Implementasi Convolutional Neural Network (CNN) untuk Face Recognition pada Sistem Presensi Kehadiran - Moch Arif Rochmanullah; Nurlaily Vendyansyah; Febriana Santi Wahyuni (IJAI (Indonesian Journal of Applied Informatics), 2025)
Abstrak : Sistem presensi merupakan elemen penting dalam memastikan kehadiran, terutama di lingkungan pendidikan dan pekerjaan. Penelitian ini bertujuan mengembangkan sistem presensi berbasis face recognition menggunakan metode Convolutional Neural Network (CNN) untuk mengatasi kelemahan presensi manual yang rentan terhadap kecurangan, seperti di Prodi Teknik Informatika ITN Malang. Model CNN dilatih dengan deep learning menggunakan dataset wajah mahasiswa untuk mengenali pola unik fitur wajah. Hasilnya, model mencapai training accuracy sebesar 97%, validation accuracy sebesar 90%, dan pengujian mencapai accuracy 93%. Sistem ini meningkatkan efisiensi absensi dan akurasi identifikasi hingga 93%, sekaligus mengurangi potensi kecurangan.CNN terbukti andal dalam mendukung presensi berbasis teknologi dengan pengelolaan lebih praktis. Kendati demikian, performa model masih dapat ditingkatkan melalui pengayaan dataset dan optimasi model. Sistem ini berpotensi besar meningkatkan keandalan dan keamanan proses presensi, menjadi solusi inovatif dalam pengelolaan kehadiran di era digital.=====================================================Abstract :The attendance system is a crucial element in ensuring presence, especially in educational and workplace settings. This study aims to develop a face recognition-based attendance system using the Convolutional Neural Network (CNN) method to address the weaknesses of manual attendance prone to fraud, as observed in the Informatics Engineering Study Program at ITN Malang. The CNN model was trained using deep learning techniques with a student face dataset to recognize unique facial features. The results show the model achieved a training accuracy of 97%, validation accuracy of 90%, and testing accuracy of 93%. This system improves attendance efficiency and identification accuracy by 93%, while reducing the potential for fraud. CNN has proven reliable in supporting technology-based attendance with more practical management. However, the model’s performance can still be improved through dataset enrichment and optimization. This system holds significant potential to enhance the reliability and security of attendance processes, providing an innovative solution for managing attendance in the digital era.