Identifikasi Gerakan Shalat Menggunakan Model Klasifikasi Convolutional Neural Network dengan Pengolahan Citra Prewitt dan Morphology
Identification of Prayer Movements Using Convolutional Neural Network Classification Model and Prewitt and Morphology Image Processing
Keywords:
Feature extraction, Gerakan Takbir, Machine Learning, Morfologi, PrewittAbstract
Gerakan shalat menurut sunnah harus dilakukan dengan tepat. Gerakan shalat dapat dipelajari dengan guru agama agar gerakannya tepat, namun banyak orang yang membutuhkan waktu lebih lama atau mencari guru agama yang dapat mengajarkannya. Untuk itu, diperlukan suatu sistem pembelajaran yang dapat membantu mengenali gerakan shalat, khususnya gerakan takbir. Penelitian ini berfokus pada gerakan takbir berdasarkan kitab Fiqih Sholat Seperti Nabi karya Syeikh Albani. Penelitian ini berfokus pada peningkatan akurasi pendeteksian gerakan takbir menggunakan metode pengolahan citra berbasis Convolutional Neural Network (CNN) dengan operator Prewitt dan operasi morfologi. Pada tahap awal, operator Prewitt diterapkan untuk mendeteksi tepi gerakan pada citra grayscale, yaitu dengan menonjolkan kontur gerakan tangan saat takbir. Kemudian, dilakukan operasi morfologi seperti dilatasi dan erosi untuk menghaluskan citra dan mengurangi noise, sehingga memperjelas tepi gerakan yang terdeteksi. Citra yang dihasilkan menjadi input bagi model CNN yang dilatih menggunakan teknik transfer learning. Dengan pendekatan ini, model CNN memperoleh akurasi sebesar 89,2% dalam mendeteksi gerakan takbir. Hasil penelitian menunjukkan bahwa kombinasi operator Prewitt, operasi morfologi, dan CNN efektif meningkatkan akurasi klasifikasi gerakan sholat dan memberikan kontribusi baru pada pengenalan gerakan sholat menggunakan metode pemrosesan gambar
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