CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach
Stunting, a condition characterised by short stature, is a growth disorder caused by chronic malnutrition, which often begins in the womb. Children affected by stunting usually show different physical and cognitive characteristics compared to their peers. Research shows that these physical differenc...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2025-01-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6068 |
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| author | Yunidar Yunidar Y Yusni N Nasaruddin Fitri Arnia |
| author_facet | Yunidar Yunidar Y Yusni N Nasaruddin Fitri Arnia |
| author_sort | Yunidar Yunidar |
| collection | DOAJ |
| description | Stunting, a condition characterised by short stature, is a growth disorder caused by chronic malnutrition, which often begins in the womb. Children affected by stunting usually show different physical and cognitive characteristics compared to their peers. Research shows that these physical differences can also be observed in facial features. Because faces provide important information and are commonly studied in digital image processing, in this study, we will compare the facial image classification performance of stunted children versus normal children using various Convolutional Neural Network (CNN) architectures. The evaluated architectures include MobileNetV2, InceptionV3, VGG19, ResNet18, EfficientNetB0, and AlexNet. To improve the learning process, augmentation techniques with Haar cascade and Gaussian filters were applied so that the data set increased from 1,000 to 6,000 images. After adding the dataset, training is carried out with an early stop approach to minimise overfitting. The main aim of this research is to identify the CNN model that is most effective in differentiating facial images of stunted children from normal children. The results show that the EfficientNetB0 architecture outperforms other models, achieving 100% accuracy. Early stopping has been shown to improve training efficiency and help prevent overfitting. |
| format | Article |
| id | doaj-art-eac81407dd9746b999169a0f633a6bdf |
| institution | DOAJ |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-eac81407dd9746b999169a0f633a6bdf2025-08-20T02:52:38ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-01-0191626810.29207/resti.v9i1.60686068CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping ApproachYunidar Yunidar0Y Yusni1N Nasaruddin2Fitri Arnia3Universitas Syiah KualaUniversitas Syiah KualaUniversitas Syiah KualaUniversitas Syiah KualaStunting, a condition characterised by short stature, is a growth disorder caused by chronic malnutrition, which often begins in the womb. Children affected by stunting usually show different physical and cognitive characteristics compared to their peers. Research shows that these physical differences can also be observed in facial features. Because faces provide important information and are commonly studied in digital image processing, in this study, we will compare the facial image classification performance of stunted children versus normal children using various Convolutional Neural Network (CNN) architectures. The evaluated architectures include MobileNetV2, InceptionV3, VGG19, ResNet18, EfficientNetB0, and AlexNet. To improve the learning process, augmentation techniques with Haar cascade and Gaussian filters were applied so that the data set increased from 1,000 to 6,000 images. After adding the dataset, training is carried out with an early stop approach to minimise overfitting. The main aim of this research is to identify the CNN model that is most effective in differentiating facial images of stunted children from normal children. The results show that the EfficientNetB0 architecture outperforms other models, achieving 100% accuracy. Early stopping has been shown to improve training efficiency and help prevent overfitting.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6068stuntingfacescnnstuntedhaar cascadeearly stopping |
| spellingShingle | Yunidar Yunidar Y Yusni N Nasaruddin Fitri Arnia CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) stunting faces cnn stunted haar cascade early stopping |
| title | CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach |
| title_full | CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach |
| title_fullStr | CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach |
| title_full_unstemmed | CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach |
| title_short | CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach |
| title_sort | cnn performance improvement for classifying stunted facial images using early stopping approach |
| topic | stunting faces cnn stunted haar cascade early stopping |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6068 |
| work_keys_str_mv | AT yunidaryunidar cnnperformanceimprovementforclassifyingstuntedfacialimagesusingearlystoppingapproach AT yyusni cnnperformanceimprovementforclassifyingstuntedfacialimagesusingearlystoppingapproach AT nnasaruddin cnnperformanceimprovementforclassifyingstuntedfacialimagesusingearlystoppingapproach AT fitriarnia cnnperformanceimprovementforclassifyingstuntedfacialimagesusingearlystoppingapproach |