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IMPLEMENTASI FILTER GRAY LEVEL CO-OCCURANCE MATRIX TERHADAP SISTEM KLASIFIKASI BREAST CANCER DENGAN METODE CONVOLUTIONAL NEURAL NETWORK

*Muhammad Ariefur Rohman  -  Prodi Teknik Elektro Fakultas Teknik, Universitas Brawijaya, Jl. MT. Haryono No.167, Malang, Jawa Timur, Indonesia 65145, Indonesia
Dikirim: 18 Jun 2021; Diterbitkan: 30 Agu 2021.
Akses Terbuka Copyright (c) 2021 Transmisi: Jurnal Ilmiah Teknik Elektro under http://creativecommons.org/licenses/by-sa/4.0.

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Kanker payudara merupakan salah satu penyakit dengan proyeksi kematian terbesar selama 10 tahun terakhir dengan indeks kematian mencapai rata-rata 5 juta per-tahun, dan diprediksi akan terus naik hingga 60% di seluruh dunia. Umumnya, banyak metode yang digunakan untuk mendeteksi penyakit ini, salah satunya dengan mengamati jaringan histopatology. Banyak dari para ilmuwan, yang menggunakan jaringan histopatology untuk menganalisa, mengamati dan membuat sistem klasifikasi kanker payudara dengan berbagai metode, seperti: convolutional neural network, deep learning, support vector machine. Penggunaan metode convolutional neural network terbukti paling unggul pada sistem klasifikasi kanker payudara, namun akurasi rata-rata yang dihasilkan relatif cukup rendah. Selain itu, penggunaan metode convolutional neural network, membutuhkan waktu komputasi yang relatif lama untuk mengklasifikasikan 7909 dataset ukuran 4 GB. Berdasarkan alasan tersebut, desain sebuah sistem klasifikasi dengan mengimplementasikan Gray Level Co-occurance Matrix pada saat prapengolahan data CNN di butuhkan. Hasil penelitian menunjukkan bahwa, penggunaan metode CNN menghasilkan waktu komputasi lebih lama, yaitu: 3300 detik dibandingkan dengan kombinasi metode GLCM Entropy dan CNN 2040 detik. Sedangkan rata-rata akurasi latih dan uji yang dihasilkan oleh metode kombinasi GLCM entropy dan CNN adalah  92,26% dan 94,16%, lebih unggul dibandingkan dengan metode CNN, yaitu: 88,41% untuk data latih, dan 87,68% untuk data uji.  

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Kata Kunci: Convolutional neural network, gray level co-occurance matrix, breast cancer, neural network

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