Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process

Authors

  • Ahmad Ilham Kushartanto Universitas Nasional
  • Fauziah Faculty of Communication and Information Technology, Universitas Nasional
  • Rima Tamara Aldisa Faculty of Communication and Information Technology, Universitas Nasional

DOI:

10.33395/sinkron.v8i2.13349

Keywords:

Classification, Convolutional Neural Network, Image, Support Vector Machine, Web Application

Abstract

Skin, as the outermost layer of the body, is often in contact with bacteria, germs and viruses because of its most external position. According to statistics from the 2009 Indonesian Health Profile, skin illness is the third most common ailment seen in outpatient settings across the country's hospitals. Therefore, maintaining healthy skin is important because it protects the body's internal organs from injury and attack by pathogens. The development of image classification, such as the classification of skin diseases, has become a focus in the health sector. This research analyses the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in web-based skin disease classification and overcomes the problem of imbalanced training data. With data augmentation and preprocess, this research improves data generalization and compares performance metrics such as Recall, Accuracy, and F1 Score. The results show that the average accuracy of CNN is 83.8%, while SVM reaches 81%. Although both models have high metrics for the normal class, other more complicated classes can only be handled by CNN with a value of more than 0.9. Apart from that, the CNN method also provides a higher Confidence Score than SVM, as well as a faster execution time. In conclusion, the CNN method is superior and recommended for skin disease classification based on web applications based on various performance test results.

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References

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How to Cite

Kushartanto, A. I., Fauziah, & Aldisa , R. T. . (2024). Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 778-788. https://doi.org/10.33395/sinkron.v8i2.13349