PERBANDINGAN METODE PCA dan KPCA UNTUK KLASIFIKASI TEH MENGGUNAKAN ELECTRONIC NOSE; THE COMPARISON OF PCA and KPCA METHOD FOR CLASSIFICATION OF TEA USING ELECTRONIC NOSE

Thohari, Anasir Y, Triyogatama Wahyu Widodo

2015 | Skripsi | FMIPA

The classification process is a step to analyze data of feature extraction result from digital output ie digital pattern (fingerprint) that is generated by electronic nose sensor. The data which are generated must be analyzed and interpreted in order to give usefull information. Multivariate Data Analysis (MDA) method is one of methods that is used to electronic nose (E-Nose). In the classification process, the use of MDA is very important for classifying the signal pattern in high dimension. MDA method that easy and often to used is Principal Component Analysis (PCA). This method is useful to reduce the dimension and classify samples based on class. PCA is used to process data with linear pattern while the data with nonliear pattern can use Kernel Principal Component Analysis (KPCA) method. The KPCA method can lay the data into higher dimension then processed using PCA standard. In this research, PCA and KCPA method are successfully classify 1, 2 and 3 kind of samples tea into three different classes. The result of classification using the PCA method obtained percent of total covariance value is 93.2951%. The classification results using the KPCA method obtained percent of total covariance value is 95.1796%. From the results of classification can be concluded that the KPCA method has better results than ordinary PCA.

Kata Kunci : MDA; PCA; KPCA; electronic nose