8,195 research outputs found
ANALISIS FAKTOR – FAKTOR YANG MEMPENGARUHI JUMLAH KEJAHATAN PENCURIAN KENDARAAN BERMOTOR (CURANMOR) MENGGUNAKAN MODEL GEOGRAPHICALLY WEIGHTED POISSON REGRESSION (GWPR)
Theft is an act taking someone else’s property, partially or entierely, with intention to have it illegally. Motor vehicle theft is one of the most highlighted crime type and disturbing the communities. Regression analysis is a statistical analysis for modeling the relationships between response variable and predictor variable. If the response variable follows a Poisson distribution or categorized as a count data, so the regression model used is Poisson regression. Geographically Weighted Regression (GWR) model is developed from global regression model where every parameter is counted for each observation location. Therefore, every observation location has different regression parameters. Geographically Weighted Poisson Regression (GWPR) is a local form of Poisson regression where data sampling location is prioritized. GWPR model is used for identifying the factors that influence the numbers of motor vehicles theft, either using a weighted gauss kernel function or bisquare kernel function. By using different weighting, the variables effecting the number of motor vehicle theft every Sub-District in the Semarang city is also different. Based on the value of Akaike Information Criterion (AIC) of Poisson regression and GWPR model, it is analyzed that GWPR model using a weighted fixed bisquare kernel function is the best model for analyzing the number of motor vehicles theft at every Sub-Districts in the Semarang city in 2012, because it has the smallest AIC value. This model has a precision of 88,81%.
Key words : Motor Vehicle Theft, Geographically Weighted Poisson Regression, Kernel Gauss Function, Kernel Bisquare Function, Akaike Information Criterio
PEMODELAN GEOGRAPHICALLY WEIGHTED POISSON REGRESSION (GWPR) PADA JUMLAH KASUS BARU KUSTA DI KABUPATEN BUTON PROVINSI SULAWESI TENGGARA TAHUN 2013
Poisson regression was obtained from the Poisson distribution, which is a theoretical distribution associated with a discrete random variable count, where each event follows the poisson distribution. Leprosy data in Buton is one example of the data count. The main problem of the poisson regression is when applied to the spatial data, the heterogeneity will occur. One impact of the emergence of spatial heterogeneity is regression parameters are varying spatially, so as to solve the problems on data spatial, the spatial modellingis done. Spatial modeling is appropriate for use Geographically Weighted Poisson Regression (GWPR). This study aims to determine the best model on the number of new cases of leprosy in Buton. This study is a non-reactive or unobtrusive method. The research was conducted in Buton Southeast Sulawesi province started from 5 to May 20 2014. The population in this study are all the new data cases of leprosy in Buton District Health Office. The sample in this study was taken by cluster random sampling technique ie the new data cases of leprosy in Buton District Health Office in 2013 with the unit of analysis is each sub-district in Buton District in 2013. The results showed that the Poisson regression models obtained did not meet the Equidispersi assumptions, so other model named the Generalized Poisson Regression (GPR) is used. Geographically Weighted Poisson Regression Model (GWPR) produces smaller AIC value compared to Generalized Poisson Regression (GPR). The best model for the number of new cases of leprosy in Buton is Geographically Weighted Poisson Regression models (GWPR)
Geographically Weighted Poisson Regression (GWPR) for Analyzing The Malnutrition Data in Java-Indonesia
Many regression models are used to provide some recommendations in private sectors or government public policy. Data are usually obtained from several districts which may varies from one to the others. Assuming there is no significant variation among local data, a single global model may provide appropriate recommendations for all districts. Unfortunately this is not common in Indonesia where regional disparities are very large. Geographically weighted regression (GWR) is an alternative approach to provide local specific recommendations. The paper compares between global model and local specific models of Poisson regression. The secondary data set used in this study is obtained from Podes (Village Potential Data) of 2008 in Java. Malnutrition as the outcome variable is the number of malnourished patients in a district. The parameter estimation in the local models used a weighting matrix accommodating the proximity among locations. Iterative Fisher scoring is used to solve the parameter estimation process. The corrected AIC shows that geographically weighted Poisson model produces better performance than the global model. Variables indicating poverty are the most influencing factors to the number of malnourished patients in a region followed by variables related to health, education, and food. The local parameter estimates based on the geographically weighted Poisson models can be used for specific recommendations
Analisis Faktor – Faktor yang Mempengaruhi Jumlah Kejahatan Pencurian Kendaraan Bermotor (Curanmor) Menggunakan Model Geographically Weighted Poisson Regression (Gwpr)
Theft is an act taking someone else's property, partially or entierely, with intention to have it illegally. Motor vehicle theft is one of the most highlighted crime type and disturbing the communities. Regression analysis is a statistical analysis for modeling the relationships between response variable and predictor variable. If the response variable follows a Poisson distribution or categorized as a count data, so the regression model used is Poisson regression. Geographically Weighted Poisson Regression (GWPR) is a local form of Poisson regression where data sampling location is prioritized. GWPR model is used for identifying the factors that influence the numbers of motor vehicles theft, either using a weighted gauss kernel function or bisquare kernel function. Based on the value of Akaike Information Criterion (AIC) of Poisson regression and GWPR model, it is analyzed that GWPR model using a weighted fixed bisquare kernel function is the best model for analyzing the number of motor vehicles theft at every Sub-Districts in the Semarang city in 2012, because it has the smallest AIC value. This model has a precision of 88,81%
Penaksiran Parameter dan Statistik Uji Model Mixed Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (Studi Kasus: Jumlah Kematian Bayi dan Jumalh Kematian Ibu di Kota Surabaya Tahun 2015
Regresi poisson merupakan analisis regresi nonlinear dari distribusi poisson yang digunakan dalam menganalisis data diskrit. Pada regresi poisson mensyaratkan kondisi dimana nilai mean dan varians dari variabel respon bernilai sama atau kondisi equidispersion. Namun dalam kasus banyak terjadi overdispersion atau underdispersion. Mixed poisson distribution merupakan solusi alternatif untuk kasus overdispersi maupun underdispersi. Salah satu metode untuk mengatasinya adalah distribusi Poisson Inverse Gaussian (PIG). Pada Poisson Inverse Gaussian tidak semua kasus yang hanya melibatkan satu varibel respon, karena dalam kenyataannya beberapa kasus akan melibatkan lebih dari satu variabel respon. Dalam penelitian ini dilakukan pengembangan model regresi bivariat yang melibatkan faktor spasial yaitu dengan adanya pembobot geografis. Pada kenyataannya tidak semua variabel dalam model geographically weighted regression mempunyai pengaruh secara spasial, ada beberapa variabel prediktor berpengaruh secara global. Penelitian ini menghasilkan estimator parameter model menggunakan Maximum Likelihood Estimation (MLE) dengan iterasi Newton-Raphson. Selanjutnya mendapatkan statistik uji pada model Mixed Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (MGWBPIGR) menggunakan MLRT. Penerapan model Mixed Geographically Weighted Bivariate Poisson Inverse Gaussian Regression yang terbentuk variabel prediktor yang berpengaruh secara signifikan terhadap jumlah kematian bayi dan jumlah kematian ibu di Kota Surabaya tahun 2015 adalah variabel rasio tenaga kesehatan, persentase persalinan oleh tenaga kesehatan, persentase ibu hamil mendapatkan tablet Fe3, persentase rumah tangga ber-PHBS dan rasio puskesmas.
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Poisson regression is a nonlinear regression analysis of the poisson distribution used in analyzing discrete data. In poisson regression requires conditions where the mean and variance values of the response variable are equal or equidistpersion conditions. But in case of over dispersion or under dispersion occurs. Mixed poisson distribution is an alternative solution for over dispersion and under dispersion cases. One method to overcome this is the Poisson Inverse Gaussian (PIG) distribution. In Poisson Inverse Gaussian not all cases involve only one response variable, because in reality some cases will involve more than one response variable. This research, the development of bivariate regression model which involves spatial factor that is with the geographic weighting. In fact, not all variables in the geographically weighted regression model have spatial influence, there are several predictor variables globally. The result of this studied is parameter estimation using Maximum Likelihood estimation (MLE) with Newton-Raphson iteration. Furthermore, statistic test on the Mixed Geographically Weighted Bivariate Poisson Inverse Gaussian Regression Model (MGWBPIGR) model. The application of the Mixed Geographically Weighted Bivariate Poisson Inverse Gaussian Regression model which is the predictor variable that significantly affects the number of infant mortality and the number of maternal deaths in Surabaya City 2015 is the ratio of health personnel, the percentage of births by health personnel, the percentage of pregnant women get Fe3 tablets, percentage of households with PHBS and ratio of puskesmas
PENERAPAN MODEL GEOGRAPHICALLY WEIGHTED POISSON REGRESSION SEMIPARAMETRIC (GWPRS) TERHADAP KASUS ANGKA KEMATIAN IBU DI JAWA BARAT
Geographically weighted poisson regression semiparametric (GWPRS) merupakan metode spasial yang dikembangkan dari model Geographically weighted poisson regression (GWPR) yang mengkombinasikan parameter-parameter bersifat lokal dengan parameter-parameter bersifat global terhadap lokasi. Kematian Ibu merupakan salah satu indikator yang peka terhadap kualitas dan aksesibilitas fasilitas pelayanan kesehatan. Tujuan penelitian ini adalah mendapatkan model GWPRS dengan pembobot fungsi kernel terbaik pada data Jumlah Kematian Ibu di Jawa Barat tahun 2013. Pada penelitian ini model GWPRS dengan pembobot fungsi kernel Adaptive Bisquare merupakan model terbaik untuk menggambarkan data Jumlah Kematian Ibu di Jawa Barat tahun 2013 karena menghasilkan nilai AICc terkecil dibandingkan dengan pembobot fungsi kernel lainnya.
Geographically Weighted Poisson Regression Semiparametric (GWPRS) is a spatial method developed from the Geographically weighted poisson regression model (GWPR) that can be combined between the global parameters and local parameters to the location. Maternal mortality is one of the indicators which is sensitive to the quality and the accessibility of health care facility. The purpose of this research is to get the GWPRS model with the best kernel weighted function on the Number of Maternal Deaths data in West Java in 2013. GWPRS model with weighted kernel of Adaptive Bisquare function is the best model to elaborate data about the Maternal mortality in west java 2013 as it produces the smallest AICc value which is compared with other weighted kernel functions
POISSON REGRESSION MODELS TO ANALYZE FACTORS THAT INFLUENCE THE NUMBER OF TUBERCULOSIS CASES IN JAVA
Tuberculosis is an infectious disease and one of the world's top 10 highest causes of mortality in Indonesia. Based on this fact, it is necessary to study what factors affect number of tuberculosis cases. The number of tuberculosis cases as dependent variable is a count data that generally analyzed using Poisson regression. However, equidispersion assumption must be met, so Generalized Poisson Regression and Negative Binomial Regression are applied if the assumption is not met. Spatial aspects can be considered so Geographically Weighted Generalized Poisson Regression and Geographically Weighted Negative Binomial Regression were also conducted. Four models were built to evaluate relationship between number of tuberculosis cases and factors affecting it in Java in 2020. The explanatory variables are population density, percentage of children receiving BCG immunization, percentage of poor people, percentage of eligible drinking water facilities, percentage of family cards with access to proper sanitation, percentage of public places meet health requirements, and percentage of food management places meet hygienic requirements. This study shows that the best model for modeling the data is GWNBR with 2 groups of significant explanatory variables. Seven explanatory variables are statistically significant in 88 districts and six explanatory variables statistically significant in 12 districts
PEMODELAN SPASIAL KEMISKINAN DENGAN MIXED GEOGRAPHICALLY WEIGHTED POISSON REGRESSION DAN FLEXIBLY SHAPED SPATIAL SCAN STATISTIC (Studi Kasus: Jumlah Rumah Tangga Sangat Miskin di Kabupaten Kulonprogo)
Analisis regresi merupakan salah satu analisis statistika yang digunakan untuk membuat model antara variabel respon dengan variabel prediktor. Salah satu analisis regresi yang dapat digunakan apabila variabel respon berupa data count adalah analisis regresi Poisson. Geographically Weighted Poisson Regression (GWPR) merupakan bentuk lokal dari regresi Poisson dimana lokasi pengambilan data diperhatikan. Dalam penelitian ini akan digunakan metode Mixed Geographically Weighted Poisson Regression (Mixed GWPR) yang merupakan bentuk lokal dari regresi Poisson dan merupakan gabungan dari metode nonparametrik dan parameterik dimana faktor lokasi diperhatikan. Sebagai studi kasus digunakan data jumlah rumah tangga sangat miskin per desa/kelurahan di Kabupaten Kulonprogo, Provinsi DI Yogyakarta dimana sejak 2010-2012 menjadi provinsi dengan persentase kemiskinan tertinggi di Pulau Jawa. Hasil perbandingan antara regresi Poisson, GWPR, dan Mixed GWPR memberikan kesimpulan bahwa Mixed GWPR dengan pembobot fungsi kernel Adaptive Bisquare merupakan model terbaik untuk menganalisis jumlah rumah tangga sangat miskin di Kabupaten Kulonprogo tahun 2011 karena memiliki nilai Akaike Information Criterion (AIC) terkecil. Selain itu, untuk mengetahui desa/kelurahan yang akan dijadikan prioritas lokasi pengentasan kemiskinan maka dilakukan deteksi hotspot/kantong kemiskinan dengan metode Flexibly Shaped Spatial Scan Statistic dimana diperoleh hasil bahwa di Kabupaten Kulonprogo terdapat tiga kantong kemiskinan.
Kata kunci: AIC, Flexibly Shaped Spatial Scan Statistic, Kantong Kemiskinan,
Mixed GWPR, Rumah Tangga Sangat Miski
Pemetaan Jumlah Property Crime di Provinsi Jawa Timur Menggunakan Metode Geographically Weighted Negative Binomial Regression (GWNBR) dan Geographically Weighted Poisson Regression (GWPR)
Kriminal merupakan suatu kegiatan yang melanggar hukum. Ada beberapa faktor yang mempengaruhi para kriminal melakukan tindakan kejahatan antara lain kemiskinan, kesempatan kerja, dan karakter pelaku yang melakukan kejahatan. Selain itu ada pula faktor lain yang mempengaruhi timbulnya kejahatan yaitu kepadatan penduduk, jumlah patroli polisi, keadaan jalan dan lingkungan, frekuensi ronda siskamling, dan faktor lainnya. Property crime merupakan kategori kejahatan yang termasuk di dalamnya yaitu pencurian, pengambilan sesuatu yang melanggar hukum, perampokan, kejahatan dengan pembakaran, dan perusakan properti. Seringkali kejadian kriminalitas akan saling berdampak dari satu wilayah ke wilayah yang lainnya. Untuk menyelesaikan kasus tersebut diperlukan suatu pemodelan dengan metode spasial kerena memperhatikan kondisi geografis yang ada di provinsi Jawa Timur. Pemodelan dengan memperhatikan faktor spasial menggunakan GWNBR dan GWPR, dimana setiap wilayah pasti memiliki kondisi geografis yang berbeda sehingga menyebabkan adanya perbedaan jumlah Property crime antara wilayah satu dengan wilayah yang lainnya sesuai dengan karakteristik wilayah tersebut. Hasil pemodelan dengan metode GWNBR terbentuk dua kelompok kabupaten/kota menurut variabel yang berpengaruh signifikan terhadap jumlah kasus Property crime. Hasil pemodelan dengan metode GWPR menunjukkan bahwa kelompok kabupaten/kota menurut variabel yang berpengaruh signifikan terhadap jumlah kasus Property crime sebanyak 16 kelompok. Berdasarkan kriteria AIC terkecil menunjukkan bahwa metode GWNBR merupakan metode yang paling sesuai untuk memodelkan jumlah kasus Property crime setiap kabupaten/kota di Jawa Timur dibandingkan dengan metode regresi Poisson, regresi binomial negatif, dan GWPR
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