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All Journal Jurnal Pengabdian Masyarakat Multidisiplin Andalasian International Journal of Applied Science, Engineering, and Technology
Baik Budi
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Aerospace Engineering Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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The Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm Baik Budi; Ilhamdi Rusydi, Muhammad; Arya Witama, Reivan; Hesti Ramadhamy, Queen; Budiman, Refki
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 5 No. 2 (2025): July 2025
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v5i02.223

Abstract

This study explores the use of Naive Bayes and k-means algorithms to predict and analyzed stability of the electrical grid. Data set for this research is public dataset from Kaggle. The main goal of the research is to develop an accurate and efficient predictive model. Naive Bayes was chosen it has ability to handle independent features and also have a compatibility with highdimensional data. The implementation was carried out using Python in Google Colab, with data preprocessing that included feature normalization and an 80:20 train-test split. The Gaussian Naive Bayes model was used for system stability classification. The results demonstrate excellent model performance, with an accuracy of 97.35%, precision of 98.91%, recall of 97.02%, and an F1-score of 97.95%. The confusion matrix reveals the model's ability to classify "stable" and "unstable" conditions with minimal prediction errors.
PERAN STRATEGIS DPL DALAM MBKM ASISTENSI MENGAJAR: STUDI DI SMAN 19 PEKANBARU TA. 2024/2025 Siti Nazhifah; Nabila Afifah Azuga; Agus Baharudin; Tiara Mahardika; Baik Budi
Jurnal Pengabdian Masyarakat Multidisiplin Vol 8 No 3 (2025): Juni
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/jpm.v8i3.6181

Abstract

The transformation of higher education in Indonesia through the Merdeka Belajar Kampus Merdeka (MBKM) policy has encouraged stronger integration between academic learning and real-world practice in the community, one of which is through the Teaching Assistance program. This article describes the strategic role of DPL or Field Supervisors in guiding students during the implementation of the Teaching Assistance Program at SMAN 19 Pekanbaru during the second semester of the 2024/2025 academic year. A descriptive-qualitative approach was used to explore the contributions of DPL as pedagogical facilitators, reflective evaluators, institutional liaisons, enhancers of soft skills and professionalism, and integrators of technology use for students. The data collection methods employed in this study included observation, interviews, documentation, guidance notes, and reflections throughout the program. The research findings indicate that the presence of DPL significantly contributes to strengthening students' pedagogical competencies, shaping professional character, and bridging communication between universities and partner schools. In addition to positively impacting the quality of student learning, this program also supports the optimal use of technology and active learning in schools. The research findings also emphasize that the success of the MBKM program implementation heavily depends on the synergy between students, DPL, and school stakeholders, as well as the active involvement of DPL in all stages of the activity.  
Co-Authors Agus Baharudin Arya Witama, Reivan Budiman, Refki Ilhamdi Rusydi, Muhammad Nabila Afifah Azuga Ramadhamy, Queen Hesti Siti Nazhifah Tiara Mahardika