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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
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 | 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 ., Riche Adinda ihdina Surbakti Aditya Rizqy Bakhtiar Anelka Winata Perangin Angin Cindy Jumaida Daud Sihar Surung Putra Anugerah Daeli Ekun Melki Mozart Siregar Erlanie Sufarnap Erlanie Sufarnap Erlanie Sufarnap Fadly Robbani Tua Lumban Tobing Fince Aprilman Waruwu Fitri Butar-Butar Florida Nirma Sanny Damanik Ginting, Tri Wulandari Haloho, Yohana Oktavia Irpan Pardosi Irvan Budi Butar Butar Jeni, Mutiara Joitanata Saragi Krissanty Monita Br Aritonang Kristian Telaumbanua Manik, Elonsius Marbun, Sartika Br Marpaung, Jeni Rifkayani Martina Grecia Wijaya Nadeak Mawar Adelia Br Marpaung Mirza Ilhami Mirza Ilhami Mirza Ilhami Muhammad Abdillah Helmi Rasyid Muhammad Alif Muhammad Ridho Harahap Muhammad Zarlis Mutiara Dini Simargolang Nadyta Situmorang Nainggolan, Tiodoras Nainggolan Tiodoras Nanda Ardian Nasrul Efendi Natalis Pardamean Situmorang Nopentri Panjaitan Obert, Steven Pahala Sirait Putri Shania Bilqis Riri Riri Roni Yunis Roni Yunis Roni Yunis Salsabila, Zulpa Salsalina Br Sembiring Samuel Rizky Ananda Sitindaon Sebrina Marsya Simanullang Shesyl Yulia Noor Diwani Sianipar, Aldo Exaudi Sitanggang, Santa Eliza sitio, Pourman Sophya Hadini Marpaung Sugianta Ovinus Ginting Sugianta Ovinus Ginting Sugianta Ovinus Ginting Umar Al Faruq Siregar Al Faruq Siregar Victor Samuel Saputra Simorangkir Viorella Natalya Simanjuntak Vuza Roinda Br Sihombing Wandra Alfaiz Wijaya, Viona Guanita Wilfy Hardi Napitupulu Yessi Claudia Sitepu Yohana Adelia Nugroho Yohana Angeliana Sianturi Zalsa Nabilla Rosa Dalimunthe