Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks
Environmental scientists often use birds to understand ecosystems because they are sensitive to environmental changes, but few experts are available. To make it easier to recognize bird species, an automatic system that can classify bird species is needed. There are lots of models to choose from, bu...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Indonesian Institute of Sciences
2022-12-01
|
| Series: | Jurnal Elektronika dan Telekomunikasi |
| Subjects: | |
| Online Access: | https://www.jurnalet.com/jet/article/view/503 |
| _version_ | 1797974357223407616 |
|---|---|
| author | Aldi Jakaria Hilman Ferdinandus Pardede |
| author_facet | Aldi Jakaria Hilman Ferdinandus Pardede |
| author_sort | Aldi Jakaria |
| collection | DOAJ |
| description | Environmental scientists often use birds to understand ecosystems because they are sensitive to environmental changes, but few experts are available. To make it easier to recognize bird species, an automatic system that can classify bird species is needed. There are lots of models to choose from, but some models require very high computational data when training data, reducing training time can result in less wasted electrical energy so that it can have a good effect on the environment. For this reason, it is necessary to test a model that has a small complexity in training time, whether it can produce good performance. Based on the number of neural network models available, this study will classify using the EfficientNet, EfficientNetV2, MobileNet, MobileNetV2, and NasnetMobile models to determine whether these models can have good performance. From the research results, all the models tested have good performance with an accuracy between 95% - 97%. The MobileNetV2 model has the less efficiency with the smallest training time while maintaining good performance. |
| first_indexed | 2024-04-11T04:18:45Z |
| format | Article |
| id | doaj.art-ae213f3132664b9785e9d91921ca7e60 |
| institution | Directory Open Access Journal |
| issn | 1411-8289 2527-9955 |
| language | English |
| last_indexed | 2024-04-11T04:18:45Z |
| publishDate | 2022-12-01 |
| publisher | Indonesian Institute of Sciences |
| record_format | Article |
| series | Jurnal Elektronika dan Telekomunikasi |
| spelling | doaj.art-ae213f3132664b9785e9d91921ca7e602022-12-31T06:09:59ZengIndonesian Institute of SciencesJurnal Elektronika dan Telekomunikasi1411-82892527-99552022-12-01222879410.55981/jet.503258Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural NetworksAldi Jakaria0Hilman Ferdinandus Pardede1Fakultas Teknologi Informasi, Universitas Nusa MandiriResearch Center for Data and Information Sciences, National Research and Innovation AgencyEnvironmental scientists often use birds to understand ecosystems because they are sensitive to environmental changes, but few experts are available. To make it easier to recognize bird species, an automatic system that can classify bird species is needed. There are lots of models to choose from, but some models require very high computational data when training data, reducing training time can result in less wasted electrical energy so that it can have a good effect on the environment. For this reason, it is necessary to test a model that has a small complexity in training time, whether it can produce good performance. Based on the number of neural network models available, this study will classify using the EfficientNet, EfficientNetV2, MobileNet, MobileNetV2, and NasnetMobile models to determine whether these models can have good performance. From the research results, all the models tested have good performance with an accuracy between 95% - 97%. The MobileNetV2 model has the less efficiency with the smallest training time while maintaining good performance.https://www.jurnalet.com/jet/article/view/503efficientnetefficientnetv2mobilenetmobilenetv2nasnetmobile |
| spellingShingle | Aldi Jakaria Hilman Ferdinandus Pardede Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks Jurnal Elektronika dan Telekomunikasi efficientnet efficientnetv2 mobilenet mobilenetv2 nasnetmobile |
| title | Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks |
| title_full | Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks |
| title_fullStr | Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks |
| title_full_unstemmed | Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks |
| title_short | Comparison of Classification of Birds Using Lightweight Deep Convolutional Neural Networks |
| title_sort | comparison of classification of birds using lightweight deep convolutional neural networks |
| topic | efficientnet efficientnetv2 mobilenet mobilenetv2 nasnetmobile |
| url | https://www.jurnalet.com/jet/article/view/503 |
| work_keys_str_mv | AT aldijakaria comparisonofclassificationofbirdsusinglightweightdeepconvolutionalneuralnetworks AT hilmanferdinanduspardede comparisonofclassificationofbirdsusinglightweightdeepconvolutionalneuralnetworks |