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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Elizabeth Fiona Hartono
Universitas Multi Data Palembang
Computer Science & IT

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Klasifikasi Jenis Plastik HDPE, LDPE, dan PS Berdasarkan Tekstur Menggunakan Metode Support Vector Machine Elizabeth Fiona Hartono; Nur Rachmat
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 2 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v9i2.2470

Abstract

Plastic is one type of large molecule formed by the polymerization process. Many plastics circulate and are often found, such as High-Density Polyethylene (HDPE), Low-Density Polyethlene (LDPE), and Polystyrene (PS) plastic types. To classify HDPE, LDPE, and PS plastic types based on texture characteristic extraction, the Local Binary Pattern (LBP) and Support Vector Machine (SVM) methods can be used. This study uses a manually photographed HDPE, LDPE, and PS plastic types of images dataset, and there are 180 images consisting of 126 training data and 54 test data with percentage of training data is 70% and testing data is 30% size 450450 pixels. The image dataset is converted from an RGB image into a grayscale image for the LBP feature extraction process. After removing texture characteristics, the classification process uses the SVM method with RBF kernels, linear, polynomial. The performance of the LBP features and SVM methods gives the highest accuracy average value of 95,05%, precision of 92,78%, and recall of 92,58% with polynomial kernel.
Co-Authors Nur Rachmat