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      "date": "2026-07-16",
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      "abstract": "Penelitian ini bertujuan merancang, membangun, serta menguji aplikasi berbasis web untuk mendeteksi dan mengklasifikasikan kualitas gerakan tolak peluru gaya spin pada fase awalan, dorongan, dan sikap akhir ke dalam kategori sempurna atau tidak sempurna. Data pengembangan model berasal dari 220 video, yang terdiri atas 110 video kategori sempurna dan 110 video kategori tidak sempurna. Setiap video dipisahkan menjadi tiga fase sehingga menghasilkan 660 sampel, dengan masing-masing fase terdiri atas 220 sampel. Setiap sampel direpresentasikan menggunakan 30 frame yang diambil secara merata. MediaPipe Pose digunakan untuk mengekstraksi 33 titik landmark tubuh yang masing-masing memiliki koordinat x, y, z, dan nilai visibility, sehingga bentuk masukan model adalah matriks berukuran 30 × 132. Metode klasifikasi menggunakan tiga model One-Dimensional Convolutional Neural Network atau 1D-CNN yang dilatih secara terpisah untuk setiap fase gerakan. Sistem diimplementasikan menggunakan framework Flask dengan fitur unggah video, perekaman melalui kamera web, pengaturan batas fase secara otomatis dan manual, visualisasi landmark, hasil klasifikasi, nilai confidence, feedback, serta kesimpulan kualitas gerakan. Hasil evaluasi model menunjukkan bahwa fase awalan memperoleh akurasi sebesar 93,94% dengan rata-rata presisi, recall, dan F1-score sebesar 0,94. Fase dorongan memperoleh akurasi sebesar 45,45% dengan rata-rata presisi, recall, dan F1-score sebesar 0,45, sedangkan fase sikap akhir memperoleh akurasi sebesar 84,85% dengan rata-rata presisi, recall, dan F1-score sebesar 0,85. Pengujian fungsional menunjukkan bahwa seluruh fitur utama aplikasi berjalan sesuai rancangan. Pengujian nonfungsional menggunakan tiga video baru dengan perbedaan durasi dan frame rate. Masing-masing video diuji sebanyak tiga kali sehingga diperoleh sembilan percobaan. Seluruh video berhasil diproses mulai dari pembacaan video, penentuan batas fase, ekstraksi landmark, hingga penyajian hasil klasifikasi, dengan rata-rata waktu pemrosesan keseluruhan sebesar 34,48 detik. Berdasarkan hasil tersebut, aplikasi berhasil dikembangkan dan mampu memproses video baru serta memberikan informasi kualitas gerakan pada setiap fase. Namun, model fase dorongan belum andal sehingga hasilnya perlu ditafsirkan secara hati-hati dan masih memerlukan peningkatan pada penelitian selanjutnya.",
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      "title": "DETEKSI DAN KLASIFIKASI GERAKAN TOLAK PELURU MENGGUNAKAN MEDIAPIPE DAN CONVOLUTIONAL NEURAL NETWORK (CNN)",
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      "referencetext": "Adiba, F. H. (2015). SISTEM ANALISIS GERAKAN BULUTANGKIS BERBASIS TEKNOLOGI BIOMETRIK. Universitas Nusantara PGRI Kediri.\r\nAngelini, F., Fu, Z., Long, Y., Shao, L., & Naqvi, S. M. (2018). ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition. ArXiv, 1–14. https:\/\/doi.org\/https:\/\/doi.org\/10.48550\/arXiv.1810.12126\r\nBazarevsky, V., Grishchenko, I., Raveendran, K., Zhu, T., Zhang, F., & Grundmann, M. (2020). BlazePose: On-device Real-time Body Pose tracking. ArXiv, 1–4. https:\/\/doi.org\/https:\/\/doi.org\/10.48550\/arXiv.2006.10204\r\nDaniel Tanugraha, F., Pratikno, H., Musayanah, M., & Indah Kusumawati, W. (2022). Pengenalan Gerakan Olahraga Berbasis (Long Short- Term Memory) Menggunakan Mediapipe. Journal of Advances in Information and Industrial Technology, 4(1), 37–45. https:\/\/doi.org\/10.52435\/jaiit.v4i1.182\r\nIkadarny. (2024). ANALISIS POLA GERAK TOLAK PELURU GAYA MENYAMPING MAHASISWA FIK UNM. JURNAL ILMIAH STOK BINA GUNA MEDAN, 12(1), 21–29. https:\/\/doi.org\/https:\/\/doi.org\/10.55081\/jsbg.v12i1.1823\r\nKato, T., Urita, Y., Kintaka, H., & Maeda, A. (2018). Relationship Between Momentum of Athlete-Shot System and Release Velocity in Rotational Shot Put Technique. 891–2311.\r\nKiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J. (2021). 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 151. https:\/\/doi.org\/10.1016\/j.ymssp.2020.107398\r\nMaharani, V. N., Suardika, I. K., & Refiater, U. H. (2024). ANALISIS BIOMEKANIKA OLAHRAGA TERHADAP METODE LATIHAN TEKNIK SHOOTING BOLA BASKET: LITERATUR REVIEW. Jambura Sports Coaching Academic Journal, 3(1), 29–40. https:\/\/doi.org\/https:\/\/doi.org\/10.37905\/jscaj.v3i1.23783\r\nMubayyin, M. T., Anantacia, E. M. A., Salsabila, C., & Hidayattullah, M. F. (2025). Klasifikasi dan deteksi pose pilates menggunakan Mediapipe dan Random Forest Classifier. Teknosains: Media Informasi Sains Dan Teknologi, 19(1), 56–63. https:\/\/doi.org\/10.24252\/teknosains.v19i1.53698\r\nPringsewu, U. A., Fauziah Novianti, A., & Saragih, Y. (2023). Aisyah Journal of Informatics and Electrical Engineering SISTEM IDENTIFIKASI PENGUKURAN BAJU MENGGUNAKAN HUMAN BODY ESTIMATION DATASET MEDIAPIPE DENGAN METODE EUCLIDEAN DISTANCE. Aisyah Journal of Informatics and Electrical Engineering, 5(2), 127–134. https:\/\/doi.org\/10.30604\/jti.v5i2.151\r\nRizki, A. B., & Zuliarso, E. (2022). Klasifikasi Teknik Bulutangkis Berdasarkan Pose Dengan Convolutional Neural Network. Jurnal Ilmiah Informatika, 10(02), 96–101. https:\/\/doi.org\/https:\/\/doi.org\/10.33884\/jif.v10i02.5559\r\nRizki, Y., & Parlindungan, D. P. (2024). Upaya Melatih Teknik Dasar Tolak Peluru untuk Meningkatkat Kemampuan Lemparan Siswa SMK Mutu Muhamadiyah 01 Wonosobo.\r\nSoekarta, R., Aras, S., & Ainun KDP, N. (2024). Sistem Deteksi Simbol Isyarat Pada SIBI (Sistem Isyarat Bahasa Indonesia) Menggunakan Mediapipe Berbasis Android. Insect (Informatics and Security): Jurnal Teknik Informatika, 10(2), 90–100. https:\/\/doi.org\/https:\/\/doi.org\/10.33506\/insect.v10i2.4079\r\nWilliam, R., Therry, L., Junaidi, A., & Sihananto, A. N. (2024). PROGRAM PENERJEMAH BAHASA ISYARAT INDONESIA (BISINDO) SECARA REAL TIME MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN MEDIAPIPE. Jurnal Mahasiswa Teknik Informatika, 8(6), 11642–11649. https:\/\/doi.org\/https:\/\/doi.org\/10.36040\/jati.v8i6.11582"
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