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All Journal Jurnal Mandiri IT SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
M. Ilham AlFatrah
Unknown Affiliation
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Library & Information Science Mathematics

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Machine learning-based approach for evaluating physical fitness through motion detection Rais, M. Fazil; Chadafa Zulti Noorta; M. Ilham AlFatrah; H.A Danang Rimbawa; Fatmawati, Uvi Desi
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.406

Abstract

Physical fitness assessment is crucial for evaluating an individual's physical performance and endurance. However, traditional methods often rely on manual observation, which can lead to subjectivity and inconsistent results. This study proposes a machine learning-based approach for physical fitness evaluation through motion detection using pose estimation and exercise classification models. A quantitative method was employed to train and evaluate models for four exercise types: push-ups, sit-ups, pull-ups, and chinning. Each model was trained separately and assessed using accuracy, precision, recall, and F1-score metrics, achieving accuracies of 97.50% for push-ups, 97.67% for sit-ups, 97.00% for pull-ups, and 98.50% for chinning. The maximum error margin compared to manual counting was 2.48%. System-generated outputs were validated against manual observations using standard evaluation matrices. These findings indicate that machine learning can offer a reliable, consistent, and automated solution for physical fitness assessment, with the potential to enhance training programs, support remote fitness monitoring, and reduce human error in performance evaluation.
Implementasi Teknologi Mediapipe Menggunakan Metode CNN Berbasis Website Untuk Pengamanan VVIP Dalam Mobil M. Ilham AlFatrah; Hery Sudaryanto; H. A. Danang Rimbawa
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 3 (2025): July
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan mengembangkan sistem deteksi gestur tangan berbasis MediaPipe dan Convolutional Neural Network (CNN) guna meningkatkan efektivitas pengamanan VVIP. Mengingat ancaman modern yang semakin kompleks, sistem ini dirancang untuk mendeteksi gestur darurat secara real-time dan memungkinkan respons cepat. Metodologi yang digunakan meliputi pengumpulan dataset gestur tangan, anotasi data menggunakan MediaPipe, dan pelatihan model CNN di Google Colab. Kinerja model dievaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Pengujian juga dilakukan dalam berbagai kondisi, seperti pencahayaan rendah dan gerakan cepat, untuk menilai ketangguhan sistem di dunia nyata. Hasilnya, sistem ini berhasil mendeteksi gestur tangan darurat dengan akurasi tinggi dan kecepatan kurang dari satu detik. Kinerja optimal, dengan akurasi mendekati 100%, tercapai pada kondisi pencahayaan yang baik. Meskipun akurasi sedikit menurun pada kondisi ekstrem, integrasi sistem pada platform website memungkinkan pengawasan dan pengambilan keputusan cepat di pusat komando. Penelitian ini membuktikan bahwa kombinasi MediaPipe dan CNN adalah solusi inovatif, namun optimasi lebih lanjut tetap dibutuhkan.
Co-Authors Chadafa Zulti Noorta H. A. Danang Rimbawa H.A Danang Rimbawa Hery Sudaryanto Rais, M. Fazil Uvi Desi Fatmawati