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All Journal JURNAL PENDIDIKAN TAMBUSAI Jurnal Teknologi Informasi dan Pendidikan
Hadi, Ahmadul
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Computer Science & IT Control & Systems Engineering Education Engineering Social Sciences

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Implementasi Computer Vision pada Media Trainer Kode Semaphore Pramuka Berbasis YOLO-Pose Khairy, Mubarakh Hayatna; Hendriyani, Yeka; Hadi, Ahmadul; Saputra, Hadi Kurnia
Jurnal Pendidikan Tambusai Vol. 9 No. 2 (2025): Agustus
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i2.31202

Abstract

Semaphore merupakan salah satu jenis sandi dalam kegiatan Pramuka yang digunakan sebagai media komunikasi visual jarak jauh. Namun, pembelajaran semaphore masih menghadapi kendala dalam mengenali gerakan dengan cepat dan tepat. Penelitian ini bertujuan untuk merancang dan membangun media trainer gerakan semaphore berbasis YOLO-Pose, sebuah metode deteksi objek yang dikombinasi dengan Human Pose Estimation. Sistem dilatih menggunakan 649 data citra huruf A–Z dan diuji pada epoch 150. Hasil menunjukkan bahwa model mampu mengenali gerakan dengan tingkat akurasi tinggi, precision mencapai 91–94%, recall 89–93%, dan mAP-50 sebesar 95%. Sistem ini berhasil mengklasifikasikan huruf secara real-time dan menunjukkan potensi sebagai media pembelajaran interaktif dalam kegiatan Pramuka.
Web-Based Dental Caries Detection Using a Convolutional Neural Network and OpenCV Alfarouq, Ahmad Dzaki; Hadi, Ahmadul; Novaliendry, Dony; Budayawan, Khairi
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan (In Press)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1059

Abstract

Early detection of dental caries presents a significant challenge, particularly in regions with limited access to healthcare services. While many AI models focus on binary classification, real-world applications must handle irrelevant inputs to be robust. This study develops and evaluates a web-based system using a Convolutional Neural Network (CNN) for a three-class dental image classification task: 'Caries', 'No Caries', and 'Not a Tooth'. The method employs transfer learning with the MobileNetV3 Small architecture, trained on a custom augmented dataset of 5,811 images. The model was implemented into an accessible web application using the Flask framework and OpenCV library, supporting both image upload and real-time detection. On the test set, the model achieved an overall accuracy of 93%. It demonstrated exceptional performance in rejecting irrelevant images and high reliability in identifying caries. This study presents a practical and robust tool for initial dental screening, highlighting the importance of a dedicated 'non-target' class for building trustworthy real-world AI applications in tele-dentistry.
Co-Authors Alfarouq, Ahmad Dzaki Budayawan, Khairi Dony Novaliendry Hadi Kurnia Saputra Khairy, Mubarakh Hayatna Yeka Hendriyani