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All Journal Jurnal Pendidikan Karakter Cakrawala Pendidikan Jurnal Kependidikan: Penelitian Inovasi Pembelajaran Jurnal Edukasi Elektro JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Journal of Education and Learning (EduLearn) International Journal of Advances in Intelligent Informatics ELINVO (Electronics, Informatics, and Vocational Education) INVOTEC Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Zamtinah Zamtinah
Universitas Negeri Yogyakarta
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Power Consumption Analysis and Evaluation of Energy Saving Potential of Lighting System in DEF Building Wardhana, Alex Sandria Jaya; Zamtinah; Sukisno, Toto; Yuniarti, Nurhening; Bachrun, Muhammad Al Azis
Jurnal Edukasi Elektro Vol. 9 No. 1 (2025): Jurnal Edukasi Elektro Volume 9, No. 1, May 2025
Publisher : DPTE FT UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jee.v9i1.85443

Abstract

This study aims to analyze power consumption and evaluate the potential energy savings of the lighting system in the DEF Building, which has various room functions including offices, laboratories, meeting rooms, and libraries. The study was conducted with a descriptive quantitative approach based on field measurement data. Data collection includes measuring lighting intensity (lux), inventory of types and number of lamps, and power consumption (watt). The measurement results were compared with the Indonesian National Standard SNI 6197:2020 to assess the minimum lighting level and maximum power limit. In addition, a simulation of replacing conventional lamps with LED lamps was carried out using Dialux Evo software. The results showed that most rooms had power consumption per square meter that was still efficient (<12 W/m²), but many rooms did not meet the minimum lighting standards. The simulation of replacing lamps with LEDs resulted in significant power savings, with a total reduction in energy consumption of 928.93 kWh per month or equivalent to electricity cost savings of around Rp1,362,997.48. Replacing lamps was also able to improve the quality of lighting in rooms that previously did not meet the standards. This study shows that effective lighting system management through LED lamp retrofitting can be an important strategy in supporting building energy efficiency.
Pengembangan Trainer Pembelajaran Smart Switch pada Mata Pelajaran Instalasi Penerangan Listrik di SMK N 1 Magelang: Penelitian Zamtinah Zamtinah; Muchtar Abdul Aziz; Alex Sandria Jaya Wardhana; Eko Swi Damarwan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 3 No. 4 (2025): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 3 Nomor 4 (April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v3i4.4983

Abstract

This study aims to produce a smart switch trainer learning media that is suitable for use in the Electrical Lighting Installation subject. The study uses the Research and Development (RnD) method with the ADDIE model which includes the stages of analysis, design, development, implementation, and evaluation. The research subjects consisted of 2 media experts, 2 material experts, and 47 grade XI students of SMK N 1 Magelang. Data collection was carried out through observation, interviews, and Likert scale questionnaires, with quantitative descriptive data analysis. The results of the development are in the form of a hardcase suitcase trainer equipped with a guidebook. The feasibility test showed that the smart switch trainer was declared suitable for use, with the validation results of material experts at 85% (very suitable), media experts 79% (suitable), and user responses 87% (very satisfactory).
Explainable machine learning with class balancing for predicting student academic performance Zamtinah Zamtinah; Rianto Rianto; Paulus Insap Santosa
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2379

Abstract

Accurate prediction of student academic performance is crucial for implementing effective early intervention strategies in higher education. However, existing models often struggle with class imbalance and lack interpretability, limiting their practical adoption. This methodological proof-of-concept develops a robust predictiveframework by benchmarking multiple machine learning algorithms, specifically Logistic Regression, Decision Tree, and the standard Random Forest, against a proposed Random Forest classifier with class balancing. Utilizing a public simulation dataset of 2,392 student records, the proposed model incorporates algorithmic penalties for misclassifications of the minority class to address data skewness. Experimental results demonstrate that this balanced approach significantly outperforms the linear and standard tree-based baselines, achieving an Accuracy of 92.3% and an F1 Macro score of 0.872. The results confirm the model's superior capability in identifying at-risk students without compromising overall precision. Furthermore, explainability analysis using SHAP (Shapley Additive exPlanations) identifies Grade Point Average (GPA) as the dominant predictor, while highlighting Absences and Weekly Study Time as critical behavioral leading indicators. These findings demonstrate that integrating predictive accuracy with explainable AI supports proactive and evidence-based academic governance. Future research is recommended to validate this framework using longitudinal data streams within Learning Management Systems, enabling real-time monitoring.
Co-Authors Adnanda, Azka Bachrun, Muhammad Al Azis Didik Hariyanto Djoko Laras Budiyo Taruno Doni Sarosa Edy Supriyadi Eko Swi Damarwan Eva Monika Nahulae Fathurrahman, Haris Imam Karim Hafidz Hafidz Herlambang SP Heru Subaris Kasjono, Heru Subaris Ketut Ima Ismara Kurniawan, Ari Wibowo Muchtar Abdul Aziz Mustaqim, Bima Nurhening Yuniarti Paulus Insap Santosa Rahmah Tyasari Rianto Rianto Sunaryo Sunarto, Sunaryo Toto Sukisno Untung Kurniawan Wardhana, Alex Sandria Jaya Yuwono Indro Hatmojo, Yuwono