Indeks Pembagunan Manusia (IPM) mer

Indeks Pembagunan Manusia (IPM) merupakan suatu indeks komposit yang mencakup tiga dimensi pokok pembangunan manusia yang dinilai mencerminkan status kemampuan dasar penduduk yaitu kesehatan, pencapaian pendidikan, dan daya beli masyarakat. Pada penelitian ini IPM dikategorikan/diklasifikasikan menjadi dua, yaitu menengah rendah dan menengah tinggi. Secara data didapati bahwa terdapat kemiripan nilai IPM antar wilayah yang berdekatan secara geografis yang mengakibatkan klasifikasi IPM wilayah yang berdekatan tersebut sama. Hal ini diduga karena adanya dependensi/keterkaitan hubungan antar wilayah. Dari penjabaran di atas, maka data IPM dalam penelitian ini didasarkan pada metode regresi probit spasial. Penelitian ini bertujuan mengkaji penaksir parameter yang diaplikasikan pada data klasifikasi IPM di Pulau Jawa dan memodelkan faktor-faktor yang mempengaruhi klasifikasi IPM di pulau Jawa serta menentukan model terbaik dengan menggunakan model regresi probit spasial autoregressive. Pemilihan variabel dilakukan secara backward elimination, sedangkan estimasi parameter dilakukan menggunakan metode MCMC Gibbs sampler dengan pendekatan Bayesian. Menggunakan delapan variabel prediktor, dua variabel diantaranya yaitu variabel persentase penduduk miskin dan rata-rata umur kawin pertama wanita dinyatakan signifikan secara statistik untuk dimasukkan ke dalam model.

Hasil (Inggris) 1:

Man its construction index (HDI) is a composite index that includes three basic dimensions of human development are assessed reflect the status of the basic capabilities of the health, educational achievement, and purchasing power. In this research the HDI classified/classified into two, namely low and medium to medium high. In the data found that similarity between the HDI value of adjacent area geographically that resulted in the classification of the adjacent territories of the HDI is the same. This is allegedly due to dependencies/relationship relationships between regions. From the discussion above, then the data of the HDI in this study is based on a spatial probit regression method. This research aims to study the penaksir parameter which applicated on classification data in Java and HDI model the factors that affect the classification of the HDI in Java as well as determine the best model by using spatial autoregressive probit regression model. The selection of variables is done backward elimination, while parameter estimation is performed using the method of MCMC Bayesian approach with Gibbs sampler. Use the predictor variables, two of eight variables such that the variable percentage of the poor population and the average age of first marriage for women was statistically significant for inclusion in the model.

Hasil (Inggris) 2:

Human pembagunan Index (HDI) is a composite index that includes three basic dimensions of human development are considered to reflect the status of the population's basic capabilities of health, educational attainment, and purchasing power. In this study IPM categorized / classified into two, namely the low-medium and medium-high. In the data found that there are similarities between the HDI value geographically adjacent regions which resulted in the classification of the area adjacent HDI same. This is presumably due to the dependency / inter-regional linkages. From the explanation above, then the data HDI in this study are based on spatial probit regression method. This study aims to assess the estimator parameters are applied to the data classification HDI in Java and modeling the factors that affect the classification of IPM in Java as well as determine the best model using spatial autoregressive probit regression model. Selection is done by backward elimination of variables, while the parameter estimation is done using MCMC methods Gibbs sampler with a Bayesian approach. Using eight predictor variables, two of which variable is the variable percentage of the poor and the average age at first marriage of women was statistically significant for inclusion in the model.