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All Journal Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert
Tawakal, Rayendra
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Computer Science & IT

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Journal : journal of advanced computer knowledge and algorithms

 1 
Real-Time Detection of Young and Old Faces Using Template Matching and Fuzzy Associative Memory Tawakal, Rayendra; Nazar, Muhammad; Asri, Rahmadi
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.18889

Abstract

A real-time facial detection system for identifying young and old faces has been developed using a combination of Template Matching and Fuzzy Associative Memory (FAM) methods. This study aims to improve accuracy in detecting facial age, particularly from images captured via a webcam. The system was tested across four categories: Old Men, Young Men, Old Women, and Young Women, with 10 image samples per category. The results indicate that the system achieved an accuracy rate of 83%. The Young Men category exhibited the best performance with 100% accuracy, while detection errors occurred in the Old Men and Old Women categories, with a false positive rate of 30%. The system proved to be more effective at detecting young faces than old faces. The primary challenge of this study was managing the complex variation in the patterns of older faces. Thus, further research is required to enhance the system’s performance in detecting older faces and reduce the false positive rate.
Performance Analysis of the Combined K–Nearest Neighbor (KNN) and Principal Component Analysis (PCA) Algorithms in Bird Species Image Classification Tawakal, Rayendra
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 3 (2025): Journal of Advanced Computer Knowledge and Algorithms - July 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i3.22597

Abstract

For most people, learning more about the many types of birds is difficult because there are so many species and many of them look similar in terms of size, color, and shape. Identifying bird species is not an easy task since it requires special skills, time, and money to study each type. Therefore, this study aims to develop an image processing system to classify bird species, especially birds found in the Aceh region. The system uses a combination of the K-Nearest Neighbor (K-NN) algorithm and Principal Component Analysis (PCA). Feature extraction in this study is based on the color and shape of the birds. The K-NN algorithm groups objects by finding the closest distance between them. Meanwhile, PCA is used to reduce the size of the data while keeping most of the important information. Based on the test results, the system achieved an accuracy of 82.50%, a precision of 83.06%, and a recall of 82.50%. This shows that combining K-NN and PCA in classifying bird images can produce better accuracy than using only the K-NN algorithm.Bird Species
Co-Authors Asri, Rahmadi Cahyani, Dewi Regita Eldo, Handry Hezma, Mela Muhammad Nazar Ramadan, Rifqi