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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Coreid Journal
Eva Nurlatifah
UIN Sunan Gunung Djati Bandung
Computer Science & IT Education

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Comparison of Long Short-Term Memory and Recurrent Neural Network For Stock Market Price Movement Classification in Islamic Bank Finance Rijki Rijki; Yana Aditia Gerhana; Gitarja Sandi; Muhammad Deden Firdaus; Eva Nurlatifah
CoreID Journal Vol. 4 No. 1 (2026): March 2026
Publisher : CV. Generasi Intelektual Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60005/coreid.v4i1.152

Abstract

This study addresses the importance of accurate stock price prediction in the Islamic finance sector, where reliable forecasting supports better investment decisions and market stability. Despite the growing use of deep learning methods, comparative studies on sequential models in this domain remain limited. Therefore, this research compares the performance of Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) models for classifying stock price movement direction of Islamic banks in Indonesia. The dataset was sourced from two Islamic banks in Indonesia, covering the period from 2022 to mid-2024, with features such as Open, High, Low, Close, Adjusted Close, and Volume. The CRISP-DM method was applied for data processing, and testing was performed with data splits of 60:40, 70:30, and 80:20, as well as epoch variations (30, 50, 80). Results indicate that RNN outperforms LSTM, with the highest accuracy of 58% for RNN and 53% for LSTM. Evaluation metrics also included precision, recall, and F1-score. In conclusion, RNN performs better for stock movement classification direction, while LSTM is more effective for minimizing prediction error.
Klasifikasi Pengenalan Huruf Hijaiyah Pada Bahasa Isyarat Arab Menggunakan Transfer Learning EfficientNetB1 Diani Eka Putri; Jumadi Jumadi; Eva Nurlatifah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6174

Abstract

Individu Muslim dengan gangguan pendengaran sering menghadapi tantangan dalam mempelajari Al-Qur'an, terutama karena kurangnya sistem pendukung untuk pembelajaran bahasa isyarat Arab. Bahasa isyarat memainkan peran penting dalam memfasilitasi komunikasi yang efektif bagi mereka yang mengalami kesulitan pendengaran. Untuk mengatasi masalah ini, penelitian ini bertujuan untuk mengembangkan sistem pengenalan Bahasa Isyarat Arab (ArSL) dengan menggunakan Convolutional Neural Network (CNN) dan pendekatan Transfer Learning menggunakan model pre-trained EfficientNetB1. Sistem ini dirancang untuk mengidentifikasi gerakan bahasa isyarat Arab dari dataset yang terdiri dari 28 huruf Hijaiyah, dengan masing-masing huruf memiliki 100 citra. Pengujian dilakukan pada model dengan empat skenario berbeda untuk menemukan konfigurasi yang paling optimal. Hasil penelitian menunjukkan bahwa dengan membekukan 30% lapisan awal model selama fine-tuning menghasilkan akurasi 98.95% pada data train dan 99.52% pada data validation. Pada data pengujian, model mencapai performa terbaik dengan accuracy, precision, recall, dan f1-score masing-masing sebesar 100%.
Co-Authors Diani Eka Putri Gitarja Sandi Jumadi Jumadi Muhammad Deden Firdaus Rijki Rijki Yana Aditia Gerhana