Prediksi penyebaran COVID-19 di Indonesia dengan support vector machine dan linear regression = Prediction of the covid-19 spread in Indonesia with support vector machine and linear regression

Juvenia, Juvenia (2021) Prediksi penyebaran COVID-19 di Indonesia dengan support vector machine dan linear regression = Prediction of the covid-19 spread in Indonesia with support vector machine and linear regression. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

COVID-19 is an infectious disease caused by the corona virus which is a respiratory pathogen. COVID-19 is originating from Wuhan, China entered Indonesia on April 9, 2020 and has spread to 34 provinces in Indonesia. This research focuses on predicting the COVID-19 pandemic in Indonesia and observe how each different model performs in making predictions. Prediction is done using the Support Vector Machine model with each kernel rbf, poly, and sigmoid and Linear Regression. After making predictions, the performance of the model is observed by calculating MAE, MSE, and RMSE and then analysing the model which model has the smallest MAE, MSE, and RMSE values, which in result has better performance. In addition, the model is also evaluated by analysing the number of predicted cases with the number of factual cases to see the differences and similarities of the result data. The results of the number of predicted cases obtained from the Support Vector Machine kernel poly model on day 305 for the WIB time zone is 606344, WITA is 167757, and WIT is 38979. The Linear Regression model on day 305 for the WIB time zone is 321388, WITA is 86840 , and WIT is 20406. Support Vector Machine model with kernel rbf and sigmoid failed to make predictions because the model was unable to process existing data patterns. By looking at the smallest MAE, MSE, and RMSE values, the Linear Regression model has better performance compared to other models. From analyzing the number of predicted cases with the number of factual cases, the Support Vector Machine model with the poly kernel has the number of prediction cases that are closest to factual./COVID-19 adalah penyakit menular yang disebabkan oleh virus corona yang merupakan patogen pernapasan. COVID-19 yang berasal dari Wuhan, Tiongkok masuk ke Indonesia pada tanggal 9 April 2020 dan telah menyebar ke 34 provinsi di Indonesia. Penelitian ini berfokus pada dengan melakukan prediksi pandemik COVID-19 di Indonesia dan melihat bagaimana performa setiap model yang berbeda dalam melakukan prediksi. Prediksi dilakukan dengan menggunakan model Support Vector Machine dengan masing-masing kernel rbf, poly, dan sigmoid dan Linear Regression. Setelah dilakukan prediksi, performa model dilihat dengan menghitung MAE, MSE, dan RMSE kemudian dianalisa model yang memiliki nilai MAE, MSE, dan RMSE terkecil memiliki performa yang lebih baik. Selain itu model juga dievaluasi dengan menganalisa jumlah kasus hasil prediksi dengan jumlah kasus faktual untuk melihat perbedaan dan kemiripan data yang dihasilkan. Hasil jumlah kasus prediksi yang didapatkan dari model Support Vector Machine kernel poly pada hari ke 305 untuk zona waktu WIB sebanyak 606344, WITA sebanyak 167757, dan WIT sebanyak 38979. Model Linear Regression pada hari ke 305 untuk zona waktu WIB sebanyak 321388, WITA sebanyak 86840, dan WIT sebanyak 20406. Model Support Vector Machine dengan kernel rbf dan sigmoid gagal melakukan prediksi dikarenakan model tidak mampu mengolah pola data yang ada. Dengan melihat nilai MAE, MSE, dan RMSE terkecil, model Linear Regression memiliki performa yang lebih baik dibandingkan dengan model lainnya. Dari menganalisa jumlah kasus hasil prediksi dengan jumlah kasus faktual didapatkan model Support Vector Machine dengan kernel poly memiliki jumlah kasus prediksi yang paling mendekati dengan faktual.

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