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International Journal Artificial Intelligent and Informatics
Published by Research and Social Study Institute
ISSN : -     EISSN : 2622626X     DOI : https://doi.org/10.33292
Core Subject : Economy, Science,
Computer Science & IT Economics, Econometrics & Finance
International Journal of Artificial Intelligence and Informatics is a scientific journal dedicated to the exploration of theories, methods, and applications of artificial intelligence in time series analysis, forecasting, and prediction. This journal serves as a platform for researchers, academics, and practitioners to publish their work on predictive models applied to various time-dependent phenomena. Topics within the journal’s scope include, but are not limited to: 1. Predictive Methodologies and Models Deep learning models for forecasting (LSTM, GRU, Transformer, etc.) Machine learning algorithms for time series forecasting (ARIMA, SARIMA, XGBoost, etc.) Optimization of forecasting models using metaheuristic approaches (PSO, GA, etc.) Hybrid models for improving prediction accuracy Statistical methods and Bayesian approaches in forecasting 2. Applications of Time Series and Forecasting Across Various Fields Financial and stock market prediction Weather forecasting and climate change analysis Energy demand forecasting and resource management Time series analysis in healthcare and epidemiology Forecasting in manufacturing and supply chain management User behavior prediction in e-commerce and social media 3. Data and Infrastructure for Forecasting Big data management in time series analysis Streaming data and real-time forecasting Explainable AI (XAI) in predictive models Data augmentation and synthetic data for forecasting The journal welcomes research articles, review papers, and case studies that provide significant contributions to the development of theories and implementation of predictive systems based on artificial intelligence.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 3, No 1 (2025)" : 5 Documents clear
Performance Comparison of Long Short-Term Memory and Convolutional Neural Network for Prediction of Exchange Rate of Indian Rupee against US Dollar Rai, Kovat; Vijayan, Amit
International Journal Artificial Intelligent and Informatics Vol 3, No 1 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (438.98 KB) | DOI: 10.33292/ijarlit.v3i1.41

Abstract

This study compares the effectiveness of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models in predicting the exchange rate of the Indian Rupee (INR) against the United States Dollar (USD). Using historical data from 2017 to 2023 obtained from Yahoo Finance, both models were trained and evaluated based on several performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and accuracy. The results showed that the hybrid LSTM model consistently outperformed the CNN model on all evaluation metrics, with a Test RMSE value of 0.38 compared to 1.32 for CNN. The LSTM model also showed better stability between training and testing performance, indicating better generalization ability and lower risk of overfitting. These findings confirm the superiority of the LSTM architecture in capturing the complex temporal patterns inherent in financial time series data, making it a more reliable option for currency exchange rate prediction.
Comparison of CNN-LSTM Hybrid and CNN Methods for Ethereum (ETH) to US Dollar (USD) Exchange Rate Prediction Regine, Daniel; Zabarnyi, Anatoly
International Journal Artificial Intelligent and Informatics Vol 3, No 1 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (293.243 KB) | DOI: 10.33292/ijarlit.v3i1.42

Abstract

This research compares the effectiveness of the hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) method and the Convolutional Neural Network (CNN) method in predicting the Ethereum (ETH) exchange rate against the United States Dollar (USD). The research uses historical ETH/USD data from Yahoo Finance for the period 2017-2022. Evaluation of the two models was carried out using the performance metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and accuracy rate. The results showed that the CNN-LSTM hybrid model significantly outperformed the CNN model in predicting the ETH/USD exchange rate with a Test RMSE value of 94.67 compared to 129.02 for CNN, as well as an accuracy rate of 96.31% versus 94.89%. These findings contribute to the fintech literature by providing empirical evidence of the superiority of hybrid methods for high volatility cryptocurrency exchange rate prediction.
Comparison of LSTM and GRU Methods for Predicting Gold Exchange Rate against US Dollar Bohovic, Dušan
International Journal Artificial Intelligent and Informatics Vol 3, No 1 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (540.568 KB) | DOI: 10.33292/ijarlit.v3i1.43

Abstract

This study aims to compare the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in predicting the gold exchange rate against the United States Dollar (USD). Using time series data from Yahoo Finance for the period 2017-2023, we evaluate and compare the two models based on comprehensive evaluation metrics. The results show that the GRU model performs better in several important metrics, especially in terms of Root Mean Square Error (RMSE) on the test data (26.41 compared to 27.54 on LSTM) and higher coefficient of determination (R²) on the test data (0.9004 compared to 0.7825 on LSTM). These findings indicate that the GRU model has better generalization ability for gold to USD exchange rate prediction, although both models show very high accuracy rates above 98% on the test data.
Petrobras Stock Price Prediction Using Deep Learning Approach: Performance Comparison of CNN and CNN-GRU Methods Gabrielzinho, Manuel; Moraes, Giovana
International Journal Artificial Intelligent and Informatics Vol 3, No 1 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (647.587 KB) | DOI: 10.33292/ijarlit.v3i1.44

Abstract

This study aims to compare the effectiveness of two deep learning models, namely Convolutional Neural Network (CNN) and combined CNN with Gated Recurrent Unit (CNN-GRU), in predicting Petrobras stock prices. Using historical stock price data from Yahoo Finance for the period 2017-2023, this study evaluates the performance of both models based on several evaluation metrics. The results showed that the CNN-GRU model outperformed the pure CNN model in all evaluation metrics, with an increase in RMSE value of 4.17% and an increase in R² value of 0.47% on the test data. The CNN-GRU model achieved 96.14% accuracy on the test data, while the CNN model achieved 96.04%. These findings indicate that the integration of CNN's feature extraction capabilities with GRU's temporal dependency modeling capabilities can improve stock price prediction accuracy. This research contributes to the computational finance literature by presenting an in-depth comparative analysis of the application of hybrid deep learning architectures in stock market prediction.
Improved Accuracy of Ethereum Exchange Rate Prediction Against USD Using CNN-LSTM Hybrid Model with Bayesian Optimization Tamene, Panom; Chernet, Ghugza
International Journal Artificial Intelligent and Informatics Vol 3, No 1 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ijarlit.v3i1.40

Abstract

This study evaluates the effectiveness of the CNN-LSTM hybrid model in predicting the Ethereum exchange rate against the United States Dollar (USD) by comparing the performance of the model without optimization and the model with hyperparameter optimization using Bayesian Optimization. The dataset used is sourced from Yahoo Finance covering the period 2017-2023. The results show that the CNN-LSTM model with hyperparameter optimization consistently outperforms the model without optimization, with improved prediction accuracy shown through the RMSE, MAE, MAPE, and R² values. Hyperparameter optimization resulted in an optimal configuration with 166 filters, kernel size 5, 168 LSTM units, 91 dense units, learning rate 0.00114, and batch size 32. This research confirms the effectiveness of the CNN-LSTM hybrid approach in predicting crypto exchange rates, and demonstrates the importance of hyperparameter optimization in improving prediction accuracy.

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