Waste classification using support vector machine with SIFT-PCA feature extraction
Population growth and changes in public consumption patterns cause increases in the volume, types and characteristics of the waste. This increase requires waste management effort. One of the efforts that can be performed is by separating waste into several types. Upon waste separation, the waste can...
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30353/ http://umpir.ump.edu.my/id/eprint/30353/1/Waste%20classification%20using%20support%20vector1.pdf |
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| author | Puspaningrum, Adita Putri Endah, Sukmawati Nur Sasongko, Priyo Sidik Kusumaningrum, Retno ., Khadijah ., Rismiyati Ernawan, Ferda |
| author_facet | Puspaningrum, Adita Putri Endah, Sukmawati Nur Sasongko, Priyo Sidik Kusumaningrum, Retno ., Khadijah ., Rismiyati Ernawan, Ferda |
| author_sort | Puspaningrum, Adita Putri |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Population growth and changes in public consumption patterns cause increases in the volume, types and characteristics of the waste. This increase requires waste management effort. One of the efforts that can be performed is by separating waste into several types. Upon waste separation, the waste can be proceeded to the waste recycling process. Current technological advances have supported automatic waste sorting so that the waste sorting process is easier and faster to do. This research proposes waste image classification to support automatic waste sorting using Support Vector Machine (SVM) classification algorithm and SIFT-PCA (Scale Invariant Feature Transform - Principal Component Analysis) feature extraction. SIFT-PCA is a combination of SIFT to extract feature data and PCA to reduce the dimensionality of the resulting feature data. The data used in this research is Trashnet datasets. The performance of the SVM classification using SIFT feature is compared with the similar algorithm with SIFT-PCA combined feature. The experimental results show that classification using SIFT feature extraction achieve accuracy of 62%. This accuracy is higher than experiment with using SIFT-PCA feature extraction. |
| first_indexed | 2025-11-15T02:57:58Z |
| format | Conference or Workshop Item |
| id | ump-30353 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:57:58Z |
| publishDate | 2020 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-303532020-12-29T03:33:05Z http://umpir.ump.edu.my/id/eprint/30353/ Waste classification using support vector machine with SIFT-PCA feature extraction Puspaningrum, Adita Putri Endah, Sukmawati Nur Sasongko, Priyo Sidik Kusumaningrum, Retno ., Khadijah ., Rismiyati Ernawan, Ferda QA75 Electronic computers. Computer science Population growth and changes in public consumption patterns cause increases in the volume, types and characteristics of the waste. This increase requires waste management effort. One of the efforts that can be performed is by separating waste into several types. Upon waste separation, the waste can be proceeded to the waste recycling process. Current technological advances have supported automatic waste sorting so that the waste sorting process is easier and faster to do. This research proposes waste image classification to support automatic waste sorting using Support Vector Machine (SVM) classification algorithm and SIFT-PCA (Scale Invariant Feature Transform - Principal Component Analysis) feature extraction. SIFT-PCA is a combination of SIFT to extract feature data and PCA to reduce the dimensionality of the resulting feature data. The data used in this research is Trashnet datasets. The performance of the SVM classification using SIFT feature is compared with the similar algorithm with SIFT-PCA combined feature. The experimental results show that classification using SIFT feature extraction achieve accuracy of 62%. This accuracy is higher than experiment with using SIFT-PCA feature extraction. IEEE 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30353/1/Waste%20classification%20using%20support%20vector1.pdf Puspaningrum, Adita Putri and Endah, Sukmawati Nur and Sasongko, Priyo Sidik and Kusumaningrum, Retno and ., Khadijah and ., Rismiyati and Ernawan, Ferda (2020) Waste classification using support vector machine with SIFT-PCA feature extraction. In: IEEE 4th International Conference on Informatics and Computational Sciences (ICICoS 2020) , 10-11 November 2020 , Semarang, Indonesia. pp. 1-6.. ISBN 978-1-7281-9526-1 (Published) https://doi.org/10.1109/ICICoS51170.2020.9298982 https://doi.org/10.1109/ICICoS51170.2020.9298982 |
| spellingShingle | QA75 Electronic computers. Computer science Puspaningrum, Adita Putri Endah, Sukmawati Nur Sasongko, Priyo Sidik Kusumaningrum, Retno ., Khadijah ., Rismiyati Ernawan, Ferda Waste classification using support vector machine with SIFT-PCA feature extraction |
| title | Waste classification using support vector machine with SIFT-PCA feature extraction |
| title_full | Waste classification using support vector machine with SIFT-PCA feature extraction |
| title_fullStr | Waste classification using support vector machine with SIFT-PCA feature extraction |
| title_full_unstemmed | Waste classification using support vector machine with SIFT-PCA feature extraction |
| title_short | Waste classification using support vector machine with SIFT-PCA feature extraction |
| title_sort | waste classification using support vector machine with sift-pca feature extraction |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/30353/ http://umpir.ump.edu.my/id/eprint/30353/ http://umpir.ump.edu.my/id/eprint/30353/ http://umpir.ump.edu.my/id/eprint/30353/1/Waste%20classification%20using%20support%20vector1.pdf |