Article Per Year (5 Year)
p-Index From 2021 - 2026
1.819
P-Index
This Author published in this journals
All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika SemanTIK : Teknik Informasi IT JOURNAL RESEARCH AND DEVELOPMENT Techne : Jurnal Ilmiah Elektroteknika Jurnal Sistem Cerdas PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Applied Technology and Computing Science Journal Letters in Information Technology Education (LITE) International Journal of Informatics Engineering and Computing
Ordiyasa, I Wayan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : IT JOURNAL RESEARCH AND DEVELOPMENT

 1 
Enhancing Stock Price Prediction Using Stacked Long Short-Term Memory Diqi, Mohammad; Ordiyasa, I Wayan; Hamzah, Hamzah
IT Journal Research and Development Vol. 8 No. 2 (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/itjrd.2023.13486

Abstract

This research explores the Stacked Long Short-Term Memory (LSTM) model for stock price prediction using a dataset obtained from Yahoo Finance. The main objective is to assess the effectiveness of the model in capturing stock price patterns and making accurate predictions. The dataset consists of stock prices for the top 10 companies listed in the Indonesia Stock Exchange from July 6, 2015, to October 14, 2021. The model is trained and evaluated using metrics such as RMSE, MAE, MAPE, and R2. The average values of these metrics for the predictions indicate promising results, with an average RMSE of 0.00885, average MAE of 0.00800, average MAPE of 0.02496, and an average R2 of 0.9597. These findings suggest that the Stacked LSTM model can effectively capture stock price patterns and make accurate predictions. The research contributes to the field of stock price prediction and highlights the potential of deep learning techniques in financial forecasting.
Co-Authors Dian Rhesa Rahmayanti Elisabeth Deta Lustiyati Evrita Lusiana Utari, Evrita Lusiana Fathinah, Azzah HAFIZAH, IDA Hamzah Hamzah Hamzah Izzurohman, Moh. Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mohammad Diqi Noverianus, Noverianus Putra Wanda, Putra Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sriwidodo B, Sriwidodo Sugeng Winardi Sugiarto, Raden Bagus Nurhadi Wijaya Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Wijaya, Nurhadi Wiratma, Harits Dwi