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All Journal Jambura Journal of Electrical and Electronics Engineering
Irvan Abraham Salihi
Universitas Ichsan Gorontalo
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Development of a Real-Time Face Recognition Attendance System Based on Face Embedding Using the FaceNet Architecture Irvan Abraham Salihi; Irma Surya Kumala Idris; Yasin Aril Mustofa; Zulfrianto Yusrin Lamasigi; Ardiansyah Kadir
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 2 (2026): Juli - Desember 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i2.38763

Abstract

This study aims to develop and evaluate an efficient and accurate face embedding-based attendance system to address the limitations of the fingerprint-based attendance system still in use at Ichsan Gorontalo University. The system was developed using the FaceNet model for 512-dimensional face embedding extraction, with facial similarity comparison performed using Cosine Distance. The system development followed the Waterfall methodology, encompassing analysis, design, implementation, and testing phases. Testing was conducted through three approaches: White Box Testing to evaluate programming logic, Black Box Testing for functional validation, and User Acceptance Testing (UAT) to measure user satisfaction. Accuracy testing was performed under three different conditions involving 10 volunteers (7 registered, 3 unregistered): neutral facial expression (at 1 meter distance), smiling expression (at 1 meter distance), and 5-meter distance. The results demonstrate that the system exhibits low logical complexity with a Cyclomatic Complexity (CC) value of 7, all functional components operate without significant errors, and it achieved a user satisfaction rate of 84.53% (Grade B). Accuracy testing yielded 90% accuracy under both neutral and smiling expression conditions, but decreased to 60% at 5-meter distance. The system achieved an average response time of 1.2 seconds with memory usage below 2 GB. This study concludes that the face embedding-based attendance system is effective and efficient for use under normal facial expression conditions and close-range scenarios, and is recommended for implementation as a more accurate and hygienic modern attendance solution.
Classification of Chili Plant Diseases Through GLCM Feature Selection and the K Parameter in the K-Nearest Neighbor Ratna A. Fi’Nawu; Irvan Abraham Salihi; Zulfrianto Yusrin Lamasigi; Irma Surya Kumala Idris
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.34661

Abstract

Chili pepper (Capsicum annuum L.) is a strategic horticultural commodity in Indonesia with high economic value. However, chili plants are often infected by diseases such as Anthracnose, Fusarium Wilt, Fruit Fly, and Thrips, which can lead to significant yield losses. Early and accurate identification of these diseases is crucial for effective control measures. This study aims to classify chili plant diseases based on leaf images using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction and the K-Nearest Neighbor (K-NN) algorithm for classification. A total of 736 leaf images were used, divided into four disease classes. The pre-processing stages included resizing the images to 300×300 pixels, rotation augmentation (0°, 45°, 75°, 90°), and conversion to grayscale. Textural features were extracted using GLCM at four angles, and K-NN was applied with K values of 5, 7, and 9. The highest classification accuracy of 88.19% was achieved at a GLCM angle of 0° and K=5, with an overall average accuracy across all angles of 85.06%. These findings not only reinforce previous findings on the effectiveness of GLCM and K-NN but also contribute by identifying the optimal parameter configuration (angle 0° and K=5) for the specific chili disease dataset. The results have the potential to be applied as a foundation for developing an automated plant disease detection system in the field.
Co-Authors Ardiansyah Kadir Irma Surya Kumala Idris Ratna A. Fi’Nawu Yasin Aril Mustofa Zulfrianto Yusrin Lamasigi