Garbage Classification Using Ensemble DenseNet169
Garbage is a big problem for the sustainability of the environment, economy, and society, where the demand for waste increases along with the growth of society and its needs. Where in 2019 Indonesia was able to produce 66-67 million tons of waste, which is an increase from the previous year of 2 to...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2021-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/3673 |
| _version_ | 1797329275497480192 |
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| author | Ulfah Nur Oktaviana Yufis Azhar |
| author_facet | Ulfah Nur Oktaviana Yufis Azhar |
| author_sort | Ulfah Nur Oktaviana |
| collection | DOAJ |
| description | Garbage is a big problem for the sustainability of the environment, economy, and society, where the demand for waste increases along with the growth of society and its needs. Where in 2019 Indonesia was able to produce 66-67 million tons of waste, which is an increase from the previous year of 2 to 3 million tons of waste. Waste management efforts have been carried out by the government, including by making waste sorting regulations. This sorting is known as 3R (reduce, reuse, recycle), but most people do not sort their waste properly. In this study, a model was developed that can sort out 6 types of waste including: cardboard, glass, metal, paper, plastic, trash. The model was built using the transfer learning method with a pretrained model DenseNet169. Where the optimal results are shown for the classes that have been oversampling previously with an accuracy of 91%, an increase of 1% compared to the model that has an unbalanced data distribution. The next model optimization is done by applying the ensemble method to the four models that have been oversampled on the training dataset with the same architecture. This method shows an increase of 3% to 5% while the final accuracy on the test of dataset is 96%. |
| first_indexed | 2024-03-08T07:03:23Z |
| format | Article |
| id | doaj.art-142e6558335f4ae9addc171dc24c0293 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:03:23Z |
| publishDate | 2021-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-142e6558335f4ae9addc171dc24c02932024-02-03T05:15:34ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602021-12-01561207121510.29207/resti.v5i6.36733673Garbage Classification Using Ensemble DenseNet169Ulfah Nur Oktaviana0Yufis Azhar1Universitas Muhammadiyah MalangUniversitas Muhammadiyah MalangGarbage is a big problem for the sustainability of the environment, economy, and society, where the demand for waste increases along with the growth of society and its needs. Where in 2019 Indonesia was able to produce 66-67 million tons of waste, which is an increase from the previous year of 2 to 3 million tons of waste. Waste management efforts have been carried out by the government, including by making waste sorting regulations. This sorting is known as 3R (reduce, reuse, recycle), but most people do not sort their waste properly. In this study, a model was developed that can sort out 6 types of waste including: cardboard, glass, metal, paper, plastic, trash. The model was built using the transfer learning method with a pretrained model DenseNet169. Where the optimal results are shown for the classes that have been oversampling previously with an accuracy of 91%, an increase of 1% compared to the model that has an unbalanced data distribution. The next model optimization is done by applying the ensemble method to the four models that have been oversampled on the training dataset with the same architecture. This method shows an increase of 3% to 5% while the final accuracy on the test of dataset is 96%.http://jurnal.iaii.or.id/index.php/RESTI/article/view/3673densenet169ensembleoversamplinggarbage |
| spellingShingle | Ulfah Nur Oktaviana Yufis Azhar Garbage Classification Using Ensemble DenseNet169 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) densenet169 ensemble oversampling garbage |
| title | Garbage Classification Using Ensemble DenseNet169 |
| title_full | Garbage Classification Using Ensemble DenseNet169 |
| title_fullStr | Garbage Classification Using Ensemble DenseNet169 |
| title_full_unstemmed | Garbage Classification Using Ensemble DenseNet169 |
| title_short | Garbage Classification Using Ensemble DenseNet169 |
| title_sort | garbage classification using ensemble densenet169 |
| topic | densenet169 ensemble oversampling garbage |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/3673 |
| work_keys_str_mv | AT ulfahnuroktaviana garbageclassificationusingensembledensenet169 AT yufisazhar garbageclassificationusingensembledensenet169 |