## 4 Feature Selection Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Number of**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Matthews Correlation**} \\  & & & & & **Coefficient** \\ \hline \multirow{3}{*}{\(1\)} & Fresh Beef & 98.039\% & 100\% & 99\% & 98.02\% \\  & Frozen Beef & 96.154\% & 100\% & 98\% & 96.077\% \\  & Rotten Beef & 94.231\% & 97.917\% & 96\% & 92.074\% \\ \hline \multirow{3}{*}{\(2\)} & Fresh Buffalo Meat & 97.959\% & 96.078\% & 97\% & 94.019\% \\  & Frozen Buffalo Meat & 96.078\% & 97.959\% & 97\% & 94.019\% \\  & Rotten Buffalo Meat & 92.308\% & 95.833\% & 94\% & 88.07\% \\ \hline \multirow{3}{*}{\(3\)} & Fresh Goat Meat & 100\% & 98.039\% & 99\% & 98.02\% \\  & Frozen Goat Meat & 98\% & 98\% & 98\% & 98\% \\  & Rotten Goat Meat & 96\% & 96\% & 96\% & 92\% \\ \hline \multirow{3}{*}{\(4\)} & Fresh Horse Meat & 100\% & 98.939\% & 99\% & 98.02\% \\  & Frozen Horse Meat & 96.154\% & 100\% & 98\% & 96.077\% \\  & Rotten Horse Meat & 94.118\% & 95.918\% & 95\% & 90.018\% \\ \hline \multirow{3}{*}{\(5\)} & Fresh Pork & 98\% & 98\% & 98\% & 96\% \\  & Frozen Park & 100\% & 98.039\% & 99\% & 98.02\% \\ \cline{1-1}  & Rotten Park & 94.231\% & 97.917\% & 96\% & 92.074\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validation results using k-NN classification for all features of class (a), class (b), and class (c). the best percentage (D). This model performs well in classifying the image of rotten meat; however, the lowest level of the image is associated with the rotten horse meat texture (D). Table 7 shows how well the three models compare in terms of the performance measurement results of the k-NN algorithm, the Haar Wavelet algorithm, and the GLCM for the three classes (fresh, frozen, and rotten). (A) k-NN, (B) Wavelet Haar, and (C) GLCM are assumed. First, for the fresh meat texture image class, the percentages are (A) 97% to 99%; (B) 89.25% to 89.96%; and (C) 51.52% to 183.21%. The percentage of frozen meat in both images is 97% to 99% in (A), 87.56% to 88.25% in (B), and 78.25% to 185.75% in (C), all three with percentages ranging from 94%. For rotten meat images, the percentages are up to 96% in (A), 86.26% to 87.97% in (B), and 34.62% to 115.79% in (C). The performance measurement comparison of k-nearest neighbor algorithm, Haar Wavelet algorithm, and gray-level co-occurrence matrix can be seen in Table 7 below:  
The performance results of algorithms (A), (B), and (C) relating to fresh, frozen, and rotten meat images provide valuable insights into the performance of algorithm models in determining the condition of meat based on texture images. Fresh Meat: (A) The k-NN algorithm gives the best results on texture images of fresh goat meat, horse meat, and beef. It also has the most significant standout score (99%). This shows how well the k-NN algorithm performs in classifying fresh meat images. k-NN performs the worst in the fresh buffalo meat texture image, with a score of 97%. Meanwhile, (B) shows the fresh pork texture image, which demonstrates that the Haar Wavelet algorithm performs worse (89.25%) than k-NN. However, when it comes to fresh beef texture images, our algorithm performs well (89.96%). For the fresh pork texture image (C), the GLCM method gives consistent results, with the lowest application percentage (51.52%). However, GLCM data can still be used for classification, although GLCM has the largest fresh goat meat texture images (183.21%). Of the three algorithms studied, the GLCM, Wavelet Haar, and k-NN models perform the best in categorizing fresh meat texture images into meat categories. This shows that the k-NN technique is more suitable for applications that manage fresh meat texture images. Frozen Meat: (A) Using the k-NN method, the frozen pork texture image gives the most prominent value (99%), while the texture image of frozen buffalo meat yields the lowest result (97%). This shows how well the k-NN algorithm performs in classifying frozen meat images.