Pemodelan Perkembangan New Cases Covid-19 di Indonesia Menggunakan Multi-Layer Perceptron dan Support Vector Machine

Muhammad Ibnu Choldun Rachmatullah

Abstract


Meningkatnya ketersediaan data historis dalam jumlah besar dan kebutuhan untuk membuat perkiraan yang akurat tentang perilaku masa depan menjadi perhatian khusus dalam mencari teknik yang dapat menarik kesimpulan dari mengamati hubungan antara data tertentu, antara data masa lalu dan data masa depan. Domain peramalan mengalami peningkatan sejak tahun 1960-an, dengan metode statistik linier, misalnya menggunakan model ARIMA. Baru-baru ini, model pembelajaran mesin telah menarik perhatian dan dapat digunakan sebagai teknik lain selain model statistik klasik untuk kasus peramalan. Penelitian ini memprediksi perubahan kasus baru positif Covid-19 per satu juta penduduk (new cases per million Covid-19) di Indonesia menggunakan pembelajaran mesin. Pemodelan perubahan new cases per million diperlukan karena penyakit ini merupakan penyakit baru, sehingga sampai saat ini belum ada pemodelan deret waktu yang cukup akurat untuk menggambarkan kasus tersebut. Teknik machine learning yang akan digunakan adalah Multi-Layer Perceptron (MLP) dan Support Vector Machine (SVM) dan dibandingkan kinerja dari kedua teknik tersebut. Dari hasil perhitungan kinerja, prediksi new cases per million Covid-19 yang dilakukan dengan menggunakan SVM(RMSE = 9,053) memiliki kinerja yang lebih baik dibandingkan dengan menggunakan MLP (RMSE = 10,284). Nilai RMSE yang lebih kecil menunjukkan kinerja yang lebih baik.


Keywords


New cases per million; Covid-19; MLP; SVM; RMSE

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References


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DOI: http://dx.doi.org/10.26418/jp.v8i2.53919

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