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Mega Silfia Dewy, Mega Silfia
Universitas Negeri Medan
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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V-LAMOT: A Cognitive-Load Optimized Virtual Lab for Three-Phase Motor Control Isnaini, Muhammad; Purba, Sukarman; Dewy, Mega Silfia; Solihin, Muhammad Dani; Silitonga, Agnes Irene
Journal of Educational Technology and Learning Creativity Vol. 4 No. 1 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v4i1.2766

Abstract

Purpose of the study: This study aims to design and validate V-LAMOT, a web-based virtual laboratory for three-phase motor starting simulation. The system is intended to address limitations of physical laboratories by providing an accessible and safe environment while maintaining conceptual accuracy and supporting the development of practical motor control skills. Methodology: The study adopted the Systems Development Life Cycle (SDLC) to develop the V-LAMOT platform using HTML5, CSS, JavaScript, and state-machine modeling. The design was guided by Cognitive Load Theory principles. Data were obtained through expert validation instruments and the System Usability Scale (SUS), and analyzed using Shapiro–Wilk tests, one-sample t-tests, Cohen’s d, and Pearson correlation with 30 students. Main Findings: Expert validation indicated high feasibility, with conceptual accuracy reaching a mean score of 4.50/5. SUS evaluation produced an overall score of 78.83 (“Good”), with learnability scoring highest at 82.00. All usability measures were significantly above the benchmark (p < 0.001) with large effect sizes (d > 0.8). A strong correlation between usability and learnability (r = 0.823) suggested effective cognitive load reduction. Novelty/Originality of this study: This study presents an integrated virtual laboratory that combines state-machine modeling with Cognitive Load Theory-based interface design for three-phase motor control. Unlike conventional simulations, V-LAMOT integrates multiple motor starting methods in one environment and empirically links usability, learnability, and cognitive load reduction, advancing virtual laboratory development through systematic integration of technical accuracy and pedagogical principles.
V-LAMOT: A Cognitive-Load Optimized Virtual Lab for Three-Phase Motor Control Isnaini, Muhammad; Purba, Sukarman; Dewy, Mega Silfia; Solihin, Muhammad Dani; Silitonga, Agnes Irene
Journal of Educational Technology and Learning Creativity Vol. 4 No. 1 (2026): June
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v4i1.2766

Abstract

Purpose of the study: This study aims to design and validate V-LAMOT, a web-based virtual laboratory for three-phase motor starting simulation. The system is intended to address limitations of physical laboratories by providing an accessible and safe environment while maintaining conceptual accuracy and supporting the development of practical motor control skills. Methodology: The study adopted the Systems Development Life Cycle (SDLC) to develop the V-LAMOT platform using HTML5, CSS, JavaScript, and state-machine modeling. The design was guided by Cognitive Load Theory principles. Data were obtained through expert validation instruments and the System Usability Scale (SUS), and analyzed using Shapiro–Wilk tests, one-sample t-tests, Cohen’s d, and Pearson correlation with 30 students. Main Findings: Expert validation indicated high feasibility, with conceptual accuracy reaching a mean score of 4.50/5. SUS evaluation produced an overall score of 78.83 (“Good”), with learnability scoring highest at 82.00. All usability measures were significantly above the benchmark (p < 0.001) with large effect sizes (d > 0.8). A strong correlation between usability and learnability (r = 0.823) suggested effective cognitive load reduction. Novelty/Originality of this study: This study presents an integrated virtual laboratory that combines state-machine modeling with Cognitive Load Theory-based interface design for three-phase motor control. Unlike conventional simulations, V-LAMOT integrates multiple motor starting methods in one environment and empirically links usability, learnability, and cognitive load reduction, advancing virtual laboratory development through systematic integration of technical accuracy and pedagogical principles.
Optimasi Penempatan dan Penentuan Kapasitas Distributed Generator Menggunakan Cucko Search Algorithm untuk Mengurangi Rugi Daya Yoakim Simamora; Muhammada Aulia Rahman S; Mega Silfia Dewy; Agnes Irene Silitonga; Lisa Melvi Ginting
ELECTRON Jurnal Ilmiah Teknik Elektro Vol 6 No 2: Jurnal Electron, November 2025
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/electron.v6i2.409

Abstract

Power losses in electrical distribution systems remain a major challenge that significantly impacts energy efficiency and system reliability. One promising approach to address this issue is the optimal placement and sizing of Distributed Generators (DGs) within the distribution network. This study aims to optimize DG placement and capacity using the Cuckoo Search Algorithm (CSA) and to compare its performance with several other algorithms, namely the Black Squirrel Optimization Algorithm (BSOA), Sine Cosine Algorithm (SCA), Teaching Learning Based Optimization - Grey Wolf Optimizer (TLBO-GWO), and GWO. The study was conducted on the IEEE 33-bus test system under two scenarios, with the initial condition of the distribution system exhibiting a power loss of 202.7 kW. In First Case Study, CSA achieved the lowest power loss of 105.31 kW, corresponding to a 48.05% reduction. In contrast, BSOA and TLBO-GWO reduced losses to 116.67 kW (42.44%) and 128.46 kW (36.62%) respectively. In Second Case Study, CSA again demonstrated superior performance with a loss reduction of 56.66%, outperforming SCA (56.33%), BSOA (55.97%), and GWO (55.82%). The optimal DG placement and sizing significantly improved overall system efficiency. The results indicate that CSA possesses strong exploration and convergence capabilities in identifying optimal DG configurations. Its application enables greater reduction in power losses while also enhancing voltage profiles and system stability. These findings suggest that CSA is an effective and competitive method for power distribution optimization involving distributed generation
Co-Authors Agnes Irene Silitonga Ali Akbar Lubis Annisa, Selly Bakti Dwi Waluyo Dadang Mulyana, Dadang Erita Astrid Ganefri . Hutahaean, Harvei Desmon Imannuel, Michael Fritz Immanuel, Michael Fritz Indrati Kusumaningrum Lisa Melvi Ginting Michel Frits Immanuel Muhammad Dani Solihin Muhammad Isnaini Muhammad Isnaini Muhammada Aulia Rahman S Sembiring, Muhammad Aulia Rahman Silitinga, Agnes Irene Sukarman Purba Yoakim Simamora