Platform Deep Learning Terpadu Olahraga & Sepak Bola
Konsolidasi 7 kecerdasan deep learning canggih dalam satu asisten interaktif. Mulai dari estimasi pose, deteksi kejadian lapangan, hingga analisis risiko cedera atlet secara instan.
Konsolidasi 7 kecerdasan deep learning canggih dalam satu asisten interaktif. Mulai dari estimasi pose, deteksi kejadian lapangan, hingga analisis risiko cedera atlet secara instan.
Kolaborasi mahasiswa Kelas 3 TI E dalam mata kuliah Sistem Besar Deep Learning.
Proyek ini merupakan wujud integrasi dari mata kuliah Sistem Besar Deep Learning oleh mahasiswa Kelas 3 TI E. Kami menyatukan karya terbaik dari 7 kelompok kelas untuk menghasilkan satu ekosistem web terpadu yang memantau performa, meminimalkan risiko cedera, serta membaca jalannya permainan sepak bola.
Dengan membagi sistem ke dalam arsitektur micro-services backend yang efisien, sistem dapat memproses video, foto, dan data tabular dalam waktu singkat tanpa membebani memori server.
Model Deep Learning
Integrasi Tanpa Hambatan
Arsitektur Model Teruji
Deteksi Realtime Tercepat
Pilih dari tujuh fitur deep learning yang dikembangkan khusus untuk pemantauan olahraga.
Deteksi pose latihan secara real-time. Menghitung repetisi push-up, curl, press, dan squat, serta memberikan koreksi sudut sendi seketika.
Memprediksi probabilitas menang, seri, atau kalah untuk pertandingan tim EPL berikutnya menggunakan model regresi sekuensial LSTM berdasarkan performa historis.
Memproyeksikan rating masa depan dan karir pemain bola. Menghasilkan keputusan taktis tim berdasarkan forecasting model autoregresif LSTM.
Menghitung persentase tingkat kerawanan cedera atlet menggunakan model jaringan saraf tiruan (ANN) berlandaskan metrik latihan harian.
Deteksi presisi pemain sepak bola, bola, wasit, dan penjaga gawang di lapangan hijau secara spasial menggunakan model YOLOv8.
Membaca rekaman video tackle dari beberapa sudut kamera untuk mengidentifikasi adanya pelanggaran (offence) menggunakan MobileNetV2 + LSTM.
Mengidentifikasi 7 jenis kejadian/kejadian pertandingan penting (seperti Corner Kick, Penalty kick, Red/Yellow Card) hanya lewat sebuah foto aksi.
Alur pemrosesan data oleh kecerdasan buatan dari input hingga hasil visualisasi interaktif.
Pilih salah satu dari 7 modul analisis cerdas olahraga melalui asisten chat interaktif.
Unggah file rekaman video, foto aksi lapangan, atau ketik metrik performa atlet Anda.
Model backend Python memproses data memakai algoritma khusus (YOLO, LSTM, ANN, dll.).
Lihat hasil berupa pelacakan pose, grafik distribusi, peta sebaran pemain, dan rekomendasi.
Kolaborasi mahasiswa dalam pengembangan proyek Sistem Besar Deep Learning.
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