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International Journal of Artificial Intelligence Research
Published by STMIK Dharma Wacana
ISSN : -     EISSN : 25797298     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : -
Articles 621 Documents
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The Transformation Of E-HRM In Developing The Quality Of Human Resources Based On Information Technology Toward Employee Performance Through Employee Satisfaction Sumaryono, Sumaryono; Machdar, Nera Marinda; Hidayat, Wastam Wahyu; Rony, Zahara Tussoleha
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1572

Abstract

This research aims to evaluate the impact of e-compensation, e-learning, and e-performance appraisal on organisational performance, with team member satisfaction as a mediating variable. The study was conducted at PT. XYZ. Methodology used: A total of 77 respondents participated in this research. Data processing was carried out using the Structural Equation Modeling (SEM) method with a Partial Least Squares (PLS) variance-based approach. The results of the reliability and construct validity tests indicate that all variables have a high reliability level, with Cronbach's Alpha values as follows: Compensation Management (0.96), E-Learning (0.95), Employee Satisfaction (0.94), Employee Performance (0.96), and Performance Appraisal (0.91), all of which exceed the threshold of 0.90. The team member satisfaction variable consists of 13 indicators (Z.01 to Z.13) and was measured using a survey involving 77 respondents. To meet the criteria for convergent validity, each item must have a loading factor value of at least 0.60. An R-square value of 0.81 indicates that 81% of the variability in the dependent variable. Based on the analysis results, all indicators in this study meet the criteria for good validity and reliability. The indicators for the Employee Satisfaction variable show loading factor values above the threshold of 0.60.
Harnessing Generative AI for ESP: A Cross-Disciplinary Vocational Education Framework with Predictive Modeling Evidence from Indonesia Pranoto, Dani Chandra Yudho; Rufii, Rufii; Sabariah, Sabariah; Bandono, Adi
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1569

Abstract

This study examines how Generative AI tools, specifically ChatGPT and Gemini, can enhance English for Specific Purposes (ESP) learning and education. Drawing on the UTAUT2 model of technology acceptance and recent discussions on AI-mediated learning, we examine the roles of baseline ability, perceived usefulness, and satisfaction as mediating factors in ESP classrooms. Data were collected from 50 vocational students across five departments using pre- and post-tests, AI usage logs, and Likert-scale surveys. Statistical analyses included descriptive statistics, paired t-tests, ANOVA with Tukey adjustment, correlation, reliability tests, and predictive modeling (OLS and LASSO) in SAS Studio. Results show a mean learning gain of 24.42 points, with Nursing and IT students benefiting most. AI usage hours strongly correlate with post-test scores but not directly with learning gain, suggesting that perceived usefulness and satisfaction (both rated 4.4/5 with ? = 1.00) mediate the outcomes. Baseline competence remains the strongest predictor, highlighting persistent disparities in skill distribution across vocational fields. These findings suggest that the effective integration of Generative AI in ESP requires scaffolding and domain-specific alignment, rather than simple exposure. The study offers a novel framework for AI-supported ESP instruction, providing practical guidance for educators and policymakers in Indonesia and similar contexts.
VISUAL HISTORICAL DATA-BASED TRAFFIC MOVEMENT AND DENSITY PATTERN EXTRACTION FOR ADAPTIVE PATTERN DETECTION BASE ON VEHICLE TYPE Angellia, Filda; Merlina, Nita; Subekti, Agus; Handayanto, Rahmadya Trias
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1570

Abstract

Traffic congestion in urban areas has become a crucial issue, impacting time efficiency, energy consumption, and quality of life. One of the main causes of difficulties in traffic management is the lack of optimal predictive systems capable of detecting and adaptively responding to vehicle movement patterns. This study proposes a historical digital image-based approach to extract traffic movement patterns and density based on vehicle type and dimensions. The developed model utilizes historical traffic video footage from CCTV systems as a visual data source, which is then processed using the YOLOv5 algorithm to detect the number, size, and type of vehicles. After the detection process, vehicle information is converted into a sequential format that reflects vehicle movement in the temporal dimension. This data is then analyzed using a Long Short-Term Memory (LSTM) model to generate traffic density prediction patterns. This study also compares the performance of LSTM with other algorithms such as Random Forest and XGBoost in terms of prediction accuracy. Model evaluation is conducted using MSE and RMSE metrics to measure accuracy against actual data.The research results show that the integration of dimension-based vehicle detection with a visual historical data-driven prediction approach can improve the accuracy and flexibility of modeling future traffic conditions. This approach significantly contributes to the development of intelligent transportation systems that can adapt to dynamic environmental conditions and traffic patterns
AI-Assisted Bibliometric Mapping of Global Research on Technology-Based Training for Enhancing School Principals' Managerial Competence in the Digital Transformation Era Sabariah, Sabariah; Rufi'i, Rufi'i; Bandono, Adi; Hakim, Luqmanul; Suharti, Suharti; Chandra, Dani; Novta, Novta; Netty, Netty
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1579

Abstract

Digital transformation demands that school principals possess technology-based managerial competencies to lead innovation, improve organizational effectiveness, and create inclusive learning environments in the dynamic era of global education. This article describes trends, thematic focuses, and global collaborations in technology-based educational leadership research to strengthen principals' managerial competencies in facing the challenges of digital transformation and inclusive education. The research methodology uses an artificial intelligence (AI)-assisted bibliometric approach with quantitative-descriptive analysis, including Scopus data extraction, cleaning, normalization, visualization, and validation of results through NLP and clustering topic modeling. The analysis shows significant publication growth since 2010 with a peak in the 2024–2025 period, driven by the accelerated adoption of educational technology after the COVID-19 pandemic. The rate of publication increase reached 6.63% per year, with an average of 11.74 citations per document. Dominant themes include educational leadership, school principals, instructional leadership, and professional development, while new topics such as digital leadership, gender equality, and policy innovation began to strengthen post-2020. Affiliation mapping shows the dominance of universities from Spain, South Africa, and Israel, followed by an increase in contributions from Asian institutions (Indonesia and Malaysia) since 2016. The most productive author is ARAR KH (8 publications), followed by DOR-HAIM P. and HALLINGER P., who are the main nodes of the global network. Cross-country collaboration has been shown to increase the number of citations and strengthen the research network. The study highlights the shift in principal leadership from traditional models to data-driven digital ones, emphasizing technology integration, global collaboration, ethics, and managerial competencies in facing the challenges of digital-era education.
The Influence of Human Resource Management, Organizational Commitment, and Work Discipline on Employee Performance Mediated by Job Satisfaction Basir, Abdul
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1583

Abstract

The purpose of this study is to examine how work discipline, organizational commitment, and human resource management affect team member performance and how job satisfaction mediates these effects.  This study used both a quantitative method and a causally associative approach.  The study's population consisted of team members employed in Indonesia's state-owned banking industry.  Purposive sampling was used to choose the 78 employees that made up the research sample.  Data is gathered through survey methods, and employees are given questionnaires to fill out.  Partial least squares structural equation modeling (PLS-SEM) is used in SmartPLS data analysis.  The results of the study indicate that work satisfaction and human resource management significantly affect team member performance. On the other hand, team members' performance was not significantly impacted by organizational commitment or work discipline.  Job satisfaction can operate as a stronger mediator between the effects of organizational commitment and human resource management on team member performance.  It does not, however, act as a mediator in the link between team members' performance and work discipline.  By including current issues unique to the banking industry, this study adds to the conceptual foundation of team members' performance.  Financial institutions can benefit from the study's insightful recommendations, which emphasize the significance of creating all-encompassing plans that combine team member welfare with corporate goals.
Work Talenta DNA as A Catalyzer Employee Performance Priyadi, Catur; Pudjiarti, Emiliana Sri; Widokarti, Joko Rizkie; Shaddiq, Syahrial
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1587

Abstract

Based on the identification of the problems explained above, this research proposal will focus on two research problems that can be formulated, namely: How is the individual behavior model based on a dynamic attitude to always follow the work culture to improve performance and. Is the proposed new concept of Work DNA Talent (WDT) able to bridge the relationship between leadership transformation and employee performance The three basic theoretical approaches that have been described previously, namely Talent Management theory, DNA theory, and Work Culture theory, are the basis for developing a new concept called Work Talent DNA (WTD). statistical analysis using structural equation models (SEM) through Analysis Moment of Structural (AMOS) software. 24.00. probability sampling with the Proportionate Stratified Random Sampling method. The population is 270 Millennial Generation ASN. The research findings prove that this concept has a significant effect on improving Employee Performance. Thus, Work Talent DNA acts as a mediating variable that links Transformational Leadership with Employee Performance, thus providing a new contribution to the development of modern human resource management theory
Optimization of Transportation Mode Selection for EPC Project Logistics Using Analytical Hierarchy Process (AHP): A Case Study of RoRo-Based Inter-Island Material Delivery in Indonesia Shohib, Muhammad; Tjendani, Hanie Teki; Purnama, Jaka
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1589

Abstract

Efficient logistics management plays a crucial role in the success of EPC projects, particularly when transporting heavy fabricated components across island regions. This study aims to identify the most optimal transportation mode using the AHP based on six key criteria: cost, lead time, risk, load capacity, flexibility, and port accessibility. A hierarchical decision model was developed and assessed by seven expert respondents, with calculations performed using Expert Choice 11. The results indicate that cost, lead time, and port accessibility hold the highest priority weights, reflecting their strategic importance in EPC logistics. The synthesis of criteria and alternative weights identified Roll-on/Roll-off (RoRo) as the most suitable transportation mode, outperforming cargo ships, barges, LCTs, and truck combinations. Validation using actual project data further confirmed this result: the total delivery cost using RoRo amounted to IDR 1,126,000,000, representing a 22% savings compared to the planned budget, and only 1.66% of the total project cost significantly lower than industry benchmarks of 8–11%.  The results confirm that AHP constitutes an effective and reliable approach for transportation mode selection in EPC projects. The study offers actionable managerial insights and advances the application of multi-criteria decision-making in construction logistics.
Development of a Web-Based Aviation English Proficiency Test: Integrating Adaptive Algorithms and Dynamic Assessment for Enhanced Evaluation in Aviation Education Rosyid, Harunur; Rochmawati, Laila; Sylvia, Tiara; Rossydi, Ahmad; Bhakti, Henny Dwi
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1620

Abstract

Aviation English proficiency is pivotal for aviation school students to ensure secure communication in global airspace per ICAO guidelines. Conventional methods are rigid, leading to inaccurate and time-consuming evaluations that hinder training efficacy. This research develops a web-based adaptive Aviation English proficiency test integrating adaptive algorithms like Item Response Theory and dynamic assessment to enhance aviation education outcomes. Using a mixed-methods framework with the ADDIE model and quantitative experimental approach, an explanatory sequential design with non-equivalent control group was employed, involving needs assessment, prototype development, validation, and implementation. The sample included 141 aviation school students. Data from pre/post-tests were analyzed via SPSS. The findings showed that i) the web-based test is valid and feasible as an assessment tool with a validation score of 89.5%; ii) student proficiency levels are significantly improved before and after using the adaptive system (paired t-test: mean rise from 72.6 to 91.4, t=-14.28, p=0.000 <0.05); iii) dynamic assessment positively impacts learning outcomes following implementation (32% uplift, ?=0.61, p<0.01); and iv) there is a significant difference between experimental and control groups in evaluation efficiency (independent t-test: 25% higher for experimental, t=10.52, p=0.000 <0.05). These affirm the test's efficacy, recommending broader adoption for refined aviation training.
Artificial Intelligence in Aviation Vocational Training: Mapping Legal Frameworks and Institutional Readiness in Indonesia Octavianie, Adhitya; Rossydi, Ahmad; Raharjo, Muh. Agung; Idyaningsih, Nining; Sukarman, Sukarman
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1635

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in higher and vocational education, yet its rapid adoption often outpaces existing legal and institutional frameworks. This study investigates how Indonesia’s regulatory landscape supports the integration of AI in teaching and learning at Politeknik Penerbangan Makassar (Poltekbang Makassar), and identifies the legal, ethical, and governance challenges shaping responsible innovation. Employing a qualitative descriptive approach, the research combines document analysis of key national policies (such as the National Education System Law 2003, Presidential Regulation No. 95/2018, and the National AI Strategy 2020–2045) with semi-structured interviews involving five lecturers and two academic policymakers. Data were analyzed thematically. The results reveal that while Indonesia’s legal foundations implicitly support AI adoption through broader digital transformation policies, significant regulatory gaps persist, particularly concerning algorithmic transparency, accountability, and data privacy. Institutional policies at Poltekbang Makassar demonstrate readiness for technological innovation but lack comprehensive AI governance mechanisms. The study proposes a multi-level regulatory model encompassing national policy formulation, sectoral alignment within vocational standards, and internal institutional governance frameworks to ensure accountability, fairness, and compliance. These findings contribute to the development of an Institutional AI Governance Framework based on principles of accountability, transparency, and compliance with the Personal Data Protection Law, offering a practical roadmap for policymakers and educational leaders in Indonesia’s vocational sector.Keywords: Artificial Intelligence (AI), AI Governance, Vocational Education, Poltekbang Makassar, Algorithmic Accountability
An Evaluation of SMOTE Effectiveness in Handling Class Imbalance in Public Opinion Data on the MBG Program Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1495

Abstract

The “Makan Bergizi Gratis” (MBG) Program is one of the strategic policies of the Government of Indonesia that reaps various opinions from the public, especially through social media. This study aims to classify public sentiment towards the MBG program with an ensemble learning-based machine learning approach, as well as evaluate the effectiveness of the SMOTE algorithm in dealing with class imbalance in opinion data. The dataset was collected from platform X (formerly Twitter) for the January–April 2025 period, totaling 4,374 tweets with label distributions: 1,783 positive, 1,634 negative, and 957 neutral. The preprocessing process includes data cleansing, normalization, stemming, and vectorization with TF-IDF. Five ensemble algorithms were used, namely Random Forest, AdaBoost, Bagging, Stacking, and Voting, tested in two scenarios: with and without the implementation of SMOTE. The results of the experiments showed that Random Forest provided the best and most consistent performance, with the F1-score increasing from 72.03% to 72.66% after the implementation of SMOTE. However, not all models benefit from SMOTE, such as Voting which experienced a drop in F1-score. These findings suggest that SMOTE is effective in increasing the sensitivity of the model to minority classes, but its success depends on the characteristics of the algorithm used. This study suggests the selective selection of balancing methods as well as the development of a more adaptive approach to handle unstructured opinion data.

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