Penerapan Model Generalized Space Time Autoregressive (GSTAR) Untuk Meramalkan Banyaknya Kasus Positif COVID-19

Oktaviani, Adinda (2021) Penerapan Model Generalized Space Time Autoregressive (GSTAR) Untuk Meramalkan Banyaknya Kasus Positif COVID-19. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

[img] Text
06111740000019-Undergraduate_Thesis.pdf - Accepted Version
Restricted to Repository staff only until 1 October 2023.

Download (2MB) | Request a copy

Abstract

Corona Virus Disease 2019 (COVID-19) adalah suatu virus baru yang dapat menular dan dampak terburuknya dapat menyebabkan kematian, COVID-19 pertama kali muncul di Wuhan, China hingga akhirnya menyebar ke seluruh negara, salah satunya yaitu Indonesia. Penyebaran kasus COVID-19 di Indonesia sendiri cukup pesat hingga akhirnya World Health Organization (WHO) telah menetapkan kasus COVID-19 sebagai pandemi. Berdasarkan kondisi saat ini, tugas akhir ini membahas mengenai peramalan kasus positif COVID-19 pada lima lokasi di Jawa Timur (Kota Malang, Kota Batu, Kabupaten Pasuruan, Kabupaten Malang, Kota Pasuruan) menggunakan model space time yaitu Generalized Space Time Autoregressive (GSTAR). Mengingat COVID-19 penyebarannya sangat mudah bukan hanya bergantung pada waktu namun juga kedekatan antar lokasi maka metode GSTAR cukup baik digunakan untuk meramalkannya dengan asumsi parameter antar lokasi heterogen. Estimasi yang digunakan adalah OLS dengan bobot lokasi normalisasi korelasi silang. Hasil dari penelitian ini mendapatkan model GSTAR(2_1)-OLS adalah model terbaik untuk meramalkan banyaknya kasus positif COVID-19 pada lima lokasi di Jawa Timur dengan pembobotan normalisasi korelasi silang berdasarkan nilai RMSE terkecil pada data out sample. Hasil peramalan 10 hari mendatang kasus positif COVID-19 di kelima lokasi tersebut menunjukkan perubahan yang tidak terlalu signifikan. ===================================================================================================== Corona Virus Disease 2019 (COVID-19) is a new virus that can be contagious and its worst effects can lead to death. COVID-19 first appeared in Wuhan, China until it finally spread throughout the country, one of which is Indonesia. The spread of COVID-19 cases in Indonesia itself is quite rapid until finally the World Health Organization (WHO) designates COVID-19 cases as pandemics. Based on current conditions, this final project discuss about predict positive case data of COVID-19 at five locations in East Java (Malang City, Batu City, Pasuruan Regency, Malang Regency, Pasuruan City) using a space-time model namely Generalized Space-Time Autoregressive (GSTAR). Considering that COVID-19 is very easy to spread not only depending on the time but also the proximity between locations, the GSTAR method is good enough to be used to predict the assumption of parameters between heterogeneous locations. The estimation used is OLS with the location weight of cross-correlation normalization. The results of this study obtained the GSTAR(2_1)-OLS model is the best model to predict the number of positive cases of COVID-19 in five locations in East Java by weighting the normalization of cross-correlation based on the smallest RMSE value in data out sample. Forecast results for the next 10 days of positive cases of COVID-19 in all five locations show not very significant changes.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: GSTAR, COVID-19, Normalisasi Korelasi Silang, OLS, GSTAR, COVID-19, Cross Correlation Normalization, OLS.
Subjects: Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry)
Q Science > QA Mathematics > QA280 Box-Jenkins forecasting
Q Science > QA Mathematics > QA278 Cluster Analysis. Multivariate analysis. Correspondence analysis (Statistics)
R Medicine > RA Public aspects of medicine > RA644.C67 COVID-19 (Disease)
Divisions: Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User: Adinda Oktaviani
Date Deposited: 26 Aug 2021 04:37
Last Modified: 26 Aug 2021 04:37
URI: https://repository.its.ac.id/id/eprint/90022

Actions (login required)

View Item View Item