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PENGENALAN ANGKA PADA CITRA TASBIH DIGITAL MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA) DAN K-NEAREST NEIGHBOR (KNN)
The process of recognizing numbers in digital prayer beads requires an appropriate method for recognizing numbers. The challenge that arises in the prayer beads image is to have a small image display, and the numbers on the digital prayer beads image only display vertical and horizontal lines. This challenge can be a complex problem because the image has a variety of backgrounds and lighting. The purpose of this study is to develop a system to recognize numbers in digital prayer beads images using the Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) methods. The PCA method is used as a feature extraction to reduce the dimensions of the data without significantly reducing the characteristics of the data, thus speeding up the classification and recognition process. The KNN method is a classification for recognizing numbers in digital prayer beads images. The advantage of KNN is that it is strong against noisy training data, effective for a large number of training examples, and simple and easy to implement, so it can solve classification problems accurately. The results show that number recognition in digital prayer beads imagery using the PCA and KNN methods achieves the best performance with an accuracy value of 99%. The PCA and KNN methods have been successfully applied to become a system that can be used for number recognition in digital prayer beads images. Changing the size of digital prayer beads digits by following the previous research's digit size obtained a fixed accuracy of 99% in each size.
Inventory Code | Barcode | Call Number | Location | Status |
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2307000831 | T87388 | T873882023 | Central Library (Referens) | Available but not for loan - Not for Loan |
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