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All Journal Xplore: Journal of Statistics
Mohamad Rafi
Departemen Kimia, IPB University
Decision Sciences, Operations Research & Management Engineering Mathematics

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Metode SVM untuk Klasifikasi Enam Tumbuhan Zingiberaceae Menggunakan Variabel Terpilih Hasil Algoritma Genetika Triyani Oktaria; Utami Dyah Syafitri; Mohamad Rafi; Farit M Afendi
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.458 KB) | DOI: 10.29244/xplore.v10i2.783

Abstract

Ginger, red ginger, emprit ginger, elephant ginger, red galangal and white galangal are known to have similar shapes and uses, especially those that are packaged in powder form. In this study, UV-Vis spectrum 200nm-700nm were used as a source of data from chemical compound contain in those plants for classification of the six plants. In this research, the support vector machine (SVM) classification method was used to classify the six plants. Another goal of this study was to identify the wavelengths which give more information about the chemical compound of the plants. The preprocessing procedure was implemented by construction of a genetic algorithm. There were four parameters in the genetic algorithm were set namely population size, crossover probability, mutation, and generation probability. The mutation and the population size influenced significantly the results of SVM. The best result was given by probability of mutation was 10 and population size was 30. The SVM model was better than the SVM model without preprocessing procedure.
Co-Authors Farit M Afendi Triyani Oktaria Utami Dyah Syafitri