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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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Stock Price Prediction of ReconAfrica (RECAF) Using Gated Recurrent Unit (GRU): Analysis and Implications for Investment Decisions Aníbal, Tomás López; Okanlawon, Rabiu
International Journal Artificial Intelligent and Informatics Vol 2, No 2 (2024)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ijarlit.v2i2.35

Abstract

This study develops a stock price prediction model for ReconAfrica (RECAF) using Gated Recurrent Unit (GRU), an effective deep learning method for capturing temporal and non-linear patterns in stock price data. The model was trained and tested using five years of historical RECAF stock price data and evaluated with metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation results show that the GRU model achieved an MAE of 0.0992, MSE of 0.0397, RMSE of 0.1993, and MAPE of 4.27, indicating a high predictive capability. These findings underscore the potential of the GRU model as a valuable tool for investors and market analysts in making more informed investment decisions. While the results are promising, the study also identifies opportunities for further development through the integration of external data and exploration of other deep learning architectures. Thus, this research contributes significantly to stock market analysis and improved investment strategies.
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.
Forecasting Stock Prices of Taiwan Semiconductor Manufacturing Company (TSMC) Using Recurrent Neural Networks: Evaluating Predictive Performance in a Volatile Market li-We, Lai; Moanu, Koundé
International Journal Artificial Intelligent and Informatics Vol 2, No 2 (2024)
Publisher : Research and Social Study Institute (ReSSI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ijarlit.v2i2.38

Abstract

Accurate stock price prediction plays a critical role in guiding investment strategies, particularly in dynamic industries such as semiconductors, where price volatility is high. This study investigates the effectiveness of Recurrent Neural Networks (RNN) in predicting the stock prices of Taiwan Semiconductor Manufacturing Company (TSMC), a global leader in the semiconductor sector. Using daily closing price data from January 2020 to January 2023, the RNN model was developed and trained to forecast future stock prices. The data was preprocessed with feature scaling to ensure the stability of model training, and a sliding window approach was applied to capture temporal dependencies. The model's predictive performance was evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) as key metrics. The RNN achieved an RMSE of 9.87 and a MAPE of 5.90%, indicating that the model provides reasonable accuracy in forecasting stock prices with a moderate level of deviation from actual values. Visual analysis further demonstrated the model's capacity to capture general trends in the stock price movements, although challenges were noted in predicting highly volatile periods. The study highlights the potential of RNN in financial forecasting while suggesting future improvements, such as incorporating advanced models like Long Short-Term Memory (LSTM) or external factors to enhance predictions during market volatility. These findings offer valuable insights for investors and analysts seeking to leverage machine learning in stock price prediction, particularly in industries characterized by rapid technological advancements and price fluctuations.
The Role of Organizational Digital Transformation in Strengthening Local Cultural Identity and Digital Literacy of the People of Mamuju Regency, West Sulawesi Sukriadi; Hasni; 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 | DOI: 10.33292/y22ag880

Abstract

Digital transformation not only changes organizational service and communication systems but also opens up new opportunities for preserving local culture and increasing community digital capacity. In Mamuju Regency, the use of technology by local governments, universities, cultural communities, and social institutions has not been fully integrated with strategies for strengthening Mandar culture and digital literacy education. This study aims to analyze the influence of organizational digital transformation on local cultural identity and community digital literacy and examine the mediating role of digital literacy in the relationship between organizational digital transformation and local cultural identity. The study used a quantitative approach with an explanatory survey method. Data were collected through a Likert-scale questionnaire from 250 respondents consisting of government officials, lecturers and students, cultural community administrators, and people aged 17–35 who actively use digital media. The sample was determined using proportionate stratified random sampling. Data analysis was carried out through instrument testing and modeling of relationships between variables. The results of the validity test showed that all 24 statements were valid, with corrected item-total correlation values ​​ranging from 0.685–0.792 and higher than the table r value of 0.124. The results of the model testing indicate that organizational digital transformation has a positive and significant impact on digital literacy and local cultural identity. Digital literacy also positively influences local cultural identity and has been shown to mediate the influence of organizational digital transformation on strengthening community cultural identity. These findings emphasize that the digitalization of services, communication, information, and cultural promotion must be accompanied by an increase in community capacity to critically access, evaluate, create, and disseminate digital content. Digital transformation based on participation and local wisdom can be a sustainable strategy for maintaining the existence of Mandar culture amidst technological developments and globalization.
The Effect of Digital Local Wisdom Integration on Sustainable Employee Performance Mediated by Digital Cultural Literacy and Green Work Behavior in Employees of th Akbar; Supardi Abas; 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 | DOI: 10.33292/cz935h51

Abstract

Digital transformation in public organizations requires not only technological mastery, but also alignment with cultural values, work behavior, and sustainability orientation. This study aims to analyze the effect of Digital Local Wisdom Integration on Sustainable Employee Performance with Digital Cultural Literacy and Green Work Behavior as mediating variables in employees of the Mamuju State Treasury Service Office. The study used a quantitative approach with an explanatory research design. All 28 employees were selected as respondents through a saturated sampling technique. Data were collected using a Likert-scale questionnaire and analyzed using Partial Least Squares–Structural Equation Modeling using SmartPLS. The results showed that Digital Local Wisdom Integration has a positive and significant effect on Sustainable Employee Performance with a path coefficient of 0.298, a t-statistic of 2.901, and a p-value of 0.004. This variable also has a significant effect on Digital Cultural Literacy of 0.642 and Green Work Behavior of 0.584. Furthermore, Digital Cultural Literacy has a positive effect on Sustainable Employee Performance with a coefficient of 0.361, while Green Work Behavior has an effect of 0.402. The model explains 71.8% of the variation in Sustainable Employee Performance. Mediation testing shows that Digital Cultural Literacy has an indirect effect of 0.232, while Green Work Behavior has an indirect effect of 0.235, with a p-value of 0.001 for each. Both variables act as complementary partial mediators. These findings confirm that integrating the values ​​of togetherness, responsibility, integrity, and simplicity into digital work systems can strengthen digital cultural literacy, shape green work behavior, and improve employee performance in an efficient, adaptive, and sustainable manner.
The Influence of Content Authenticity and Digital Creativity on Affiliate Marketing Performance through Digital Trust on Facebook Pro User Content Creators Masyitah Febrianti Hamzah; Achmed Ghazali Ar Lery; 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 | DOI: 10.33292/c5c0gz69

Abstract

The development of the creator economy has opened up opportunities for mothers to engage in affiliate marketing through Facebook Professional Mode. However, authenticity and creativity of content do not always result in clicks, sales, and commissions because audience decisions are also influenced by the level of trust in the creator. This study aims to analyze the influence of Content Authenticity and Digital Creativity on Affiliate Marketing Performance, both directly and through Digital Trust as a mediating variable for mom content creators using Facebook Pro. The study used a quantitative approach with an explanatory design. The sample consisted of 205 respondents selected using a purposive sampling technique based on the criteria of being mothers, actively using Facebook Pro, producing content, and having promoted products through affiliate links. Data were collected through an online questionnaire with a Likert scale of 1–5 and analyzed using Structural Equation Modeling based on AMOS version 31. The research model showed a good level of fit, with an RMSEA of 0.000, a CFI of 1.000, a CMIN/DF of 0.900, and an NFI of 0.943. The test results show that Content Authenticity does not have a significant direct effect on Affiliate Marketing Performance (β=0.127; p=0.174). Digital Creativity also does not have a significant direct effect (β=−0.003; p=0.974). Conversely, Content Authenticity (β=0.648; p<0.001) and Digital Creativity (β=0.578; p<0.001) have a positive effect on Digital Trust. Furthermore, Digital Trust has a positive effect on Affiliate Marketing Performance (β=0.657; p<0.001). The Sobel test proves that Digital Trust fully mediates the effect of content authenticity and creativity on affiliate performance. These findings confirm that digital trust is the primary mechanism that transforms content quality into measurable affiliate marketing results.
The Effect of Digital Transformation on Sustainable Employee Performance with Commitment to Change and Green Work Behavior as Mediation Variables in Employees of the Ministry of Finance in West Sulawesi Province Muchammad Syafar; Isra Febriana; 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 | DOI: 10.33292/9mach345

Abstract

Digital transformation in the public sector requires not only the use of technology but also changes in employee attitudes and behavior to maintain sustainable performance. This study aims to analyze the effect of digital transformation on sustainable employee performance with commitment to change and green work behavior as mediating variables among Ministry of Finance employees in West Sulawesi Province. The study used a quantitative approach with an explanatory design and a cross-sectional survey. The study population consisted of 198 employees spread across six vertical agencies of the Ministry of Finance in West Sulawesi Province and all were targeted by a total sampling technique. Data were collected using a five-point Likert scale questionnaire and analyzed using Structural Equation Modeling–Partial Least Squares using SmartPLS. The results showed that digital transformation had a positive and significant effect on sustainable employee performance with a path coefficient of 0.24 and a p-value of 0.032. Digital transformation also had an effect on commitment to change of 0.69 and green work behavior of 0.71, each with a p-value <0.001. Furthermore, commitment to change had a 0.28 effect on sustainable employee performance with a p-value of 0.014, while green work behavior had a 0.35 effect with a p-value of 0.002. The mediation effect through commitment to change reached 0.19 with a p-value of 0.021, while mediation through green work behavior was 0.25 with a p-value of 0.005. The model was able to explain 63% of the variation in sustainable employee performance. The study concluded that digital transformation will be more effective in improving sustainable performance if supported by a commitment to change and efficient and environmentally friendly work behavior.
The Effect of HRIS Information System Service Quality on Employee Work Productivity: The Role of Green Behavior as a Moderation Variable in the Regional Office of the Directorate General of Treasury of West Sulawesi Province Husni; Yuniawan Hari Raharjo; 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 | DOI: 10.33292/xq3jpq64

Abstract

Digital transformation in human resource management requires public organizations to provide information systems that are not only technically reliable but also capable of supporting employee efficiency and sustainability. This study aims to analyze the effect of Human Resource Information System (HRIS) service quality on employee work productivity and examine the role of green behavior as a mediating variable at the Regional Office of the Directorate General of Treasury of West Sulawesi Province. The study used a quantitative approach with an explanatory design. All 64 employees were selected as respondents through a saturated sampling technique. Data were collected using a Likert-scale questionnaire and analyzed using Structural Equation Modeling–Partial Least Squares using SmartPLS. The results showed that HRIS service quality had a positive and significant effect on employee work productivity with a path coefficient of 0.312, a t-statistic of 2.986, and a p-value of 0.003. HRIS service quality also had a positive and significant effect on green behavior with a coefficient of 0.751, a t-statistic of 9.214, and a p-value <0.001. Furthermore, green behavior had a positive and significant effect on work productivity, with a coefficient of 0.418, a t-statistic of 4.307, and a p-value <0.001. The indirect effect of HRIS service quality on productivity through green behavior was also significant, with a coefficient of 0.314, a t-statistic of 4.081, and a p-value <0.001. The model explained 71.2% of the variation in employee work productivity. These findings confirm that responsive, easy-to-use, and tailored HRIS services can increase productivity directly and through fostering resource-efficient, orderly, and sustainability-oriented work behaviors.
The Influence of Quality and Green Behavior of the SIAGA Application Management Information System on the Performance of PAI Teachers at the Ministry of Religion of Mamuju Regency Sahnidar Hasan; Hasan Imran; 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 | DOI: 10.33292/swc93h50

Abstract

Digital transformation in educational administration services demands the availability of a quality information system and efficient and sustainability-oriented user behavior. The Religious Education Teacher Information and Administration System (SIAGA) application is used to support data collection, document updating, verification, and administrative services for Islamic Education teachers. This study aims to analyze the effect of the quality of the SIAGA application and green behavior on the performance of Islamic Education teachers at the Ministry of Religious Affairs of Mamuju Regency. The study used a quantitative approach with an explanatory research design. The study population included all 42 Islamic Education teachers within the Ministry of Religious Affairs of Mamuju Regency. Due to the relatively limited population, all members of the population were selected as respondents through a saturated sampling technique. Data were collected using a Likert-scale questionnaire and analyzed using the Partial Least Squares–Structural Equation Modeling method through SmartPLS 4. The results of the measurement model evaluation showed that all indicators had outer loading values ​​above 0.70, AVE values ​​of 0.735–0.744, and composite reliability of 0.917–0.921, thus the instrument was declared valid and reliable. The results of the structural model test indicate that SIAGA quality has a positive and significant effect on Islamic Religious Education (PAI) teacher performance, with a path coefficient of 0.421, a t-statistic of 3.912, and a p-value <0.001. Green behavior also has a positive and significant effect on performance, with a coefficient of 0.368, a t-statistic of 3.276, and a p-value of 0.001. Both variables explain 64.2% of the variation in Islamic Religious Education (PAI) teacher performance. The study concludes that improving the technical quality of SIAGA and cultivating digital work behaviors that are resource-efficient, orderly, and responsible can sustainably strengthen Islamic Religious Education (PAI) teacher performance.
The Influence of Service Quality on and Trust of Business Actors in the OSS Information System: The Role of Satisfaction as a Moderation Variable in LKPM Reporting at the Mamuju Regency Investment and One-Stop Integrated Services Office: Service Quality, oss, Information System Mastura; Arsywanty; 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 | DOI: 10.33292/4gvbzh72

Abstract

The digitalization of licensing services through the Online Single Submission Information System (OSS) is expected to improve efficiency, transparency, and compliance of business actors in submitting Investment Activity Reports (LKPM). However, the success of the system is not only determined by technical aspects, but also by service quality, user satisfaction, and business actor trust. This study aims to analyze the effect of service quality on business actor trust and examine satisfaction as a moderating variable in LKPM reporting through OSS at the Mamuju Regency Investment and One-Stop Integrated Service Office. The study used a quantitative approach with an explanatory research design. The research sample consisted of 64 business actors selected using a purposive sampling technique based on the criteria of having a Business Identification Number, actively using OSS, and having submitted LKPM. Data were collected through a Likert-scale questionnaire and analyzed using Structural Equation Modeling–Partial Least Squares with the help of SmartPLS. The results showed that service quality had a positive and significant effect on satisfaction with a path coefficient of 0.782, a t-statistic of 8.941, and a p-value <0.001. Service quality also positively influences trust, with a coefficient of 0.314, a t-statistic of 2.874, and a p-value of 0.004. Satisfaction positively influences trust, with a coefficient of 0.447, a t-statistic of 4.912, and a p-value <0.001. The interaction between service quality and satisfaction proved significant, with a coefficient of 0.176 and a p-value of 0.023. The R-square value of 0.668 indicates that the model explains 66.8% of the variation in business actor trust. It is concluded that satisfaction strengthens the influence of service quality on trust, making increased responsiveness, procedural clarity, system stability, and reporting assistance crucial in supporting the effectiveness of the OSS (Owner-Source Accountability System)

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