Pattern Recognition untuk Klasifikasi Penyakit Kanker Kulit menggunakan Artificial Intelligence (AI)
DOI:
https://doi.org/10.35473/ikn.v2i1.3563Keywords:
Klasifikasi, InceptionV3; Convolutional Neural Network; Kanker KulitAbstract
This research aims to classify skin cancer images using an artificial intelligence method called Convolutional Neural Networks (CNN). The study focuses on classifying skin cancer into 7 categories, using data from the International Skin Imaging Collaboration (ISIC). We employed the CNN algorithm to train the model, which involved learning features, classifying images, and optimizing the model. To evaluate the model's performance, we experimented with different training data proportions (70%, 80%, and 90%), dropout rates (0.5, 0.6, 0.7, and 0.8), and batch sizes (8, 16, 32, 64). The best results were achieved with 80% of the data for training, a dropout rate of 0.4, and a batch size of 16, resulting in an accuracy of 83.22%.
ABSTRAK
Penelitian ini bertujuan untuk mengimplementasikan metode kecerdasan buatan melalui algoritma Convolution Neural Network (C-NN) untuk mengklasifikasikan citra kanker kulit. Objek pada penelitian ini adalah klasifikasi kanker kulit dengan berdasarkan 7 kategori kanker kulit, sedangkan Data yang digunakan oleh peneliti adalah data yang bersumber dari The International Skin Imaging Collaboration (ISIC). Metode yang digunakan peneliti adalah Algoritma Convolutional Neural Networks (CNN). Pada data training dilakukan pembelajaran fitur, klasifikasi, dan optimum model, dimana proses ini merupakan implementasi algoritma yang digunakan. Skenario pengujian dengan indikator skenario pengujian yaitu pembagian data training 70%, 80%, dan 90%, inisialisasi Dropout layer bernilai 0.5, 0.6, dan 0.7, dan 0,8 dan Batchsize bernilai 8, 16, 32, 64. Kesimpulan dari Penelitian ini adalah mendapatkan model terbaik dengan nilai akurasi 83.22% dari komposisi data Taining 80%, Dropout 0.4 dan Batchsize 16.
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