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All Journal Jurnal ULTIMA Computing Edsence: Jurnal Pendidikan Multimedia Bantenese Jurnal Pengabdian Masyarakat Indonesian Journal of Electrical Engineering and Computer Science REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Jurnal Intelek Dan Cendikiawan Nusantara
Fauzan, Mochamad Rizal
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Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Air Filtration System Utilizing Biomimetic Technology and IoT for Air Quality Improvement Fauzan, Mochamad Rizal; Al Azhima, Silmi Ath Thahirah; Pramudita, Resa; Hakim, Dadang Lukman; Rahmawati, Hanifah Indah; Azmi, Mutiara Nabila; Fauzi, Rafi Rahman; Somantri, Maman; Rahayu, Sri
ULTIMA Computing Vol 16 No 2 (2024): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/sk.v16i2.3871

Abstract

The "Hepix" smart air filtration system, developed with biomimetic and Internet of Things (IoT) technology, aims to address the urgent issue of poor indoor air quality, particularly in high-mobility urban areas. This system integrates advanced sensors (MQ135 and BME680) and biomimetic filtration inspired by leaf stomata to monitor and filter air pollutants. Tested across three locations”Cilame, Jatinangor, and Cibiru”the system achieved an approximate 24.4% reduction in pollutant levels, as well as stable control of humidity and air pressure. Real-time data is continuously monitored through a mobile and web interface, supported by Google Assistant integration for voice commands. The results demonstrate that "Hepix" effectively improves air quality, offering a practical solution for healthier indoor environments in urban areas.
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.
Accuracy–Efficiency Trade-off Analysis of CNN Backbones for Multi-Class Waste Classification Fauzan, Mochamad Rizal; Surya, Irgi; Pramudita, Resa
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 5 No. 2 (2026)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v5.i2.48169

Abstract

Automated waste classification is a critical component of intelligent recycling systems, where model selection must balance predictive performance and computational efficiency. This study benchmarks three representative convolutional neural network (CNN) backbones—ResNet50, EfficientNet-B0, and MobileNetV3-Large—for eight-class waste classification under controlled augmentation and unified optimization protocols. Using a fixed 80/10/10 split (7,747 training, 969 validations, and 960 testing images), all models are evaluated across multiple random seeds to ensure statistical reliability. Performance is assessed using macro-F1, precision, recall, and GPU-based inference latency to characterize the accuracy–efficiency trade-off. EfficientNet-B0 achieves the highest macro-F1 (0.9676 ± 0.0034), while MobileNetV3-Large delivers comparable performance (0.9669 ± 0.0023) with substantially lower latency—approximately six times faster than ResNet50. Augmentation sensitivity analysis further reveals architecture-dependent robustness under occlusion-based perturbation. These results demonstrate that lightweight architectures can achieve near-optimal classification performance with significantly reduced computational cost, providing deployment-oriented guidelines for practical waste sorting systems.
Co-Authors Al Azhima, Silmi Ath Thahirah Al Qibtiya, Mariya Al Qibtya, Mariya Ath Thahirah Al Azhima, Silmi Azmi, Mutiara Nabila Budi, Agus Heri Setya Dadang Lukman Hakim Dedi Rohendi Dedy Suryadi Fauzi, Rafi Rahman Ibnu Hartopo Ilyasa Nafan Faza Iwa Kuntadi Iwan Kustiawan Jaja Kustija Jaja Kustija, Jaja Juanda, Endang Akhmad Kaniarudi, Neneng Puspita Khairi, Saiqa Fatur Maman Somantri Muhammad Adli Rizqulloh Muhammad Saseno Neris Peri Ardiansyah Nurul Fahmi Arief Hakim Pingky Setiawati Pramudita, Resa Raden Muhammad Rafi Rachman Rahmawati, Hanifah Indah Resa Pramudita Roer Eka Pawinanto, Roer Eka Ryan Nurhidayat Saripudin Saripudin Siscka Elvyanti, Siscka SRI RAHAYU Surya, Irgi Tuti Suartini Wasimudin Surya Saputra Wawan Purnama