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All Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi) MDP Student Conference
Tommy Saputra
Universitas Multi Data Palembang
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Mathematics

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Klasifikasi Jenis Kanker Kulit Benign Dan Malignant Menggunakan Model Arsitektur AlexNet Tommy Saputra; Muhammad Ezar Al Rivan
MDP Student Conference Vol 2 No 1 (2023): The 2nd MDP Student Conference 2023
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1107.696 KB) | DOI: 10.35957/mdp-sc.v2i1.4344

Abstract

Kulit merupakan organ tubuh terluar manusia yang sering terserang penyakit. Penyakit berbahaya pada kulit yaitu kanker. Jenis kanker kulit terbanyak yang ditemukan di Indonesia adalah basal cell carcinoma, squamous cell carcinoma, dan melanoma. Kanker kulit dapat disembuhkan dengan penanganan yang tepat. Convolutional Neural Network (CNN) dapat digunakan dalam pendeteksian dini jenis kanker kulit benign dan malignant. Data yang digunakan diperoleh dari International Skin Imaging Collaboration (ISIC) tahun 2019. Model dibangun menggunakan arsitektur AlexNet dengan fungsi optimizer Adaptive Moment Estimation (Adam). Hasil pengujian menunjukan model AlexNet dengan optimizer Adam dapat melakukan klasifikasi jenis kanker dengan akurasi. sebesar 81,26%
Analisis Performa ResNet-152 dan AlexNet dalam Klasifikasi Jenis Kanker Kulit Tommy Saputra; Muhammad Ezar Al-Rivan
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 8, No 1 (2023)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v8i1.16464

Abstract

Skin cancer is a dangerous disease. The most common skin cancers in Indonesia is melanoma. Melanoma cases reached 9,6 million in 2018. Skin cancer can be cured with proper and quick treatment. Skin cancer early detection can be done by detection system types of skin cancer based on benign and malignant classes using Convolutional Neural Network (CNN) with ResNet-152 and AlexNet architecture. The data are taken from the 2019 International Skin Imaging Collaboration (ISIC) archives. The optimizer algorithms used are Adaptive Moment Estimation (Adam) and Mini-Batch Gradient Descent (MBGD). The result of the research indicates that ResNet-152 architecture using MBGD optimizer gives the best result with an accuracy of 87.85%
Co-Authors M Ezar Al Rivan