cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Auralia
Contact Email
submissions@ijarlit.org
Phone
-
Journal Mail Official
submissions@ijarlit.org
Editorial Address
Prenggan, Kotagede, Kota Yogyakarta, Daerah Istimewa Yogyakarta 55172, Indonesia
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 2 (2025)" : 5 Documents clear
Prediction of Euro to US Dollar Exchange Rate Using CNN Method with Grid Optimization Al Anshori, Faqihuddin; Pidgeon, S
International Journal Artificial Intelligent and Informatics Vol 3, No 2 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (601.877 KB) | DOI: 10.33292/ijarlit.v3i2.45

Abstract

This research compares the performance of Convolutional Neural Network (CNN) models without optimization and CNN with Grid Search optimization in predicting the Euro exchange rate against the United States Dollar. Data obtained from Yahoo Finance for the period 2018-2023. The results showed that the CNN model with Grid Search optimization provided better performance with an RMSE value of 0.01, MAE of 0.01, MAPE of 0.61%, and R² of 0.8586 on test data, and prediction accuracy reached 99.39%. Grid Search optimization successfully found the best parameters with batch_size 32, dense_units 50, filters 64, kernel_size 3, and learning_rate 0.001. This research proves that hyperparameter optimization can improve the performance of CNN models in predicting currency exchange rates, which can be a decision support tool for foreign exchange market players.
Optimization of GRU Method with Bayesian Optimization for Prediction of South African Rand Exchange Rate against US Dollar Abdallah, Amme; Rahis, Saqib
International Journal Artificial Intelligent and Informatics Vol 3, No 2 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ijarlit.v3i2.46

Abstract

This research compares the performance of the standard Gated Recurrent Unit (GRU) model with GRU optimized using Bayesian Optimization to predict the exchange rate of the South African Rand (ZAR) against the United States Dollar (USD). By utilizing time series data from Yahoo Finance for the period 2018-2023, this research implements a deep learning architecture to capture patterns of currency exchange rate fluctuations. The results show that the GRU model with Bayesian optimization produces better performance on the test data with a MAPE value of 0.81% and R² 0.9352, compared to the standard GRU model with a MAPE of 0.86% and R² 0.9267. Despite the slight decrease in accuracy on the training data, the optimized model has a simpler architecture with a single GRU layer, which indicates better computational efficiency. These findings make a significant contribution to the development of more accurate and efficient currency exchange rate prediction models, particularly for emerging financial markets.
Comparison of LSTM and LSTM with Grid Search Optimization for Stock Price Prediction of Saudi Arabian Oil Company (Aramco) Yilmaz, Eva; Tcrol, Ema
International Journal Artificial Intelligent and Informatics Vol 3, No 2 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.514 KB) | DOI: 10.33292/ijarlit.v3i2.47

Abstract

This study compares the performance of the Long Short-Term Memory (LSTM) model without optimization and LSTM with Grid Search optimization in predicting Saudi Arabian Oil Company (Aramco) stock prices. Using stock price data from December 2019 to December 2023, this study aims to identify a more accurate prediction model. Results show that the LSTM model with Grid Search optimization provides a significant performance improvement compared to the standard LSTM model, with a decrease in Root Mean Square Error (RMSE) of 11.63% on the test data. This finding indicates the importance of hyperparameter optimization in improving the accuracy of stock price prediction models, especially for the world's largest oil company such as Aramco, whose stock price can be affected by various macroeconomic and geopolitical factors.
Performance Comparison of Standard LSTM and LSTM with Random Search Optimization for Spark New Zealand Limited Stock Price Prediction Flavia, Axia; Mio, Camila
International Journal Artificial Intelligent and Informatics Vol 3, No 2 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (708.211 KB) | DOI: 10.33292/ijarlit.v3i2.48

Abstract

This study compares the performance of the standard Long Short-Term Memory (LSTM) model with the LSTM model optimized using the Random Search method to predict the stock price of Spark New Zealand Limited. The data used is historical stock price data from Yahoo Finance for the period 2018-2023. Model evaluation is performed using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), coefficient of determination (R²), and accuracy metrics. The results showed that the standard LSTM model achieved Test RMSE performance of 0.04, Test MAE of 0.03, Test MAPE of 0.73%, Test R² of 0.8571, and Test Accuracy of 99.27%. While the LSTM model with Random Search optimization achieved Test RMSE performance of 0.04, Test MAE of 0.03, Test MAPE of 0.78%, Test R² of 0.8302, and Test Accuracy of 99.22%. Although both models performed very well, the standard LSTM model was slightly superior in some evaluation metrics on the test data. This research provides insight into the effectiveness of hyperparameter optimization in the context of stock price prediction.
Comparison of CNN, CNN-GRU, and GRU Models for Prediction of Hryvnia (Ukraine) Exchange Rate against US Dollar Mostafa, Sanoun
International Journal Artificial Intelligent and Informatics Vol 3, No 2 (2025)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.381 KB) | DOI: 10.33292/ijarlit.v3i2.49

Abstract

This study aims to compare the performance of three neural network-based machine learning models, namely Convolutional Neural Network (CNN), hybrid CNN-Gated Recurrent Unit (CNN-GRU), and Gated Recurrent Unit (GRU) in predicting the exchange rate of the Ukrainian Hryvnia against the United States Dollar. The data used is sourced from Yahoo Finance in the range of 2018 to 2023. The evaluation results show that the CNN-GRU hybrid model provides the best performance with the highest test accuracy of 99.69% and an R² value of 0.6899. The CNN model achieved 98.99% test accuracy but with a negative R² (-1.0343), while the GRU model showed 97.94% test accuracy with a very low R² (-6.3755). This study reveals the advantages of the hybridization approach in modeling financial time series data by combining the feature extraction capabilities of CNN and the sequential modeling capabilities of GRU. These results provide important insights for the development of predictive models for volatile currency markets, especially for emerging economies such as Ukraine.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 3, No 2 (2025) Vol 3, No 1 (2025) Vol 2, No 2 (2024) Vol 2, No 1 (2024) Vol 1, No 2 (2018) Vol 1, No 1 (2018)