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All Journal Tensor: Pure and Applied Mathematics Journal
Green Kenny Sarimanella
UNIVERSITAS PATTIMURA
Computer Science & IT Mathematics

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Klasifikasi Citra Tekstur Daging Sapi, Kambing, dan Babi Menggunakan Ekstraksi Fitur Wavelet Haar dan Symlet Berbasis Support Vector Machine Green Kenny Sarimanella; Francis Yunito Rumlawang; Harmanus Batkunde; Meilin Imelda Tilukay; A. Z. Wattimena
Tensor: Pure and Applied Mathematics Journal Vol 7 No 1 (2026): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol7iss1pp59-66

Abstract

Meat is one of the animal protein sources widely consumed by the public; however, distinguishing different types of meat visually is often difficult because they have very similar textures. This study applies the Support Vector Machine (SVM) method with feature extraction based on Haar Wavelet and Symlet Wavelet (Sym4) to classify texture images of beef, goat meat, and pork. The dataset consisted of 1200 digital images processed through resizing, grayscale conversion, and normalization stages. Feature extraction was performed using the Discrete Wavelet Transform (DWT) to obtain statistical texture features. The classification process employed the Radial Basis Function (RBF) kernel with a multiclass classification approach. The results showed that the Haar Wavelet achieved an accuracy of 96.67%, while the Symlet Wavelet (Sym4) achieved 94.17%. These findings indicate that the combination of wavelet methods and SVM is effective for automatic and objective meat type identification
Co-Authors A. Z. Wattimena Batkunde, Harmanus Meilin Imelda Tilukay RUMLAWANG, FRANCIS YUNITO