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Putri Shania Bilqis
STMIK Mikroskil, Medan
Computer Science & IT

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Identifikasi Sampah Berdasarkan Tekstur Dengan Metode GLCM dan GLRLM Menggunakan Improved KNN Kristian Telaumbanua; Sudarto Sudarto; Fitri Butar-Butar; Putri Shania Bilqis
Explorer Vol 1 No 2 (2021): Juli 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (384.894 KB) | DOI: 10.47065/explorer.v1i2.94

Abstract

Inorganic garbage is a type of garbage which takes a long time to decompose naturally, and it is important to recycle this type of garbage to avoid it stacking up in the environment. Before the recycle process, the inorganic garbage will be grouped which needs an identification of its form of material. The digital image processing can be used to create a system which could identify the form of material the inorganic garbage is by analyzing the features. In this research, the feature that will be used is the texture feature. The texture feature will be extracted using the Gray Level Co-Occurrence Matrix, and Gray Level Run Length Matrix method. And for the material of garbage identification will use the Improved KNN classification method. The results of the test by using 50 images as data testing from 5 different material of garbage which is cardboard, glass, metal, paper, and plastic type of garbage have the highest mean of accuracy 90,4% by using the GLRLM method with 135° angle of extraction. Meanwhile the accuracy when combining the extraction methods, which is adding up the value of GLCM and GLRLM, have the highest mean of accuracy 88% with 0° angle of extraction
Co-Authors Fitri Butar-Butar Kristian Telaumbanua Sudarto Sudarto