## 3 Results
### Machine Learning Classification
Further, almost all models of 18 bands obtained similar classification accuracy as 35 bands, and some gained higher accuracy than 35 bands, except for quadratic SVM, cubic SVM, and bagged trees models. A distinct improvement was observed in quadratic discriminant, where the classification accuracy has increased by 11.72%. In comparison, the other models gained an average of 0.6% increase in accuracy except for linear discriminant and subspace kNN that earned around 2% increases. Furthermore, only quadratic SVM, fine kNN, and subspace discriminant achieved higher accuracy than 18 bands when using 14 bands; however, the increase was only up to 0.88%. The remaining models either had the same classification accuracy as the 18 bands or had a decrease in classification accuracy, with an evident decrease of 2.83% in the cubic SVM model. Nonetheless, fine tree, medium tree, coarse tree, linear discriminant, quadratic discriminant, linear SVM, quadratic SVM, cubic SVM, and bagged trees models of 11 bands outperformed 14 bands in classification accuracy. For instance, cubic SVM obtained 93.56% accuracy at 11 bands compared to 88.79% at 14 bands. Apart from that, two models of 11 bands maintained the same classification accuracy as 14 bands, while the remaining models produced a slightly lower accuracy with an average of 0.43% decrement. For 9 bands, four models such as a medium tree, linear discriminant, coarse Gaussian SVM, and subspace kNN scored better classification accuracy than 11 bands. Meanwhile, fine Gaussian SVM, fine kNN, and weighted kNN procured equal accuracy as 11 bands, while the other models obtained lower accuracy. A significant decrease was observed in cubic SVM, where the classification accuracy decreased sharply from 93.56% in 11 bands to 78.21% in 9 bands, amounting to a 15.35% decrement. In short, the 18, 14, and 11 bands have successfully established more accurate machine learning models compared to 35 bands. It was demonstrated by the average of all accuracy tabulated in Table 4, where the 18, 14, and 11 bands attained a higher average than 35 bands, generally indicating that those bands' models performed better in classifying the I and U seedlings. F-score was computed and presented in Table 6 to determine which model was the best. According to the table, the highest F-score was 95.77% when the linear SVM, fine Gaussian SVM, medium kNN, and cubic kNN models were used with 18 bands. Meanwhile, the quadratic SVM model with 11 bands achieved the second-highest F-score of 95.48%. The 35 and 14 bands, on the other hand, obtained the same highest F-score value of 95.45%. The similar models of 35 and 14 bands that attained that accuracy were fine Gaussian SVM, medium Gaussian SVM, medium kNN, and cubic kNN that demonstrated their \begin{table}
\begin{tabular}{c c} \hline
**Total Significant Bands** & **Significant Bands (nm)** \\ \hline
18 & 826 886 890 894 898 902 906 910 914 918 922 926 930 934 938 942 946 950 \\
9 & 914 922 926 930 934 938 942 946 950 \\
14 & 898 902 906 910 914 918 922 926 930 934 938 942 946 950 \\
11 & 910 914 918 922 926 930 934 938 942 946 950 \\ \hline \end{tabular}
\end{table} 
Table 5: Significant bands that used as inputs of machine learning classifiers. insensitivity to band optimization. Next, 9 bands obtained 95.21% F-score value only for coarse Gaussian SVM, which was considered the highest for 9 bands. Due to the fact that a few models achieved the highest F-score, performance time for each model of each band was presented in Table 7. Among the models of 35 bands that obtained the highest F-score, medium Gaussian SVM has the shortest performance time of 4.8147 s. Meanwhile, for 18 bands, the shortest performance time of 4.0248 s was attained by linear SVM. For 14 bands, medium Gaussian SVM was developed in 3.7307 s and yielded the same highest F-score as 35 bands; however, 14 bands provided 1.084 s faster performance time. Further, the quadratic SVM of 11 bands took 11.6320 to classify the I and U data, which was the longest time consumed.