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All Journal Sistemasi: Jurnal Sistem Informasi IJIS - Indonesian Journal On Information System Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika BAJET ( Baturaja Journal of Educational Technology) Journal of Educational Management and Learning
Albaar, Muhammad Ridha
Universitas Khairun
Humanities Computer Science & IT Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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A CNN-BASED APPROACH FOR DEVELOPING AN AUTOMATED FACE DETECTION SYSTEM Albaar, Muhammad Ridha; Turuy, Seh; Darmawan, M. Arhan; Ramadhan, Muhammad Fikri; Hadi, Gamal
IJIS - Indonesian Journal On Information System Vol 11, No 2 (2026): SEPTEMBER
Publisher : POLITEKNIK SAINS DAN TEKNOLOGI WIRATAMA MALUKU UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36549/ijis.v11i2.526

Abstract

Facial detection technology has become increasingly significant across various domains, including security, surveillance, biometrics, and human–computer interaction. This study aims to develop a facial detection system using a Convolutional Neural Network (CNN) approach to recognize and classify facial features from image data. A quantitative experimental method was employed, integrating deep learning modeling with software engineering principles. The system was implemented in MATLAB using a graphical user interface (GUI), and the model was trained on a dataset of 800 facial expression images. Data preprocessing involved image resizing, pixel normalization, and data augmentation to enhance model robustness. The dataset was divided into training, validation, and testing sets to ensure reliable model evaluation and generalization. The CNN model was trained and empirically tested to assess its performance under various conditions. The results indicate that the proposed system is capable of accurately detecting facial regions, even under natural lighting and complex backgrounds. The model achieved high accuracy in face identification, as evidenced by correct bounding box localization in the output interface. Furthermore, the system demonstrated stable performance across different facial orientations. However, several limitations remain, including the need for improved multi-face detection, real-time processing optimization, and broader evaluation across diverse expressions and demographic variations. In conclusion, the CNN-based facial detection system demonstrates strong effectiveness and accuracy, indicating its potential applicability in real-world visual recognition systems.Keywords:  CNN, Face Detection, Deep Learning, Influence. Image Recognitiion, Matlab
Co-Authors Abdullah, Saiful Do Abjan Samad Abubakar Abdullah, Abubakar Acim, Acim Akil Thalib Amal Khairan Darmawan, M. Arhan Destitha Almaqvira Bachmid Erick Harlest Budi Harjo Guret, Rahmat Hadi, Gamal Hairil Kurniadi Siradjudin Haydar Qisyaam Sangadji Kapita, Syarifuddin N M Kasyif Gufran Umar Mamonto, Arifandy Mario Muhammad Fikri Ramadhan Muin, Yasir rosihan, rosihan Sadly Samsuddin Siradjuddin, Hairil Kurniadi Sitanggang, Rinson Sudjud, Nuryati Syakdiyah, Halimatus Turuy, Seh