofi Mahesa Rezky Ramadhan Miftah Wangsadanureja Nawangsih, Ismasari Noval Adi Iryanto Noval Adi Iryanto Nurhadi Surojudin Nursinta Afrianti Permana, Agung Nabawi Prameswari R, Shanti Prasetyo, Dwi Indra Qoriah Ar Raudhatul Hasanah Rahendra Herlianto, Hemdani Rangga Purnama Riady, Sasmitoh Rahmad Rijal Akmaludin Rijal Akmaludin Rozikin, Zaenur Septianto, Riski Setiyadi Wicaksono Subarkah, Asuy Suderajat, Agung Sulaeman, Asep Arwan Wahyu Tri Utami Widya, Adi Rusdi Wiyarno Wiyarno Wiyarno, Wiyarno Yefta Ondi Spalanzani Simatupang Zaenur Rozikin Zaenur Rozikin Zulfa, Hikmatul