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All Journal Journal of Informatics and Communication Technology (JICT) Internet of Things and Artificial Intelligence Journal Jurnal Masyarakat Madani Indonesia Akademika
Nindy Raisa Hanum
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Public Health Social Sciences

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Journal : Akademika

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ANALISIS PERBANDINGAN MODEL GRU DAN LSTM UNTUK PREDIKSI HARGA SAHAM BANK RAKYAT INDONESIA: Deep Learning, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), Stock Price Prediction Perdana, Yogi; Raisa Hanum, Nindy; Rabiula, Andre; Anzari, Yandi
JURNAL AKADEMIKA Vol 17 No 2 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i2.1692

Abstract

This research implements and compares two deep learning architectures, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for predicting the stock price of Bank Rakyat Indonesia (BRI) using historical data from February 2023 to October 2024. Through systematic hyperparameter tuning and comprehensive evaluation, the study finds that GRU consistently outperforms LSTM across all regression metrics, with a 10.7% improvement in R² and an 18.5% reduction in MAPE. The optimal GRU configuration (100 units, 100 epochs, batch size 32, learning rate 0.001) achieves an MSE of 6517.5 and MAPE of 1.3764%. Visual analysis confirms GRU's superior ability to capture stock price fluctuations and adapt more quickly to trend changes. The simpler architecture of GRU with fewer parameters proves more effective for handling the high-noise characteristics and varying volatility of stock price data. While both models face challenges in predicting extreme market events, GRU demonstrates better resilience and faster recovery after such occurrences. This research contributes to the understanding of recurrent neural network applications in financial time series forecasting and provides practical insights for developing more accurate stock price prediction systems.
PEMODELAN PREDIKTIF TRAFIK WEBSITE BERDASARKAN VOLUME KONTEN: PENDEKATAN REGRESI: Web performance, content strategy, linear regression model, page view analysis, digital content optimization Hasanatul Iftitah; Nindy Raisa Hanum
JURNAL AKADEMIKA Vol 17 No 2 (2025): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v17i2.1694

Abstract

In today's digital landscape, a website's performance serves as a key metric of an institution’s online presence and communication strategy. This research focuses on forecasting website performance by analyzing the relationship between the number of published articles and the volume of page views using a simple linear regression approach. Monthly data was obtained from the official website of the Faculty of Science and Technology at Universitas Jambi, comprising content publication frequency and corresponding traffic. The analysis reveals a strong positive correlation, where each additional published article contributes to a notable increase in page views. The regression model yields a coefficient of 103.75 with an R² value of 0.7278, indicating that over 72% of traffic variation is attributable to content volume. These results emphasize the importance of consistent content production in enhancing web visibility and provide valuable insights for content strategy development.
Co-Authors Abda Abda Akhiyar Waladi Andre Rabiula Ashar, Rahmad Edi Saputra Hasanatul Iftitah Perdana, Yogi Ulfa Khaira, Ulfa Yandi Anzari