Seleksi Fitur dengan Artificial Bee Colony untuk Optimasi Klasifikasi Data Teh menggunakan Support Vector Machine

Abstract

Tea quality can be recognized through the aroma it produces. Tea classification using e-nose generally only detects aroma using a general gas sensor. However, redundancy of sensor features can cause a decreasing in the system performance. Therefore we need a system that can select features so the classification performance becomes optimal. A software system of feature selection was built to optimize classification performance. Input data for the system is e-nose sensor response to 3 black tea qualities. The features are sensors on the e-nose instrument. Feature selection is implemented using wrapper approach, ABC algorithm is used for feature selection, then the selected features are evaluated by SVM classification. The results of the ABC-SVM system are then compared with the SVM only system. The results showed that from 12 e-nose sensors, sensors that most characterized black tea quality were TGS 2600, TGS 813, TGS 825, TGS 2602, TGS 2611, TGS 832, TGS 2612, TGS 2620 and TGS 822. Meanwhile, MQ-7, TGS 826 and TGS 2610 sensors are redundant in the system because the gas detected by the 3 sensors can be represented by other sensors. With the reduction in features to 9, the classification accuracy performance increased by 16.7%.