PENGEMBANGAN SISTEM REKOMENDASI BERPAKAIAN MENGGUNAKAN LOCAL BINARY PATTERN DAN K-NEAREST NEIGHBOR DI PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS PELITA BANGSA
DOI:
https://doi.org/10.36080/skanika.v8i2.3584Keywords:
student dress code, Local Binary Pattern (LBP), K-Nearest Neighbor (K-NN), formal clothing detection, image classificationAbstract
The student code of ethics governs behavior, speech, actions, appearance, and dress during their academic journey. At Pelita Bangsa University, many students tend to follow evolving fashion trends, which often conflict with the faculty's dress code that emphasizes wearing formal, collared clothing. This research addresses the issue by developing an image-based detection system to identify whether students wear formal or informal attire. The study utilizes the Local Binary Pattern (LBP) method for feature extraction and the K-Nearest Neighbor (K-NN) method for classification. A total of 130 images were tested, consisting of 70 t-shirts and 60 shirts. The best accuracy was achieved using parameters R=1 and P=8 for LBP and K=1 with Euclidean distance for K-NN, resulting in an average accuracy of 95.16%. The developed system is capable of accurately classifying images of t-shirts and shirts, demonstrating high precision and efficiency in image-based classification. These findings indicate that the application of the Local Binary Pattern (LBP) and K-Nearest Neighbor (K-NN) methods is an effective combination for detecting compliance with student dress code regulations
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