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All Journal Energi & Kelistrikan Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Jaja Kustija
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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APLIKASI TRANSISTOR DARLINGTON PADA RANGKAIAN INVERTER PORTABLE Sofitri Rahayu; Jaja Kustija
Energi & Kelistrikan Vol 10 No 2 (2018): Energi & Kelistrikan
Publisher : Sekolah Tinggi Teknik PLN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.495 KB) | DOI: 10.33322/energi.v10i2.229

Abstract

Energi listrik digunakan dalam segala lapangan kehidupan manusia untuk membantu meringankan pekerjaannya. Kebutuhan akan sumber energi listrik AC (arus bolak balik) tidak selamanya tersedia di setiap tempat misalnya ketika kita berada jauh dari supplay energi listrik dari PLN. Untuk mendapatkan sumber energi listrik arus bolak balik (AC) ketika berada jauh dari sumbernya maka di dapat dengan cara mengubah sumber DC menjadi AC menggunakan inverter. Inverter yang dirancang portable menggunakan sumber DC 12 volt dan komponen inverter berbasis IC 555 sebagai pembangkit pulsa dan CMOS 4013 sebagai rangkaian D flip-flop. Karena tegangan yang keluar dari CMOS 4013 ini kecil maka dihubungkan dengan transistor MJ11016 sebagai penguat. Kemudian tegangan yang dihasilkan dinaikkan dengan menggunakan trafo Step Up. Sistem ini dirancang berkemampuan daya sebesar 60 watt.
Student Behavior Detection Using YOLOv10 for Classroom Engagement Analysis Resa Pramudita; Mochamad Rizal Fauzan; Ilyasa Nafan Faza; Jaja Kustija; Ibnu Hartopo; Muhammad Adli Rizqulloh
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 15 No 2: Mei 2026 (dalam proses)
Publisher : This journal is published by the Department of Electrical and Information Engineering, Faculty of Engineering, Universitas Gadjah Mada.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v15i2.24611

Abstract

Student engagement is a critical determinant of learning effectiveness, yet manual observation in classroom environments remains labor-intensive, subjective, and difficult to scale. This study examined a student behavior detection framework built on You Only Look Once (YOLO) version 10 or YOLOv10, the latest generation of real-time object detection models. A dataset of 2,600 annotated classroom images covering eight behavioral categories was collected under diverse conditions, including variations in lighting, camera perspectives, and occlusion. Five YOLOv10 variants (n, s, m, l, x) were trained and evaluated using precision, recall, F1 score, and mean average precision (mAP). The best-performing configuration achieved an overall mAP@0.5 of 0.821 and mAP@0.5:0.95 of 0.640, with strong performance on upright (AP = 0.967), bow head (AP = 0.958), and sleep (AP = 0.943), while more subtle behaviors such as writing (AP = 0.519) and hand-raising (AP = 0.650) proved challenging. Importantly, the system maintained real-time inference speeds ranging from 40 to 88 FPS depending on the YOLOv10 variant, when evaluated on an RTX 2060 GPU, thereby demonstrating its robustness for deployment in classroom settings. To ensure usability, the optimized YOLOv10 model was integrated into a Streamlit-based interactive dashboard, enabling educators to monitor engagement levels and respond with timely interventions. By combining state-of-the-art YOLOv10 architecture with real-time behavioral analytics, this work establishes a scalable foundation for intelligent classroom monitoring and contributes to advancing technology-enhanced education.
Co-Authors Fauzan, Mochamad Rizal Ibnu Hartopo Ilyasa Nafan Faza Muhammad Adli Rizqulloh Resa Pramudita Sofitri Rahayu