Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Aiti: Jurnal Teknologi Informasi
Didik Nugroho
Universitas Stikubank
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Pengembangan model prediksi saham perbankan Indonesia dengan optimasi algoritma Long Short-Term Memory (LSTM) Didik Nugroho; Eka Ardhianto
AITI Vol 23 No 2 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i2.231-244

Abstract

Banking stocks have attracted public attention because they are often perceived as relatively stable and capable of reflecting the current economic conditions. However, in reality, banking stocks are quite volatile for various reasons. Investors need an accurate stock prediction model to make investment decisions and reduce risk. This study proposes a stock prediction model based on the Long Short-Term Memory (LSTM) deep learning algorithm. The dataset consists of ten years of historical stock market data from banking companies listed on the Indonesia Stock Exchange (IDX), covering the period from 2014 to 2023. LSTM was chosen because it is known to be effective at predicting time-series data and can capture long-term dependencies and patterns. The results of this model were optimized using the Adam optimizer and hyperparameter tuning. These findings suggest that the proposed approach can serve as a reliable tool for stock price forecasting and support more informed investment strategies in the banking sector.
Co-Authors Eka Ardhianto