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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Miftahuddin Fahmi
Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Computer Science & IT

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Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image Miftahuddin Fahmi; Anton Yudhana; Sunardi Sunardi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2819

Abstract

Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies.
Co-Authors Anton Yudhana Sunardi Sunardi