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Widi Aribowo
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vubeta@unesa.ac.id
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Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 30640768     DOI : https://doi.org/10.26740/vubeta.v1i1
Core Subject : Science, Engineering,
Computer Science & IT Engineering Mechanical Engineering Transportation
Vokasi Unesa Bulletin Of Engineering, Technology and Applied Science is a peer-reviewed, Quarterly International Journal, that publishes high-quality theoretical and experimental papers of permanent interest, that have not previously been published in a journal, in the field of engineering, technology, and applied sciences that aim to promote the theory and practice of Engineering, Technology And Applied Science.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 83 Documents
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Absorptive Materials -Based Cooling Technologies for Solar Thermal: A Review of Thermal Management Strategies and Performance Enhancements Noori, Sajad W; lafta, Duaa Alaa; lafta, Alaa M.; mansour, mustafa
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.44867

Abstract

The growing need for utility-scale photovoltaic (PV) systems to advance environmental goals has heightened concerns about the costs of scaling and thermal control. Among all the technical problems linked to PV panels, increased temperatures are the key issue, causing reduced efficiency and module damage. When solar energy is not absorbed by the photocells, the PV module's surface temperature can rise much higher, especially in hot climates. This is especially problematic at air temperatures above 50 °C, as traditional natural convection is unable to efficiently cool the PV modules; hence, the Spanish solar PV harnessing system traps 30% of the energy in the PV modules compared to the original efficiency. Moreover, high surface temperature cause material degradation, resulting in earlier thermal failure, replacement, or the expense of disposing of the latter. This is why methods for enhancing thermal management within PV panels are among the most important aspects, and, combined with several technological advances, PV readily available and could potentially reduce the cost of solar energy in the near future.
Stock Price Forecasting Using LSTM with Cross-Validation Rifki Ainul Yaqin; Anshori, Muhammad Iqbal; Angel, Reddis; Agung, Ignatius Wiseto Prasetyo; Arifin, Toni; Junianto, Erfian
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.45130

Abstract

Stock price prediction remains challenging due to the market’s nonlinear, volatile nature, influenced by diverse economic and behavioral factors. Traditional models often suffer from overfitting and limited generalizability. This study addresses these limitations from prior research by other researchers for integrating Long Short-Term Memory (LSTM) with k-Fold Cross-Validation to improve prediction robustness. The proposed framework systematically evaluates model performance across varying market conditions. This methodological contribution enhances forecasting accuracy and stability, offering a more reliable approach to complex financial time series prediction. This study employs LSTM with one to two layers of 64–128 units, trained using Adam and dropout regularization, to capture long-term dependencies in stock price data. The workflow integrates feature selection, Min-Max scaling, and k-Fold Cross-Validation for robust evaluation. Model performance is assessed using RMSE, with reconfiguration applied to address underfitting or overfitting. The proposed model demonstrated substantial performance gains, achieving an average RMSE improvement of approximately 78.40% across all tested stocks compared to prior research. These enhancements are attributed to optimal hyperparameter tuning, consistent use of the Adam optimizer, and the implementation of k-Fold Cross-Validation, which reduced overfitting and provided more stable evaluations. Furthermore, findings revealed that simpler feature sets, such as using only closing prices, can outperform multiple technical indicators when normalization is inadequate, underscoring the importance of robust preprocessing and validation strategies. This study concludes that integrating LSTM with k-Fold Cross-Validation and optimized hyperparameters significantly improves stock price prediction accuracy.
The Conceptual Understanding of Metaheuristic Algorithms : A Brief Reviews Aribowo, Widi
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.46163

Abstract

Metaheuristic algorithms have garnered significant attention in the field of optimization due to their ability to address complex, nonlinear, and combinatorial problems where conventional exact methods are often impractical. Inspired by natural phenomena, social behaviors, and physical processes, these algorithms provide near-optimal solutions within reasonable computational time by balancing exploration and exploitation. This paper presents a comprehensive review of metaheuristic algorithms, categorizing them into single-solution-based and population-based approaches. It further discusses hybrid and adaptive variants designed to overcome limitations such as premature convergence and parameter sensitivity. The study highlights the advantages, disadvantages, and practical applications of various metaheuristics across diverse domains including engineering, logistics, artificial intelligence, energy systems, and bioinformatics offering researchers a structured guide for selecting appropriate algorithms based on problem characteristics.

Page 9 of 9 | Total Record : 83

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