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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 41 Documents
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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.
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.
Effect of Plyometric Training on Smash Performance in Extracurricular Volleyball Students at SMP Negeri 3 Sembawa Riski Dwi Putra; Farizal Imansyah; Puput Sekar Sari
International Journal Artificial Intelligent and Informatics Vol. 4 No. 3 (2026): August 2026
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (457.954 KB) | DOI: 10.33292/m3yhsn59

Abstract

This research examines the effect of plyometric training on smash performance improvement in extracurricular volleyball students at SMP Negeri 3 Sembawa. Low smash ability marked by insufficient hitting power, directional inaccuracy, and attack ineffectiveness is the main problem in school extracurricular coaching. The research method used a quantitative experiment with a pre-experimental one-group pretest-posttest design. The sample consisted of 10 male students selected through purposive sampling with criteria of age 11–15 years and active participation in volleyball extracurricular activities for a minimum of six months. The treatment was a plyometric training program consisting of basic box jump, burpee, and depth jump for 16 sessions with a frequency of four times per week. Measurement instruments used a volleyball smash skill test with scoring technique toward target areas. Data analysis included Shapiro-Wilk normality test and paired sample t-test hypothesis testing using IBM SPSS 26. The results showed an increase in the average value from 9.80 (pretest) to 14.00 (posttest) with a percentage increase of 42.85%. The paired sample t-test obtained a value of t = −31.500, p = 0.000 (< 0.05), with Cohen's d = 2.91 effect size classified as very large. The conclusion indicates that plyometric training has a significant effect on improving smash performance in extracurricular volleyball students at SMP Negeri 3 Sembawa. This finding provides a recommendation for coaches and physical education teachers to integrate plyometric training programs systematically into smash technique training design.
The Impact of Digitalization and the Quality of the P3-TGAI Monitoring Application’s Information System on the Effectiveness and Accountability of Irrigation Management at the Sulawesi V Mamuju River Basin Office Fitriani; Naluria Islami; Hari Yeni; Muhammad Aqil
International Journal Artificial Intelligent and Informatics Vol. 4 No. 3 (2026): August 2026
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (423.524 KB) | DOI: 10.33292/8bfccp21

Abstract

The digitalization of water resources management is a strategic necessity to accelerate the monitoring process, enhance information accuracy, and increase the transparency of government program implementation. This study aims to analyze the effect of digitalization and the quality of the monitoring application information system for the Irrigation Water Use Acceleration Improvement Program (P3-TGAI) on the effectiveness and accountability of irrigation management at the Sulawesi V Mamuju River Basin Office. The study used a quantitative approach with an explanatory design. Data were collected through a Likert-scale questionnaire to 60 respondents consisting of BWS technical staff, P3A technical assistants, administrators of water user farmer organizations, and structural officials who use the application. Data were analyzed using Structural Equation Modeling–Partial Least Squares with the help of SmartPLS 4.0. The results showed that digitalization had a positive and significant effect on effectiveness with a path coefficient of 0.341, a t-statistic of 3.214, and a p-value of 0.001. Digitalization also had a significant effect on accountability with a coefficient of 0.289, a t-statistic of 2.876, and a p-value of 0.004. Information system quality has the strongest influence on effectiveness with a coefficient of 0.412, a t-statistic of 4.508, and a p-value <0.001, and an impact on accountability with a coefficient of 0.377, a t-statistic of 3.962, and a p-value <0.001. Digitalization and information system quality explain 64.2% of the variation in effectiveness and 58.7% of the variation in accountability. This study concludes that an integrated, stable, easy-to-use monitoring application that produces accurate information can improve program coordination, control, reporting, transparency, and accountability. Strengthening digital infrastructure, user competency, system security, and data validation should be a priority in realizing effective, accountable, and sustainable P3-TGAI governance.
The Influence of Information System Quality and Perceived Cybersecurity on Students' Continued Usage, with Trust as a Mediating Variable Muh. Rivaldi; Irwan Syarifuddin; Hari Yeni; Muhammad Aqil
International Journal Artificial Intelligent and Informatics Vol. 4 No. 3 (2026): August 2026
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (670.829 KB) | DOI: 10.33292/wghfwv62

Abstract

Digital transformation in higher education requires the availability of academic information systems that are not only high quality but also capable of protecting students' digital data and activities. This study aims to analyze the influence of information system quality and cybersecurity perceptions on student continuance usage, both directly and indirectly through trust as a mediating variable. The study used a quantitative approach with a survey design on 216 active students of the Muhammadiyah Polman Institute of Technology and Business who have used the Academic Information System. Respondents were selected using a purposive sampling technique. Data were collected through a five-point Likert-scale questionnaire and analyzed using Partial Least Squares–Structural Equation Modeling with the help of SmartPLS 4. The results showed that information system quality had a positive and significant effect on trust (β=0.339; t=6.325; p<0.001) and continuance usage (β=0.222; t=3.903; p<0.001). Cybersecurity perceptions also had a positive and significant effect on trust (β=0.387; t=6.174; p<0.001) and continuance usage (β=0.275; t=5.528; p<0.001). Trust exerted the strongest influence on continuance usage (β=0.427; t=7.745; p<0.001). Furthermore, trust was shown to partially mediate the effect of information system quality on continuance usage, with an indirect effect of 0.145 and a VAF of 39.4%, and the effect of cybersecurity perceptions on continuance usage, with an indirect effect of 0.165 and a VAF of 37.6%. This study concluded that the continued use of academic information systems is determined by technical quality, cybersecurity assurance, and especially student trust. Universities need to improve system reliability, data protection, security transparency, and service responsiveness to maintain continued system use.

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