Analisis Risiko Penyebaran COVID-19 di Surabaya Raya Menggunakan Model Neyman-Scott Cox Processes

Izati, Prajna Pramita (2022) Analisis Risiko Penyebaran COVID-19 di Surabaya Raya Menggunakan Model Neyman-Scott Cox Processes. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

COVID-19 merupakan penyakit yang menyerang alat pernapasan. Jumlah kasus COVID-19 di Jawa Timur terus mengalami peningkatan tiap harinya khususnya wilayah Surabaya Raya meliputi Kota Surabaya, Kabupaten Gresik, dan Kabupaten Sidoarjo yang memiliki jumlah pasien terkonfirmasi positif tertinggi di Jawa Timur. Kota Surabaya menjadi penyumbangkan terbesar kasus terkonfirmasi postif COVID-19 di Surabaya Raya yaitu sebesar 60,1 %. Penelitian ini bertujuan untuk membandingkan 4 model Neyman-Scott Cox Process yaitu Matern, Thomas, Cauchy, dan Variance Gamma Cluster Process dengan melibatkan beberapa kovariat untuk mendapatkan model yang paling sesuai dalam memodelkan risiko penyebaran COVID-19 di Surabaya Raya dimana kriteria pembandingnya yaitu nilai BIC terkecil dan envelope K-function. Hasil uji homogenitas menunjukkan penyebaran data kasus terkonfirmasi positif COVID-19 di Surabaya Raya tidak homogen dan untuk korelasi spasial dengan Inhomogeneous K-function diperoleh bahwa data cenderung membentuk kelompok. Berdasarkan hasil pemodelan Neyman-Scott Cox Processes didapatkan bahwa model Inhomogeneous Cauchy Cluster Process merupakan model terbaik, dimana kovariat kepadatan penduduk dan kepadatan lokasi kerumunan yaitu pusat perindustrian dan tempat ibadah berpengaruh secara signifikan terhadap risiko penyebaran kasus terkonfirmasi positif COVID-19 di Surabaya Raya. Hasil prediksi risiko kasus terkonfirmasi positif COVID-19 di Surabaya Raya menunjukkan risiko penyebarankasus terkonfirmasi positif COVID-19 di wilayah Kota Surabaya lebih tinggi jika dibandingkan dengan wilayah Kabupaten Sidoarjo maupun Gresik.
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COVID-19 is a disease that attacks the respiratory system. The
number of COVID-19 cases in East Java continues to increase
every day, especially the Greater Surabaya area including
Surabaya City, Gresik Regency, and Sidoarjo Regency which have
the highest number of positive confirmed patients in East Java. The
city of Surabaya became the largest contributor to the positive
confirmed case of COVID-19 in Greater Surabaya, amounting to
60.1%. This study aims to compare 4 Neyman-Scott Cox Process
models, namely Matern, Thomas, Cauchy, and Variance Gamma
Cluster Process by involving several covariates to obtain the most
suitable model in modeling the spread of COVID-19 in Greater
Surabaya where the comparison criterion is the BIC function value.
K envelope and smallest. The results of the homogeneity test
showed that the spread of data on positive confirmed cases of
COVID-19 in Greater Surabaya was not homogeneous and for the
spatial correlation with the K-function which was not
homogeneous, it was found that the data tended to form groups.
Based on the Neyman-Scott Cox Processes modeling, it was found
that the Inhomogeneous Cauchy Cluster Process model is the best
model, where the covariates of population density and density of
industrial centers and places of worship have a significant effect on
the risk of spreading positive confirmed cases of COVID-19 in
Greater Surabaya. The results of the prediction of the risk of
positive confirmed cases of COVID-19 in Greater Surabaya show
that the risk of spreading positive confirmed cases of COVID-19 in
ix
the Surabaya City area is higher when compared to the Sidoarjo
and Gresik regencies.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	COVID-19, Inhomogeneous Cauchy Cluster Process, Neyman-Scott Cox Processes, Pemodelan, Surabaya Raya, Inhomogeneous Cauchy Cluster Process, Modeling, Neyman-Scott Cox Processes, Surabaya Raya
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HA Statistics > HA30.6 Spatial analysis H Social Sciences > HA Statistics > HA31.7 Estimation R Medicine > RA Public aspects of medicine > RA644.C67 COVID-19 (Disease)
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Prajna Pramita Izati
Date Deposited:	21 Feb 2022 01:56
Last Modified:	04 Dec 2025 06:50
URI:	http://repository.its.ac.id/id/eprint/94670

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