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All Journal International Journal of Engineering, Science and Information Technology ELECTROPS : Jurnal Ilmiah Teknik Elektro
Adi Pandu Wirawan
Universitas Mulawarman
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Analisis Kualitas Daya Listrik Pada Gedung Perpustakaan Universitas Mulawarman Tahun 2025 Rois Gusti Dewa; Fatkhul Hani Rumawan; Adi Pandu Wirawan; Muslimin Muslimin; Didit Suprihanto
ELECTROPS : Jurnal Ilmiah Teknik Elektro Vol 5, No 1 (2026): ELECTROPS : Jurnal Ilmiah Teknik Elektro
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/electrops.v5i1.25869

Abstract

Penelitian ini bertujuan menganalisis profil kualitas daya listrik pada Gedung Perpustakaan Universitas Mulawarman, mengingat peran vitalnya sebagai pusat layanan informasi yang memiliki banyak beban elektronik sensitif. Menggunakan metode kuantitatif deskriptif, penelitian dilakukan melalui pengukuran langsung pada panel utama (Main Distribution Panel) menggunakan Power Quality Analyzer Kyoritsu KEW 6315. Pengambilan data berlangsung selama 10 hari kerja pada dua periode beban puncak, yaitu pagi (09.00–10.00 WITA) dan siang (13.00–14.00 WITA), untuk mendapatkan gambaran performa sistem yang akurat. Parameter yang dievaluasi meliputi daya listrik, faktor daya, tegangan, frekuensi, ketidakseimbangan beban, serta harmonisa, yang kemudian dikomparasi terhadap standar SNI PUIL 2020, SPLN, IEEE 519-2014, dan Peraturan Menteri ESDM. Hasil penelitian menunjukkan sistem beroperasi dengan kinerja sangat baik: faktor daya terjaga pada rentang 0,909–0,950, frekuensi stabil (49,999–50,017 Hz), dan deviasi tegangan di bawah 10%. Distorsi harmonisa juga tercatat rendah dengan THDv maksimal 1,126% dan THDi maksimal 9,601%. Meskipun terdapat ketidakseimbangan beban sebesar 6,10%–7,50%, nilainya masih dalam batas toleransi standar, sehingga disimpulkan sistem kelistrikan gedung sangat andal dan aman mendukung operasional perpustakaan.
Multiperson Automated Attendance System Based on Face Recognition Using YOLO and DeepFace with Active Learning Didit Suprihanto; Adi Pandu Wirawan; Kahlil Gibran Saputra; Arif Harjanto; Imam Muhammad Hakim; Happy Nugroho
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1772

Abstract

Accurate student attendance tracking is essential in academic environments, yet traditional methods remain inefficient and vulnerable to manipulation. This research presents a classroom attendance system based on facial recognition that integrates YOLO for multiperson face detection and the SFace model from the DeepFace framework for feature extraction and identity matching. A key contribution of this study is the implementation of an Active Learning mechanism that enables the system to update its embedding Database using user-provided corrections, enabling continuous adaptation to real classroom conditions. The system was developed as a Python-based desktop application and evaluated using 38 group images captured with various devices under uncontrolled lighting, diverse head poses, occlusion, and different classroom densities. Performance was assessed using accuracy, False Rejection Rate (FRR), and False Acceptance Rate (FAR) across two scenarios: before and after Active Learning. Experimental results show a substantial improvement after the learning process, with accuracy increasing from 52.0% to 96.6%, while maintaining a low FAR of 0%. These findings demonstrate that Active Learning effectively enhances recognition performance by enriching the embedding Database with real-world facial variations that do not present during initial registration. Overall, the proposed system highlights the importance of integrating Active Learning into face recognition–based attendance applications to improve robustness and adaptability in unconstrained multiperson classroom environments.
Co-Authors Arif Harjanto Didit Suprihanto, Didit Fatkhul Hani Rumawan Happy Nugroho Imam Muhammad Hakim Kahlil Gibran Saputra Muslimin Muslimin Rois Gusti Dewa