Arsitektur CNN InceptionResNet-V2 Untuk Pengelompokan Pneumonia Chest X-Ray

Indra Bakti; Mohamad Firdaus

doi:10.58290/jukomtek.v1i2.66

Penulis

Indra Bakti ITB AHMAD DAHLAN
Mohamad Firdaus Universitas Indraprasta PGRI

DOI:

https://doi.org/10.58290/jukomtek.v1i2.66

Kata Kunci:

InceptionResNet, Convolution Neral Network, Chest X-Ray, Pneumonia

Abstrak

Dalam abad terakhir ini, khususnya metode Convolution Neural Network (CNN) pada penggunaan machine learning yang merupakan teknologi intelegensi artifisial atau Artificial Intelligence (AI) telah dapat dan sangat mampu membantu dunia medis (kedokteran). Melalui penelitian tindakan pada dataset gambar, CNN berhasil dan mampu menunjukkan klasifikasi atau pengelompokan berdasarkan karakteristik dan properti yang sama pada gambar yang tidak berlabel dengan akurasi lebih tinggi dan lebih cepat daripada metode pembelajaran mesin lainnya. Hal ini sangat berguna bagi dunia Kesehatan, khususnya dalam penggunaan chest-x-ray (rontgen dada) pada dunia kedokteran. Studi ini bertujuan untuk mengoptimalkan arsitektur CNN InceptionResNet-V2, untuk mengklasifikasikan penyakit Covid-19, dengan melatih 4000 dataset citra x-ray dada. Hasil uji akurasi dari InceptionResNet-V2 menghasilkan 98%, dengan presisi tiap kelas arsitektur CNN InceptionRestNet-V2 adalah Covid (99%), Lung_Opacity (97%), Normal (98%), Viral_Pneumonia (98%). Arsitektur CNN InceptionRestNet dapat membantu hasil chest-x-ray dengan cepat dan akurat.

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