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      "contact_email": "digilib@unpkdr.ac.id",
      "department": "KODEPRODI55201#Teknik Informatika",
      "date": "2026-08-06",
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      "institution": "Universitas Nusantara PGRI Kediri",
      "pages": 92,
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      "abstract": "Penelitian ini dilatar belakangi oleh tren home workout tanpa supervisi profesional yang\r\nrentan memunculkan kesalahan teknik pada latihan isolasi otot, khususnya triceps\r\nkickback. Kesalahan seperti excessive elbow swing (ayunan siku berlebihan) dan\r\nekstensi lengan yang tidak penuh dapat menurunkan efektivitas latihan serta\r\nmeningkatkan risiko cedera pada bahu, siku, atau punggung bawah. Sebagai solusi,\r\ndikembangkan sebuah aplikasi web dengan arsitektur client-side yang menggunakan\r\npendekatan hybrid.\r\nSistem ini mengintegrasikan pustaka MediaPipe untuk mendeteksi koordinat landmark\r\ntubuh secara real-time, logika state machine untuk melacak repetisi latihan, dan\r\nalgoritma Decision Tree untuk mengklasifikasikan kualitas gerakan menjadi \"Benar\"\r\natau \"Salah\". Sistem juga dilengkapi dengan Web Speech API yang memberikan audio\r\nfeedback asinkron untuk mengoreksi postur pengguna secara langsung. Berdasarkan\r\nhasil pengujian, model Decision Tree (model kedua) berhasil mencapai tingkat akurasi\r\nsebesar 91%. Pengujian usabilitas menggunakan System Usability Scale (SUS)\r\nmenghasilkan skor rata-rata 86,0 (kategori Excellent), yang menunjukkan bahwa sistem\r\nini sangat layak dan mudah digunakan oleh masyarakat awam. Sistem secara\r\nkeseluruhan terbukti efektif dalam memantau postur, menghitung repetisi, memberikan\r\numpan balik suara, dan mengestimasi kalori pengguna berdasarkan usaha mekanik.",
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      "thesis_type": "other",
      "keywords": "Computer Vision, Pose Estimation, MediaPipe, Decision Tree, Triceps Kickback, Koreksi Postur Otomatis",
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      ],
      "title": "SISTEM DETEKSI DAN KOREKSI GERAKAN TRICEPS\r\nKICKBACK BERBASIS POSE ESTIMATION",
      "thesis_name": "dphil",
      "userid": 65061,
      "eprintid": 27661,
      "lastmod": "2026-08-06 13:47:48",
      "referencetext": "Afifah, A. N. N., & Suradi, A. A. M. (2025). Sistem Deteksi Postur Duduk Berbasis\r\nMediaPipe untuk Meningkatkan Ergonomi dan Kesehatan Pekerja. In Februari\r\n(Vol. 2025, Number 1).\r\nChen, H., Feng, R., Wu, S., Xu, H., Zhou, F., & Liu, Z. (2022). 2D Human Pose\r\nEstimation: A Survey. http:\/\/arxiv.org\/abs\/2204.07370\r\nDyansah, K. R. K., Purwantoro, S. D., Ilmi, M., & Wulanningrum, R. (2025).\r\nPenggunaan Computer Vision untuk Estimasi Pose Squat sebagai Solusi Alternatif\r\nLatihan Kebugaran di Gym (Vol. 4).\r\nJijo, B. T., & Abdulazeez, A. M. (2021). Classification Based on Decision Tree\r\nAlgorithm for Machine Learning. Journal of Applied Science and Technology\r\nTrends, 2(1), 20–28. https:\/\/doi.org\/10.38094\/jastt20165\r\nKim, J. W., Choi, J. Y., Ha, E. J., & Choi, J. H. (2023). Human Pose Estimation Using\r\nMediaPipe Pose and Optimization Method Based on a Humanoid Model. Applied\r\nSciences (Switzerland), 13(4). https:\/\/doi.org\/10.3390\/app13042700\r\nMansour, M., Serbest, K., & Kutlu, M. Ç. (2025). Rapid Estimation of Elbow Joint\r\nMoment and Triceps Force During Triceps Dumbbell Kickback. Politeknik Dergisi,\r\n1–1. https:\/\/doi.org\/10.2339\/politeknik.1681703\r\nMishra, Y. (2025). AI Human Fitness Tracker using Computer Vision with MediaPipe.\r\nInternational Journal for Research in Applied Science and Engineering Technology,\r\n13(5), 1659–1669. https:\/\/doi.org\/10.22214\/ijraset.2025.70547\r\nPark, J., Chung, S. Y., & Park, J. H. (2022). Real-Time Exercise Feedback through a\r\nConvolutional Neural Network: A Machine Learning-Based Motion-Detecting\r\nMobile Exercise Coaching Application. Yonsei Medical Journal, 63, S34–S42.\r\nhttps:\/\/doi.org\/10.3349\/ymj.2022.63.S34\r\nSimoes, W., Reis, L., Araujo, C., & Maia, J. (2024). Accuracy Assessment of 2D Pose\r\nEstimation with MediaPipe for Physiotherapy Exercises. Procedia C"
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