Penerapan Algoritma Decision Tree Untuk Klasifikasi KIPI Vaksin Covid-19
DOI:
https://doi.org/10.22441/fifo.2022.v14i2.005Keywords:
COVID-19, Decision Tree, Classification, AEFI, Decision Support System, VaccinationAbstract
Pandemi COVID-19 merupakan wabah yang terjadi di seluruh dunia, terutama Indonesia. Pandemi COVID-19 telah melumpuhkan berbagai bidang di sektor publik dan banyak penduduk terkena Sars-Cov-2 yang menyebabkan kematian bagi masyarakat dan tenaga kesehatan. dalam melaksanakan Program Vaksinasi Coronavirus di Indonesia. Banyak masyarakat yang khawatir terhadap Vaksinasi Coronavirus dikarenakan hoax terhadap Vaksinasi Coronavirus dan ketakutan dengan dampak KIPI. Penulis melakukan penelitian dengan menggunakan metode Decision Tree untuk melakukan klasifikasi KIPI Vaksin COVID-19 menggunakan data Vaksin COVID-19 siswa/siswi salah satu SMP Kota Bekasi. Berdasarkan hasil penelitian yang didapatkan, penelitian menghasilkan model Decision Tree dari 4 atribut yang didapatkan lalu dikategorikan dengan 2 variabel yang berbeda yakni variabel target dan variabel prediksi. Penelitian menghasilkan model Decision Tree lalu melakukan perbandingan dengan algoritma naive bayes dengan masing-masing keakuratan sebesar 89,5349% dan 88.3721 %. Hasil ini menunjukan algoritma Decision Tree memiliki keakuratan lebih tinggi dibandingkan dengan algoritma Naïve Bayes sehingga algoritma Decision Tree merupakan teknik yang tepat dalam hal pengklasifikasian.
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