Geographycally weighted regression dan spatial pattern analysis untuk pemodelan kejadian penyakit malaria dan faktor yang mempengaruhi di provinsi Papua

Fadhilah, Nurul (2015) Geographycally weighted regression dan spatial pattern analysis untuk pemodelan kejadian penyakit malaria dan faktor yang mempengaruhi di provinsi Papua. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Malaria merupakan penyakit menular yang menjadi perhatian global, termasuk di Indonesia. Papua merupakan provinsi dengan insiden dan prevalensi kejadian penyakit malaria tertinggi di Indonesia pada tahun 2013. Dalam menentukan salah satu kebijakan di bidang kesehatan Pemerintah Daerah Provinsi Papua, maka perlu adanya penelitian terkait penyebaran penyakit malaria di Provinsi Papua. Berbagai penelitian kasus malaria dengan metode statistika sudah banyak dilakukan. Namun, penelitian tersebut hanya menganalisis faktor risiko tanpa memperhatikan faktor spasial serta belum membahas pola penyebarannya. Oleh karena itu, dalam penelitian ini dilakukan analisis spasial dengan dua metode, yaitu Spatial Pattern Analysis dan Geographically Weighted Regression. Metode Spatial Pattern Analysis untuk mendeskripsikan pola persebaran dan menyusun peta kerawanan persebaran kejadian penyakit malaria di Provinsi Papua. Sedangkan metode Geographically Weighted Regression untuk menyusun model kejadian penyakit malaria di Provinsi Papua serta mengetahui faktor yang berpengaruh secara signifikan di tiap lokasi. Persebaran kejadian penyakit malaria mempunyai pola yang menyebar. Kabupaten/kota di Provinsi Papua yang tergolong rawan yaitu Kabupaten Jayapura, Nabire, Kepulauan Yapen, Biak Numfor, Paniai, Puncak Jaya, Mimika, Boven Digoel, Intan Jaya, dan Kota Jayapura. Hasil pemodelan dengan GWR diperoleh model yang berbeda-beda untuk tiap kabupaten/kota di Provinsi Papua. Berdasarkan variabel signifikan di tiap kabupaten/kota, terbentuk pengelompokan yaitu 15 kelompok. ============================================================================================================== Malaria is one of the contagious diseases which currently being international concern, including in Indonesia. Papua is a province with highest occurrence and prevalence of Malaria in 2013. In order to determine a policy related to health sector in regional government of Papua, a research related to dissemination of Malaria disease in Papua is required. Actually, a lot of researches related to Malaria disease by using statistical methods have been done previously. However, existing researches only analyze about the risk factor and often neglect spatial factor as well as its dissemination patterns. Therefore, in this research the analysis is done by using two methods, which are Spatial Pattern Analysis and Geographically Weighted Regression. Spatial Pattern Analysis method is used to describe the dissemination patterns and formulate the map of dissemination susceptibility of Malaria disease in Papua. On the other hand, Geographically Weighted Regression method is used for modeling the occurrence of Malaria disease in Papua and knowing what factors that significantly affecting in each location. Based on this research, the occurrence pattern of Malaria disease is spreading. Some of the regions/cities in Papua considered prone of Malaria disease are Kabupaten Jayapura, Nabire, Kepulauan Yapen, Biak Numfor, Paniai, Puncak Jaya, Mimika, Boven Digoel, Intan Jaya, dan Kota Jayapura. By using GWR method, several different models are obtained for each region/city. Based on the significant variables in each region/city, there are 15 groups can be made.

Item Type: Thesis (Undergraduate)
Additional Information: RSSt 519.536 Fad g
Uncontrolled Keywords: GWR; Malaria; Regresi Linier Berganda; Spatial Pattern
Subjects: Q Science > QA Mathematics > QA278.2 Regression Analysis
Divisions: Faculty of Mathematics and Science > Statistics > (S1) Undergraduate Theses
Depositing User: - Taufiq Rahmanu
Date Deposited: 24 Jun 2019 06:58
Last Modified: 24 Jun 2019 06:58
URI: http://repository.its.ac.id/id/eprint/63200

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