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Journal : Journal of Embedded Systems, Security and Intelligent Systems

POST TRAINING QUANTIZATION IN LENET-5 ALGORITHM FOR EFFICIENT INFERENCE Dary Mochamad Rifqie; Dewi Fatmarani Surianto; Nurul Mukhlisah Abdal; Wahyu Hidayat M; Hartini Ramli
Journal of Embedded Systems, Security and Intelligent Systems Vol 3, No 1 (2022): May 2022
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ketika model jaringan saraf tiruan menjadi lebih baik , keinginan untuk mengimplementasikannya di dunia nyata semakin meningkat. Namun, konsumsi energi dan akurasi jaringan saraf tiruan sangat besar karena ukuran dan kompleksitasnya, sehingga sulit untuk diimplementasikan pada embedded devices. Kuantisasi jaringan saraf ini adalah sebuah teknik untuk dapat memecahkan masalah seperti mengurangi ukuran dan kompleksitas jaringan saraf tiruan dengan mengurangi ketepatan parameter dan aktivasi. Dengan jaringan yang lebih kecil, dimungkinkan untuk menjalankan jaringan saraf di lokasi yang diinginkan. Artikel ini mengkaji tentang kuantisasi yang telah berkembang dalam beberapa dekade terakhir. Dalam penelitian ini, kami mengimplementasikan kuantisasi dalam algoritma lenet-5, yang merupakan algoritma jaringan saraf convolutional pertama yang pernah ada, dan dievaluasi dalam dataset MNIST dan Fashion-MNIST.
LSTM Based Stock Price Forecasting Using RSI and MACD: A Case Study On BBRI Nurul Mukhlisah Abdal; Ridwan Daud Mahande; Maya Sari Wahyuni; Andi Muhammad Amil Siddik
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8518

Abstract

The high volatility and non-linearity of stock market data present significant challenges in forecasting price movements. This study aims to develop an accurate predictive model for the daily closing price of PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) using a Long Short-Term Memory (LSTM) neural network. The primary objective is to enhance predictive accuracy by incorporating technical indicators—Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD)—into the model architecture. Historical stock data from 2010 to 2024 were collected from Yahoo Finance. The dataset was preprocessed through data cleaning, feature engineering, normalization, and time-based splitting into training and testing sets. The LSTM model was trained using the Mean Squared Error (MSE) loss function and optimized with the Adam optimizer. Model performance was evaluated based on key metrics including MSE, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that the model performs effectively, with training RMSE of 73.61 and testing RMSE of 92.47. These findings demonstrate that the LSTM model, enriched with RSI and MACD indicators, is capable of capturing temporal patterns in stock prices and generating reliable forecasts. The study contributes to the growing body of literature on deep learning applications in financial forecasting, and offers practical insights for investors and analysts in understanding market behavior and supporting data-driven decision-making.