PUTRI, AMARA SAKINA (2024) IDENTIFIKASI VIRUS CORONA (COVID-19) BERDASARKAN CITRA CHEST X-RAY MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK. S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.
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Abstract
Coronavirus Disease-19 (COVID-19) is a respiratory infection caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV2) virus strain and has been designated as a global pandemic. The main diagnosis is currently done through Polymerase Chain Reaction (PCR) and swab tests, but some people complain that this method causes discomfort in the nose and even pain when the doctor inserts the swab tool, causing fear for the public. The rapid spread and development of this virus has raised concerns for all parties, so a system is needed to help early detection of COVID-19 without causing discomfort and without pain in the nose. This research was conducted through chest x-ray images and identifying these images with 3 classes using the Convolutional Neural Network (CNN) method. The images used were 140 images per class consisting of 105 training images and 35 test images. Simulation is done with Python programming language and Google Colab. The highest accuracy result obtained in training is 94.09% and in testing is 69.52%.
| Item Type: | Thesis (S1) | |||||||||
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| Additional Information: | Coronavirus Disease-19 (COVID-19) merupakan penyakit infeksi saluran pernapasan yang disebabkan oleh jenis virus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-COV2) dan telah ditetapkan sebagai pandemi global. Diagnosis utama pada saat ini dilakukan melalui uji Polymerase Chain Reaction (PCR) dan swab, namun beberapa masyarakat mengeluh bahwa metode ini menimbulkan rasa tidak nyaman pada hidung bahkan nyeri saat dokter memasukkan alat swab sehingga menimbulkan ketakutan bagi masyarakat. Pesatnya penyebaran dan perkembangan virus ini telah menimbulkan kekhawatiran bagi semua pihak, maka dibutuhkan suatu sistem guna membantu pendeteksian dini COVID-19 tanpa menimbulkan rasa tidak nyaman dan tanpa rasa nyeri pada hidung. Penelitian ini dilakukan melalui citra chest x-ray dan mengindetifikasi citra tersebut dengan 3 kelas menggunakan metode Convolutional Neural Network (CNN). Citra yang digunakan sebanyak 140 citra tiap kelasnya yang terdiri dari 105 citra latih dan 35 citra uji. Simulasi dilakukan dengan bahasa pemrograman Python dan Google Colab. Hasil akurasi tertinggi diperoleh pada pelatihan yaitu 94.09% dan pada pengujian yaitu 69.52%. | |||||||||
| Uncontrolled Keywords: | Coronavirus Disease-19 (COVID-19), Convolutional Neural Network | |||||||||
| Subjects: | T Technology > T Technology (General) | |||||||||
| Divisions: | 03-Fakultas Teknik 03-Fakultas Teknik > 20201-Jurusan Teknik Elektro |
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| Depositing User: | AMARA SAKINA PUTRI | |||||||||
| Date Deposited: | 26 Aug 2024 08:59 | |||||||||
| Last Modified: | 26 Aug 2024 08:59 | |||||||||
| URI: | http://eprints.untirta.ac.id/id/eprint/41334 |
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