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All Journal Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Arkabaev, Nurkasym
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Education Engineering Industrial & Manufacturing Engineering

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MODELLING AND ANALYSIS OF OPTIMIZATION ALGORITHMS Arkabaev, Nurkasym; Rahimov, Elshan; Abdullaev, Alisher; Padmanaban, Harish; Salmanov, Vugar
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 9 No. 1 (2025): Volume 9, Nomor 1, March 2025
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v9i1.38410

Abstract

The purpose of this study was to comprehensively analyze existing optimization algorithms for Machine Learning (ML) models and develop new approaches aimed at improving their performance and efficiency. The study compared traditional and novel machine learning optimization techniques to evaluate their impact on model performance. The main results include a detailed overview of the main optimization methods in ML, including gradient descent, stochastic gradient descent, metaheuristic-based methods, and non-zero methods. Specific cases of using optimization algorithms in ML tasks, such as image processing, machine translation, and speech recognition were presented. A table comparing the advantages and disadvantages of the methods by key performance metrics is provided. The structural diagrams and principles of operation of each method are presented. In addition, the methods of integrating the developed approaches into existing ML platforms are investigated. The study's results demonstrate that integrating novel optimization techniques significantly enhances machine learning model performance. These methods offer a substantial improvement over traditional techniques like gradient descent, providing greater flexibility and efficiency in handling complex and evolving data. The findings suggest that combining these approaches with existing optimization strategies can lead to more robust and scalable machine learning systems across diverse industries. The findings suggest that combining these methods with traditional approaches can enhance machine learning performance and guide future AI developments. The novelty of the research is in the introduction of the novel techniques like adaptive model selection and dynamic parameter adaptation to improve machine learning efficiency in real-time data environments.
GENERATIVE AI REDUCES PERIPHERAL BUT NOT MENTAL COGNITIVE LOAD IN PROGRAMMING EDUCATION Arkabaev, Nurkasym; Oichueva, Roza; Ajibekova, Aizada
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 4 (2026): Volume 10, Nomor 4, August 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i4.58071

Abstract

Generative AI tools have entered programming classrooms faster than universities have learned to manage them, and evidence from resource-constrained education systems remains scarce. This study examined how engineering students use ChatGPT when learning programming and whether this use is associated with changes in perceived cognitive load. A quantitative cross-sectional survey design was applied: 140 undergraduate engineering students completed a three-block questionnaire combining a demographic and usage profile, a modified NASA-TLX cognitive load scale, and a perceived usefulness scale adapted from the Technology Acceptance Model. The results show near-universal adoption: 94.3% of respondents use ChatGPT for learning, mainly for code debugging and explanations of theoretical concepts. Compared with conventional study, working with ChatGPT was rated significantly lower on five of six NASA-TLX subscales (physical demand, temporal demand, frustration, perceived difficulty, and effort), whereas mental demand did not differ. Usefulness ratings were moderate, risk awareness was low, and usage intensity declined from the second to the fourth year of study (H=7.78; p=0.021). The novelty of this study lies in providing the first field evidence from Central Asia that self-directed generative AI use selectively removes the peripheral load of programming work while leaving its intellectual core intact. The main limitations are the single-institution sample and the use of self-reported measures. The findings support scaffolded integration of AI tutors and compulsory modules on verifying AI-generated code within AI literacy provision.
Co-Authors Abdullaev, Alisher Ajibekova, Aizada Oichueva, Roza Padmanaban, Harish Rahimov, Elshan Salmanov, Vugar