Analisis Pengaruh Faktor-Faktor Sosial Ekonomi Terhadap Penyakit Demam Dengue Pada Sembilan Negara di Amerika Selatan

Christi, Veren (2023) Analisis Pengaruh Faktor-Faktor Sosial Ekonomi Terhadap Penyakit Demam Dengue Pada Sembilan Negara di Amerika Selatan. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Dalam penelitian ini, ditinjau 29 variabel faktor-faktor sosial ekonomi terhadap jumlah kasus demam dengue di 9 Negara di Amerika Selatan. Terdapat multikolinearitas yang tinggi di antara variabel-variabel tersebut sehingga dilakukan metode regresi Partial Least Square(PLS). Regresi PLS diaplikasikan pada analisis faktor sosial ekonomi terhadap jumlah kasus dengue pada 9 Negara di Amerika Selatan dengan tujuan untuk menghilangkan masalah multikolinearitas sehingga data dapat digunakan untuk melakukan prediksi dan diperoleh taksiran estimasi yang tepat. Data yang digunakan yakni data faktor sosial ekonomi dan jumlah kasus dengue pada tahun 1990 hingga 2020. Metode regresi PLS dimulai dengan melakukan regresi Ordinary Least Square (OLS) dengan metode Backward Elimination untuk memperoleh model dengan seluruh variabel yang signifikan. Kemudian dilakukan pembentukkan jumlah komponen optimal dengan uji Root Mean Square Error Prediction. Hasil regresi PLS terbukti dapat mengatasi masalah multikolinearitas. / In this study, 29 social and economic factors were reviewed towards the number of dengue fever cases in 9 countries in South America. There is a high multicollinearity between these variables, so the Partial Least Square (PLS) regression method was used. PLS regression was applied to the analysis of social and economic factors towards the number of dengue cases in 9 countries in South America with the aim of eliminating the problem of multicollinearity so that the data can be used to make predictions and obtain accurate estimates. The data used are data of social and economic factors and the number of dengue cases from 1990 to 2020. The PLS regression method begins by performing Ordinary Least Square (OLS) regression with the Backward Elimination method to obtain a model with all significant variables. Then the optimal number of components is obtained with the Root Mean Square Error Prediction test. The results of the PLS regression proved to be able to solve the problem of multicollinearity.

Item Type: Thesis (Bachelor)
Creators:
CreatorsNIMEmail
Christi, VerenNIM01112180044backupveren@gmail.com
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorMargaretha, HelenaNIDN0312057504helena.margaretha@uph.edu
Thesis advisorFerdinand, Ferry VincenttiusNIDN0323059001ferry.vincenttius@uph.edu
Thesis advisorUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Uncontrolled Keywords: partial least square regression; backward elimination; ordinary least square;
Subjects: Q Science > QA Mathematics
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Mathematics
Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Mathematics
Depositing User: Veren Christi
Date Deposited: 05 Jun 2023 03:02
Last Modified: 05 Jun 2023 03:02
URI: http://repository.uph.edu/id/eprint/55681

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