Pengenalan Pola untuk Identifikasi Jenis Kain Tenun Sibolga Menggunakan Metode Principal Component Analysis dan K-Nearest Neighbours
-
Dinara Sarvina Piliang
*
Universitas Islam Negeri Sumatera Utara, Medan,
Indonesia
- Sriani Sriani Universitas Islam Negeri Sumatera Utara, Medan, Indonesia (*) Corresponding Author
Abstract
Sibolga woven fabric is one of Indonesia's traditional fabrics that has high artistic and cultural value. Sibolga woven fabric motifs are usually inspired by nature, such as flora, fauna, and local culture. Sibolga woven fabric and is famous for its unique and diverse motifs. Sibolga woven fabric motifs are usually inspired by nature, such as flora, fauna, and local culture. Manually classifying the types of Sibolga woven fabrics is a time-consuming process and requires special expertise. This causes the complexity of motifs and color variations found in Sibolga woven fabrics. Therefore, a system is needed that can classify the types of Sibolga woven fabrics automatically and accurately. The method used in this study is the feature extraction method, which is to extract new features from the initial data set. One of the feature extraction techniques that can be used is Principal Component Analysis (PCA). The use of PCA can be used to reduce the lower dimensions of data with very little risk of information loss. The study also uses KNN because this algorithm is used effectively to classify fabrics based on these key features, thereby reducing computational complexity and improving accuracy. The results of the classification of sibolga woven fabrics using the K-NN algorithm by utilizing the feature extraction process using PCA obtained an accuracy of 72%. It can be concluded that the classification of sibolga woven fabrics using an algorithm using the K-Nearest Neighbours (K-NN) algorithm can be done by extracting features using the PCA method (Pricipal Component Analysis).
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