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All Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Intelek Dan Cendikiawan Nusantara
Mochamad Rizal Fauzan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Other

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Rancang Bangun Sistem Kipas Angin Daur Ulang 12 Volt Berbasis Sensor Suhu dan PIR Mochamad Rizal Fauzan; Muhammad Saseno; Raden Muhammad Rafi Rachman; Ryan Nurhidayat; Pingky Setiawati
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 1 No. 2 (2024): APRIL - MEI 2024
Publisher : PT. Intelek Cendikiawan Nusantara

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Abstract

Pada era modern ini, efisiensi energi dan kenyamanan menjadi kebutuhan yang penting. Penelitian ini bertujuan merancang sistem kipas angin daur ulang berbasis sensor suhu DHT11 dan sensor PIR menggunakan Arduino Nano untuk meningkatkan efisiensi energi. Metode yang digunakan adalah eksperimen yang melibatkan perancangan, pembangunan, pemrograman, serta pengujian sistem. Sensor suhu dan sensor PIR mendeteksi kondisi lingkungan dan keberadaan manusia, yang kemudian diproses oleh Arduino untuk mengontrol operasi kipas angin. Hasil pengujian menunjukkan bahwa sensor DHT11 memiliki akurasi yang baik dengan error rendah, sedangkan sensor PIR efektif mendeteksi keberadaan hingga jarak 70 cm. Kipas angin beroperasi pada kecepatan yang berbeda berdasarkan suhu yang terdeteksi dan keberadaan manusia, serta mati saat tidak ada objek dalam jangkauan deteksi, menunjukkan peningkatan efisiensi energi. Kesimpulannya, sistem kipas angin yang dikembangkan dapat berfungsi otomatis dan efisien, menyesuaikan kecepatan kipas berdasarkan kondisi lingkungan. Disarankan untuk menggunakan sensor suhu dengan akurasi lebih tinggi dan memperluas jangkauan deteksi sensor PIR untuk meningkatkan kinerja sistem dalam berbagai kondisi.
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 Ibnu Hartopo Ilyasa Nafan Faza Jaja Kustija Muhammad Adli Rizqulloh Muhammad Saseno Pingky Setiawati Raden Muhammad Rafi Rachman Resa Pramudita Ryan Nurhidayat