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JOMLAI: Journal of Machine Learning and Artificial Intelligence
Published by Yayasan Literasi Sains Indonesia
ISSN : 28289102     EISSN : 28289099     DOI : 10.55123/jomlai
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Focus and Scope JOMLAI: Journal of Machine Learning and Artificial Intelligence is a scientific journal related to machine learning and artificial intelligence that contains scientific writings on pure research and applied research in the field of machine learning and artificial intelligence as well as an overview of the development of theories, methods, and related applied sciences. Topics cover the following areas (but are not limited to): Software engineering Hardware Engineering Information Security System Engineering Expert system Decision Support System Data Mining Artificial Intelligence System Computer network Computer Engineering Image processing Genetic Algorithm Information Systems Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Other relevant study topics Noted: Articles have primary citations and have never been published online or printed before
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 4 Documents
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Search results for , issue "Vol. 4 No. 4 (2025): Desember 2025" : 4 Documents clear
Entropy-Regularized Nonlinear Auto-Regressive Network with eXogenous Inputs (ER-NARX): A Mathematical Framework for Scalable and Robust Big Data Forecasting Using ITL and Fractional Dynamics Zulfatri Aini; Tengku Reza Suka Alaqsa
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 4 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i4.6689

Abstract

This study proposes the Entropy-Regularized NARX (ER-NARX) model, which integrates nonlinear autoregressive modeling, entropy-based regularization, and information-theoretic learning for big data forecasting. The NARX model captures temporal dependencies between past outputs and exogenous inputs, while entropy regularization is incorporated to control the uncertainty of model predictions and prevent overfitting. The innovation of this model is its ability to control information flow through entropy regularization, which helps balance predictive accuracy with uncertainty, preventing the model from becoming overly deterministic. By combining these components, the ER-NARX model enhances the stability and robustness of the forecasts and improves its generalization to complex, high-dimensional data. Additionally, fractional dynamics are employed to model long-range memory effects in temporal data to enhancing the model's ability to handle datasets with extended dependencies. The resulting ER-NARX framework provides a mathematically grounded approach to big data forecasting improved performance in a computationally efficient manner. Future research may explore advanced entropy regularization techniques and apply the model to more diverse real-world data with intricate dependencies.
Quantum-Entropy NARX (Q-ENARX): A Mathematical Framework for Forecasting Based on Quantum Information Theory and Nonlinear Dynamic Regularization Tengku Reza Suka Alaqsa; Syarifah Adriana
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 4 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i4.6722

Abstract

This study addresses the limitations of conventional nonlinear autoregressive models, which struggle to maintain stability and generalization in high-dimensional, non-stationary forecasting environments. The research aims to develop a mathematical framework that integrates deterministic dynamics with probabilistic uncertainty through the proposed Quantum-Entropy NARX (Q-ENARX) model. The methodology combines nonlinear autoregressive modeling, entropy-based trust-region optimization, and quantum information theory to establish a unified formulation for dynamic forecasting. The model embeds NARX states into a quantum Hilbert space, introduces an entropy-regularized loss function to balance accuracy and uncertainty, and employs a quantum Fisher Information Matrix for curvature-aware optimization. Analytical derivations reveal that Q-ENARX achieves enhanced stability, improved generalization, and robust convergence by leveraging quantum state dynamics, entropy-energy duality, and fractional learning operators. The results shows that the integration of entropy and quantum principles transforms traditional NARX forecasting into a probabilistically interpretable and physically grounded framework capable of capturing complex temporal correlations with high mathematical precision.
Integration of Artificial Intelligence in Physical Education Learning to Improve Learning Quality Butsiarah, Butsiarah
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 4 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i4.6734

Abstract

This study aims to analyze the impact of integrating Artificial Intelligence (AI) into physical education learning to enhance students’ learning quality. The research employed a quasi-experimental method with a pretest-posttest control group design involving 60 students, divided into experimental and control groups. The research instruments included a learning motivation questionnaire, a student activity observation sheet, and a physical fitness test. The results revealed a significant improvement in the experimental group that applied AI-based learning. The average learning motivation score increased from 72.3 to 88.6 (a 22.5% increase), students’ active participation rose from 68.4% to 90.2%, and physical fitness scores improved from 70.1 to 83.5. The t-test results indicated a significant difference between the experimental and control groups (p < 0.05). These findings demonstrate that the implementation of artificial intelligence technology in physical education can effectively enhance students’ motivation, participation, and learning outcomes. Therefore, integrating AI into the learning process can serve as an innovative and effective strategy to improve the quality of physical education and support the transformation of learning in the rapidly evolving digital era.
Artificial Intelligence and the Sociology of Folklore: A Comparative Study of Turkey and Indonesia Dewi Christa Kobis; Michel Farrel Tomatala
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 4 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i4.7459

Abstract

This study examines how Artificial Intelligence (AI) mediates the production and circulation of folklore in Turkey and Indonesia and asks: How does AI mediate folklore production and circulation in both contexts? And What similarities and differences characterize AI–folklore interaction from a sociological perspective? Using a qualitative comparative cultural sociology design, the analysis draws on Turkish sociological theory, Ziya Gokalp’s concept of halk kültürü and Şerif Mardin’s center–periphery framework alongside documentary and digital ethnographic observation of publicly accessible folklore texts, AI-mediated outputs (retellings, translations, summaries), and platform-based circulation. Findings show that AI mediates folklore production through textual regeneration, translation, narrative standardization, and platform-oriented adaptation, while circulation is shaped by recommendation, tagging, ranking, and translation interfaces that regulate visibility, dissemination, and the dominance of particular versions. Although both countries experience algorithmic pressures on cultural memory and narrative visibility, impacts diverge by context: Turkey’s institutionally anchored cultural authority tends to constrain AI toward selective continuity, whereas Indonesia’s pluralistic and decentralized landscape allows AI and platforms to operate as stronger cultural gatekeepers, enabling more transformative reinterpretations. Overall, the study concludes that AI is a structural cultural mediator whose sociological effects are context-dependent, amplifying existing power relations rather than uniformly transforming folklore.

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