Visualisasi Prediksi Akumulasi Kasus Virus Covid-19 di Indonesia Menggunakan Support Vector Regression

Dinanto, Zahrul Zizki (2021) Visualisasi Prediksi Akumulasi Kasus Virus Covid-19 di Indonesia Menggunakan Support Vector Regression. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penyakit Coronavirus (COVID-19) adalah penyakit menular yang disebabkan oleh virus korona yang baru ditemukan. Kebanyakan orang yang terinfeksi virus COVID-19 akan mengalami penyakit pernapasan ringan hingga sedang dan sembuh tanpa memerlukan perawatan khusus. Kasus COVID-19 akan semakin menyebar dengan cepat di Indonesia apabila tidak ada kesadaran untuk mengerti betapa cepat penyebaran COVID-19. hingga pada tanggal 3 Juni 2021 angka kasus COVID-19 di Indonesia sudah mencapai 1,83 juta kasus terkonfirmasi. Salah satu cara mencegah penyebaran COVID-19 adalah dengan memberikan informasi pertumbuhan jumlah kasus dan prediksi yang akurat kepada masyarakat agar kewaspadaan diri mereka meningkat terhadap wabah COVID-19 di Indonesia. Penulis membuat sistem informasi visualisasi Peramalan Akumulasi Kasus Virus COVID-19 di Indonesia menggunakan metode Support Vector Regression (SVR) karena memiliki kelebihan dalam menunjukan performa yang sangat baik untuk prediksi timeseries. Penelitian ini memiliki tujuan untuk menerapkan model prediksi menggunakan algoritma SVR untuk melakukan prediksi akumulasi kasus COVID-19 di Indonesia agar masyarakat semakin waspada akan penyebaran COVID-19 di Indonesia.Pada Peneletian ini berhasil memberikan visualisasi prediksi dalam bentuk website dan memberikan akurasi yang baik dengan menggunakan algoritma SVR.
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Coronavirus disease (COVID-19) is an infectious disease caused by the newly discovered corona virus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. COVID-19 cases will increase rapidly in Indonesia if there is no awareness to understand the speed of the spread of COVID-19 until June 3, 2021, the number of COVID-19 cases in Indonesia has reached 1.83 million confirmed cases. One way to prevent the spread of COVID-19 is to provide information about the growth in the number of cases and accurate predictions to the public so that they increase their awareness of the COVID-19 outbreak in Indonesia. The author proposes to create an information system that contains a visualization of the Forecasting Accumulation of COVID-19 Virus Cases in Indonesia using the Support Vector Regression (SVR) learning method because it has the advantage of showing excellent performance for time series prediction. This study aims to apply a prediction model using the SVR algorithm to predict the accumulation of COVID-19 cases in Indonesia so that people are more aware of the spread of COVID-19 in Indonesia. This research has succeeded in providing predictive visualization in the form of a website and providing good accuracy using the SVR algorithm.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Covid-19,Support Vector Regression,Accumulation,Prediction.
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T57.5 Data Processing
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Zahrul Zizki Dinanto
Date Deposited:	13 Aug 2021 18:29
Last Modified:	13 Aug 2021 18:29
URI:	http://repository.its.ac.id/id/eprint/86258

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