Analisa Perbandingan Algoritma CNN Dan MLP Dalam Mendeteksi Penyakit COVID-19 Pada Citra X-Ray Paru
Abstract
Pada bulan Maret 2020 Organisasi Kesehatan Dunia atau WHO (World Health Organization) menyatakan bahwa COVID-19 sebagai pandemi global. Untuk mengendalikan penyebaran COVID-19 ini dibutuhkan diagnosis secara dini dan akurat. Saat ini, standar emas dalam diagnosis COVID-19 didasarkan pada Reverse Transcripttion Polymerase Chain Reaction (RT-PCR) yakni mengambil dari sample pasien secara langsung. Dalam menangani masalah yang ada dibutuhkan metode diagnostic alternative, seperti melakukan pengolahan dan analisis dari pencitraan medis. Tujuan dari penelitian ini adalah untuk melakukan diagnosis alternatif menggunakan data citra paru untuk dapat mengklasifikasi mana paru yang terkena COVID-19 dan mana paru yang sehat. Metode yang digunakan dalam mengklasifikasi data citra adalah dengan pendekatan Deep Learning. Pada kasus ini, penelitian ini akan melakukan perbandingan algoritma CNN dan MLP untuk dapat melihat mana dari keduanya yang menghasilkan hasil yang lebih baik. Hasil yang didapat menunjukkan bahwa CNN lebih unggul dengan akurasi sebesar 97,14% dibandingkan dengan MLP dengan akurasi sebesar 91,39%. Hal ini dikarena Algoritma CNN memiliki lebih banyak lapisan dibandingkan dengan MLP, serta Algoritma CNN dapat bekerja dengan baik pada data spasial.
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DOI: http://dx.doi.org/10.30872/jsakti.v3i2.5129
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