Penerapan Model Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Untuk Meramalkan Jumlah Positif Covid-19 Pada Empat Provinsi di Indonesia

Pramadana, Bryllian Reyga Akbar (2021) Penerapan Model Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Untuk Meramalkan Jumlah Positif Covid-19 Pada Empat Provinsi di Indonesia. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Virus Covid-19 atau biasa disebut virus corona pertama kali masuk di Kota Wuhan tepatnya negara China dan masuk ke Indonesia pada Desember 2019. Virus ini dapat berkembang dengan cepat hingga menyebabkan infeksi lebih parah dan gagal organ. Menurut World Health Organization (WHO) penyebaran virus corona dapat terjadi melalui tetesan pernapasan (droplets), transmisi udara, transmisi permukaan benda, dan feses (tinja orang yang terinfeksi). Terdapat 4 Provinsi dengan selisih kenaikan kasus positif Covid-19 tertinggi yaitu DKI Jakarta, Jawa Barat, Jawa Tengah, dan Jawa Timur. Analisis statistika yang melibatkan aspek waktu dan lokasi serta dapat digunakan untuk meramalkan jumlah positif Covid-19 pada empat provinsi tersebut adalah model Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA). Pada penelitian ini menggunakan bobot lokasi invers jarak serta metode estimasi parameter yang digunakan adalah Ordinary Least Square (OLS). Model terbaik untuk meramalkan jumlah positif Covid-19 pada empat provinsi di Indonesia adalah GSTAR(21)-OLS yang memenuhi asumsi kebaikan model dengan nilai RMSE terkecil sebesar 294,102. =================================================================================================== The Covid-19 virus or commonly called the corona virus first entered the city of Wuhan, precisely in China and entered Indonesia in December 2019. This virus can develop rapidly to cause more severe infections and organ failure. According to the World Health Organization (WHO), the spread of the corona virus can occur through respiratory droplets (droplets), airborne transmission, transmission of surface objects, and feces (feces of infected people). There are 4 provinces with the highest increase in the increase in positive cases of Covid-19, namely DKI Jakarta, West Java, Central Java, and East Java. Statistical analysis involving aspects of time and location and can be used to predict the number of positive Covid-19 in the four provinces is the Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) model. In this study, the inverse distance location weights and the parameter estimation method used are Ordinary Least Square (OLS). The best model to predict the number of positive Covid-19 in four provinces in Indonesia is GSTAR(21)-OLS which developed the model achievement with the smallest RMSE value of 294,102.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Covid-19, World Health Organization, GSTARIMA, Bobot lokasi invers jarak, OLS, Covid-19, World Health Organization, GSTARIMA, inverse distance location weighting matrix, OLS.
Subjects: Q Science > QA Mathematics > QA275 Theory of errors. Least squares. Including statistical inference
Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry)
Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Q Science > QA Mathematics > QA278 Cluster Analysis. Multivariate analysis. Correspondence analysis (Statistics)
Divisions: Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User: Bryllian Reyga Akbar Pramadana
Date Deposited: 27 Aug 2021 07:08
Last Modified: 27 Aug 2021 07:08
URI: https://repository.its.ac.id/id/eprint/90664

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