Jumlah penduduk miskin di Jawa Tengah pada Maret 2020 sebesar 3,98 juta orang (11,41%), peringkat kedua terbesar di Pulau Jawa. Angka jumlah penduduk miskin yang relatif tinggi menjadi prioritas bagi pemerintah untuk menanggulangi kemiskinan. Salah satu cara penanggulangan kemiskinan adalah dengan mengetahui faktor kemiskinan. Penelitian ini bertujuan untuk mengidentifikasi faktor kemiskinan di Jawa Tengah menggunakan metode Structural Equation Modelling-Partial Least Squares (SEM-PLS). Data yang digunakan dalam penelitian ini adalah data kabupaten/kota di Jawa Tengah tahun 2020. Pada kasus ini, terdapat satu peubah laten eksogen kesehatan dan tiga peubah laten endogen kemiskinan, ekonomi, dan sumberdaya manusia. Permasalahan yang ditemui adalah data amatan relatif kecil yaitu 35 amatan serta sebaran data tidak memenuhi asumsi kenormalan, sehingga analisis yang tepat digunakan dalam penelitian ini adalah Structural Equation Modelling-Partial Least Squares (SEM-PLS). Hasil penelitian menunjukkan bahwa peubah laten ekonomi dan Sumber Daya Manusia memiliki pengaruh positif tetapi tidak signifikan. Peubah laten kesehatan memiliki pengaruh negatif dan signifikan terhadap peubah laten kemiskinan. Nilai Q2 untuk peubah laten kemiskinan adalah 0,333, hal ini menunjukkan bahwa sebesar 33,3% keragaman peubah laten kemiskinan dapat dijelaskan oleh peubah laten ekonomi, kesehatan, dan sumber daya manusia. Kata Kunci: Kemiskinan, Structural Equation Modeling (SEM), Partial Least Square (PLS)
Spatial regression measures the relationship between response and explanatory variables in the regression model considering spatial effects. Detecting and accommodating outlier is an important step of the regression analysis. Several methods can detect outliers in spatial regression. One of this method is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model. This research applies robust spatial autoregressive (RSAR) model with S- estimator to the Original Local Government Revenue (OLGR) data. The RSAR model with the 4-nearest neighbour weighting matrix is the best model produced in this study. Coefficient of the RSAR model gives the more relevant result. Median absolute deviation (MdAD) and median absolute percentage error (MdAPE) values in RSAR model with 4-nearest neighbour give smaller result than SAR model.mean shift
Ordinal logistic regression is a method describing the relationship between an ordered categorical response variable and one or more explanatory variables. The parameter estimation of this model uses the maximum likelihood estimation having assumption that each sample unit having an equal chance of being selected, or using simple random sampling (SRS) design. This study uses data from the National Socio-Economic Survey (SUSENAS) having two-stage one-phase sampling (not SRS). So, the parameter estimation should consider the sampling weights. This study describes the parameter estimation of the ordinal logistic regression with sampling weight using the pseudo maximum likelihood method, especially in SUSENAS sampling design framework. The variance estimation method uses Taylor linearization. This study also provides numerical examples using ordinal logistic regression with sampling weight. Data used is 121,961 elderly spread over 514 districts/cities. Testing data (20%) is used to obtain the accuracy of the prediction results. The variables used in this study are the health status of the elderly as the response variable, and nine explanatory variables. The results of this study indicate that the ordinal logistic regression model with sampling weights is more representative of the population and more capable to predict minority categories of the response variable (poor and moderate health status) than is without sampling weights.
Senior high school in Indonesia is divided into two groups, namely Natural science and Social science. Those grouping of majors is allegedly not appropriate enough the potential of students yet because of the multiple intelligence of each student is different. This study aims to establish an extracurricular program for everyone grouped by multiple intelligences carried out by each student. The method used in this study are the non-hierarchical clustering k-Means and hierarchical clustering Ward method. The k-Means method used to determine the effective number of groups, while Ward method used to identify the member of each cluster and the recommendation of extracurricular in the cluster obtained. Based on the results of the clustering analysis, there are five clusters obtained, Language and Fine Arts; Communication; Leadership; Nature Lovers; and also Design and Photography.
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