Face Race Classification using ResNet-152 and DenseNet- 121
ABSTRAK Penelitian ini bertujuan untuk membandingkan hasil kinerja arsitektur ResNet-152 dan DenseNet-121 dalam mengklasifikasikan wajah berdasarkan ras. Ras yang diklasifikasikan terdiri dari 4 kelas: Putih, Hitam, India, dan Asia. Penelitian ini menggunakan parameter batch_size 32, optimizer, dan...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | Indonesian |
| Published: |
Teknik Elektro Institut Teknologi Nasional Bandung
2024-07-01
|
| Series: | Jurnal Elkomika |
| Subjects: | |
| Online Access: | https://ejurnal.itenas.ac.id/index.php/elkomika/article/view/11936 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846128001708720128 |
|---|---|
| author | JASMAN PARDEDE SYAFIQ SALIM KLEB |
| author_facet | JASMAN PARDEDE SYAFIQ SALIM KLEB |
| author_sort | JASMAN PARDEDE |
| collection | DOAJ |
| description | ABSTRAK
Penelitian ini bertujuan untuk membandingkan hasil kinerja arsitektur ResNet-152 dan DenseNet-121 dalam mengklasifikasikan wajah berdasarkan ras. Ras yang diklasifikasikan terdiri dari 4 kelas: Putih, Hitam, India, dan Asia. Penelitian ini menggunakan parameter batch_size 32, optimizer, dan learning rate untuk meningkatkan kinerja model. Dua optimizer yang dibandingkan, yaitu Adam Optimizer dan Nadam Optimizer. Nilai learning rate yang dipertimbangkan adalah 0,0001 dan 0,001. Berdasarkan hasil percobaan klasifikasi wajah berdasarkan ras, baik arsitektur ResNet-152 maupun DenseNet-121 mencapai kinerja akurasi dan recall yang sama yaitu 0,788. Kinerja terbaik untuk presisi dan f1-score adalah ResNet-152. ResNet-152 memiliki presisi dan f1-score yang lebih baik masingmasing sebesar 0,376% dan 0,252%, jika dibandingkan dengan DenseNet-121. Oleh karena itu, ResNet-152 memiliki kinerja terbaik jika dibandingkan DenseNet-121 dalam klasifikasi wajah berbasis ras.
Kata kunci: kinerja, ResNet-152, DenseNet-121, wajah, klasifikasi
ABSTRACT
This study aims to compare the performance results of the ResNet-152 and DenseNet-121 architectures for classifying faces based on race. The classified race consists of 4 classes i.e.: White, Black, Indian, and Asian. The study used a batch size of 32, an optimizer, and a learning rate to improve model formation performance. Two optimizers are being compared, namely Adam Optimizer and Nadam Optimizer. The learning rate values considered are 0.0001 and 0.001. Based on the results of facial classification experiments based on race, both the ResNet-152 and DenseNet-121 architectures achieve the same accuracy and recall performance, namely 0.788. The best performance for precision and f1-score is ResNet-152. The ResNet-152 has better Precision and F1-Score of 0.376% and 0.252% respectively, in case it’s compared to DenseNet-121. Hence, it can be inferred that ResNet-152 surpasses DenseNet-121 in delivering superior performance outcomes for racial-based facial classification.
Keywords: performance, ResNet-152, DenseNet-121, face, classification |
| format | Article |
| id | doaj-art-e74a578c7e4243c3abfb5017dea8daa9 |
| institution | Kabale University |
| issn | 2338-8323 2459-9638 |
| language | Indonesian |
| publishDate | 2024-07-01 |
| publisher | Teknik Elektro Institut Teknologi Nasional Bandung |
| record_format | Article |
| series | Jurnal Elkomika |
| spelling | doaj-art-e74a578c7e4243c3abfb5017dea8daa92024-12-11T07:54:34ZindTeknik Elektro Institut Teknologi Nasional BandungJurnal Elkomika2338-83232459-96382024-07-0112310.26760/elkomika.v12i3.7983307Face Race Classification using ResNet-152 and DenseNet- 121JASMAN PARDEDE0SYAFIQ SALIM KLEB1Institut Teknologi Nasional BandungInstitut Teknologi Nasional BandungABSTRAK Penelitian ini bertujuan untuk membandingkan hasil kinerja arsitektur ResNet-152 dan DenseNet-121 dalam mengklasifikasikan wajah berdasarkan ras. Ras yang diklasifikasikan terdiri dari 4 kelas: Putih, Hitam, India, dan Asia. Penelitian ini menggunakan parameter batch_size 32, optimizer, dan learning rate untuk meningkatkan kinerja model. Dua optimizer yang dibandingkan, yaitu Adam Optimizer dan Nadam Optimizer. Nilai learning rate yang dipertimbangkan adalah 0,0001 dan 0,001. Berdasarkan hasil percobaan klasifikasi wajah berdasarkan ras, baik arsitektur ResNet-152 maupun DenseNet-121 mencapai kinerja akurasi dan recall yang sama yaitu 0,788. Kinerja terbaik untuk presisi dan f1-score adalah ResNet-152. ResNet-152 memiliki presisi dan f1-score yang lebih baik masingmasing sebesar 0,376% dan 0,252%, jika dibandingkan dengan DenseNet-121. Oleh karena itu, ResNet-152 memiliki kinerja terbaik jika dibandingkan DenseNet-121 dalam klasifikasi wajah berbasis ras. Kata kunci: kinerja, ResNet-152, DenseNet-121, wajah, klasifikasi ABSTRACT This study aims to compare the performance results of the ResNet-152 and DenseNet-121 architectures for classifying faces based on race. The classified race consists of 4 classes i.e.: White, Black, Indian, and Asian. The study used a batch size of 32, an optimizer, and a learning rate to improve model formation performance. Two optimizers are being compared, namely Adam Optimizer and Nadam Optimizer. The learning rate values considered are 0.0001 and 0.001. Based on the results of facial classification experiments based on race, both the ResNet-152 and DenseNet-121 architectures achieve the same accuracy and recall performance, namely 0.788. The best performance for precision and f1-score is ResNet-152. The ResNet-152 has better Precision and F1-Score of 0.376% and 0.252% respectively, in case it’s compared to DenseNet-121. Hence, it can be inferred that ResNet-152 surpasses DenseNet-121 in delivering superior performance outcomes for racial-based facial classification. Keywords: performance, ResNet-152, DenseNet-121, face, classificationhttps://ejurnal.itenas.ac.id/index.php/elkomika/article/view/11936performanceresnet-152densenet-121faceclassification |
| spellingShingle | JASMAN PARDEDE SYAFIQ SALIM KLEB Face Race Classification using ResNet-152 and DenseNet- 121 Jurnal Elkomika performance resnet-152 densenet-121 face classification |
| title | Face Race Classification using ResNet-152 and DenseNet- 121 |
| title_full | Face Race Classification using ResNet-152 and DenseNet- 121 |
| title_fullStr | Face Race Classification using ResNet-152 and DenseNet- 121 |
| title_full_unstemmed | Face Race Classification using ResNet-152 and DenseNet- 121 |
| title_short | Face Race Classification using ResNet-152 and DenseNet- 121 |
| title_sort | face race classification using resnet 152 and densenet 121 |
| topic | performance resnet-152 densenet-121 face classification |
| url | https://ejurnal.itenas.ac.id/index.php/elkomika/article/view/11936 |
| work_keys_str_mv | AT jasmanpardede faceraceclassificationusingresnet152anddensenet121 AT syafiqsalimkleb faceraceclassificationusingresnet152anddensenet121 |