Skripsi

SISTEM KLASIFIKASI PENYAKIT KULIT (LESI) MENGGUNAKAN TEKNIK PENGOLAHAN DIGITAL DAN PEMBELAJARAN MENDALAM

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Penilaian

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Skin diseases account for 1.79% of the world's disease burden. Indonesia is ranked 3rd of the top ten diseases in the world. Digital processing can enable better feature extraction from images of skin lesions, thereby aiding in the identification of patterns or characteristics that may be difficult to recognize by the human eye. Deep learning has demonstrated the ability to understand and process image data with a high degree of accuracy. By applying deep learning to skin lesion image data, classification systems can learn automatically to identify patterns associated with various skin lesions. This research discusses the Machine Learning model for classifying skin lesion images using the CNN method with MobileNet architecture and VGG16 architecture. To test the efficiency of implementing the CNN method with the MobileNet architecture and VGG16 architecture, the dataset used in carrying out this research is the HAM10000 dataset. Experimental results show that the best model on the MobileNet architecture obtains an accuracy of 99%, and the best model on the VGG16 architecture obtains an accuracy of 84% during training.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000063	T163145	T1631452025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1631452025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer., 2025

Collation

xiii, 66 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Teknik digital
Prodi Sistem Komputer, Fakultas Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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