Optimasi Klasifikasi Parasit Malaria Dengan Metode LVQ, SVM dan Backpropagation

• Main Authors: Jani Kusanti, Tri Irianto Tjendrowarsono
• Format: Article
• Language: English
• Published: P3M, Politeknik Negeri Cilacap 2021-03-01
• Series: Infotekmesin: Media Komunikasi Ilmiah Politeknik Cilacap
• Subjects: lvq; svm; backpropagation; malaria
• Online Access: https://ejournal.pnc.ac.id/index.php/infotekmesin/article/view/483

The use of the classification method affects the accuracy of the test results. The accuracy of the classification method is affected by the number of classes in the image. The number of classes and the amount of data should be considered when making decisions in choosing a classification method. This study used 600 data, which were divided into 510 training data and 90 test data. The number of classes tested is 12 classes with the number of initial features used by 22 features. The characteristics used in the test consist of shape characteristics and texture characteristics. The classification methods used in this study are LVQ, Backpropagation, and SVM. The data has 22 features or attributes that are the result of texture and shape feature extraction. Texture features are energy 0o, energy 45o, energy 90o, energy 135o, entropy 0o, entropy 45o, entropy 90o, entropy 135o, contrast 0o, contrast 45o, contrast 90o, contrast 135o, homogeneity 00, homogeneity 45o, homogeneity 90o, homogeneity 135o, correlation 0o, Correlation 45o, correlation 90o, correlation 135o, features of área and perimeter shape. The test results using the Backpropagation method obtained 89.7% results, using the LVQ method obtained 77.78% results, and the SVM method obtained 99.1% results.