PREDIKSI KASUS AKTIF COVID-19 MENGGUNAKAN METODE K-NEAREST NEIGHBORS

Merinda Lestandy

Abstract


Penyebaran COVID-19 yang semakin pesat membutuhkan pendekatan yang tepat untuk memprediksi penyebarannya dalam proses memerangi virus. Hal ini membenarkan bagaimana dan sejauh mana Machine Learning (ML) penting dalam mengembangkan dan meningkatkan sistem perawatan kesehatan pada sekala global. KNN merupakan algoritma yang melakukan klasifikasi berdasarkan kedekatan lokasi (jarak) suatu data dengan data yang lain. Konsep dasar dari K-NN adalah mencari jarak terdekat antara data yang akan dievaluasi dengan K tetangga terdekatnya dalam data pelatihan. Dataset yang digunakan pada penelitian ini yaitu 260 data dengan 13 parameter. Dari 260 data, 80% merupakan data training dan 20% merupakan data testing. Dimana data training sebesar 208 data dan data testing sebesar 52 data. Metode KNN mampu memberikan prediksi kasus aktif  penyakit COVID-19 yang akurat dengan MSE sebesar 0,007 dan akurasi sebesar 72,3337%.

Keywords


Covid-19, KNN, prediksi

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References


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DOI: https://doi.org/10.22219/sentra.v0i6.3836

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Universitas Muhammadiyah Malang Kampus III

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