Pengaruh Hukuman Mati Terhadap Dinamika Jumlah Pengguna Narkoba Di Indonesia

Tulisan ini membahas model matematika dinamika jumlah pengguna narkoba dengan memperhatikan hukuman mati yang sudah diberlakukan pemerintah Indonesia. Tujuannya untuk melihat seberapa besar dampak hukuman mati tersebut terhadap dinamika jumlah pengguna narkoba di Indonesia. Model matematika dari permasalahan ini dinyatakan dalam bentuk sistem persamaan diferensial nonlinierberdasarkan asumsi-asumsi yang digunakan. Selanjutnya model yang sudah dibentuk, dianalisis dan hasilnya diinterpretasikan kembali ke masalah nyat

Pengaruh Hukuman Mati terhadap Dinamika Jumlah Pengguna Narkoba di Indonesia

Tulisan ini membahas model matematika dinamika jumlah pengguna narkoba dengan memperhatikan hukuman mati yang sudah diberlakukan pemerintah Indonesia. Tujuannya untuk melihat seberapa besar dampak hukuman mati tersebut terhadap dinamika jumlah pengguna narkoba di Indonesia. Model matematika dari permasalahan ini dinyatakan dalam bentuk sistem persamaan diferensial nonlinierberdasarkan asumsi-asumsi yang digunakan. Selanjutnya model yang sudah dibentuk, dianalisis dan hasilnya diinterpretasikan kembali ke masalah nyat

Eksistensi Dan Kestabilan Model SIR Dengan Nonlinear Insidence Rate

Diberikan model epidemi SIR dengan laju insidensi penularan berbentukbI 2 S . Pada model tersebut diselidiki eksistensi dan kestabilan titik ekuilibrium bebas penyakit dan endemik. Berdasarkan hasil penyelidikan didapat bahwa, terdapat satu titik ekuilibrium bebas penyakit dan dua buah titik ekuilibrium endemik. Titk ekuilibrium bebas penyakit stabil global, sedang titik ekuilibrium endemik masing-masing stabil lokal untuk suatu kondisi yang dipersyaratka

CLUSTERING OF STATE UNIVERSITIES IN INDONESIA BASED ON PRODUCTIVITY OF SCIENTIFIC PUBLICATIONS USING K-MEANS AND K-MEDOIDS

Scientific publication is a measure of the performance of a university. Universities that are owned and operated by the government and whose establishment is carried out by the President of Republic Indonesia are state universities (PTN). One of the efforts that can be made to determine the quantity and quality of state university scientific publications is to conduct PTN clustering based on the productivity of scientific publications. This clustering aims to see the position of state universities in Indonesia into 3 categories, namely “high”, “medium”, and “low”. One of the clustering methods that can be used is cluster analysis. The cluster analysis used in this study is k-means and k-medoids with Silhoutte's validity. Based on the results of the analysis, it was found that the Silhouette k-means value (0.8018) was higher than the Silhouette k-medoids value (0.7281). Therefore, in this case, it can be concluded that the k-means method is better than the k-medoids. The results of cluster analysis using K-Means are 1) PTN with high productivity of scientific publications, namely ITB, ITS, UGM, and UI. The four PTNs are PTN as Legal Entity (PTN-BH) located in Java, 2) PTN with medium scientific publication productivity consists of 16 PTN which were dominated by PTN-BH and PTN as Public Service Board (PTN-BLU) with the largest location in Java, and 3) PTN with low scientific publication productivity consisted of 102 PTN which were dominated by PTN as general state financial management (PTN-Satker) with most locations outside Java

Eksistensi dan Kestabilan Model SIR dengan Nonlinear Insidence Rate

Diberikan model epidemi SIR dengan laju insidensi penularan berbentukbI 2 S . Pada model tersebut diselidiki eksistensi dan kestabilan titik ekuilibrium bebas penyakit dan endemik. Berdasarkan hasil penyelidikan didapat bahwa, terdapat satu titik ekuilibrium bebas penyakit dan dua buah titik ekuilibrium endemik. Titk ekuilibrium bebas penyakit stabil global, sedang titik ekuilibrium endemik masing-masing stabil lokal untuk suatu kondisi yang dipersyaratka

Clustering of State Universities in Indonesia based on Productivity of Scientific Publications Using K-Means and K-Medoids

Scientific publication is a measure of the performance of a university. Universities that are owned and operated by the government and whose establishment is carried out by the President of Republic Indonesia are state universities (PTN). One of the efforts that can be made to determine the quantity and quality of state university scientific publications is to conduct PTN clustering based on the productivity of scientific publications. This clustering aims to see the position of state universities in Indonesia into 3 categories, namely “high”, “medium”, and “low”. One of the clustering methods that can be used is cluster analysis. The cluster analysis used in this study is k-means and k-medoids with Silhoutte's validity. Based on the results of the analysis, it was found that the Silhouette k-means value (0.8018) was higher than the Silhouette k-medoids value (0.7281). Therefore, in this case, it can be concluded that the k-means method is better than the k-medoids. The results of cluster analysis using K-Means are 1) PTN with high productivity of scientific publications, namely ITB, ITS, UGM, and UI. The four PTNs are PTN as Legal Entity (PTN-BH) located in Java, 2) PTN with medium scientific publication productivity consists of 16 PTN which were dominated by PTN-BH and PTN as Public Service Board (PTN-BLU) with the largest location in Java, and 3) PTN with low scientific publication productivity consisted of 102 PTN which were dominated by PTN as general state financial management (PTN-Satker) with most locations outside Java

Model Multivariate Adaptive Geographically Weighted Generalized Poisson Regression Splines (Magwgprs) (Studi Kasus: Pemodelan Banyaknya Kasus Demam Berdarah Dengue)

Multivariate Adaptive Generalized Poisson Regression Splines (MAGPRS) merupakan kombinasi antara Multivariate Adaptive Regression Splines (MARS) dan Generalized Poisson Regression (GPR). MAGPRS merupakan model MARS dengan respon count yang mampu mengatasi masalah equidispersion. Namun, model MAGPRS belum mempertimbangkan efek spasial pada data. Banyak kasus yang berkaitan dengan efek spasial (kewilayahan), diantaranya persebaran penyakit menular yang berbeda antar wilayah pengamatan. Perbedaan tersebut terjadi karena adanya perbedaan karakteristik antar lokasi pengamatan, yaitu perbedaan sosial ekonomi, kepadatan penduduk, tingkat pendidikan, kondisi lingkungan, dan sebagainya. Salah satu efek spasial ini adalah heterogenitas spasial (terjadinya pelanggaran asumsi homogenitas spasial). Tujuan penelitian ini adalah mengembangkan MAGPRS yang memperhatikan heterogenitas spasial, diberi nama dengan Multivariate Adaptive Geographically Weighted Generalized Poisson Regression Splines (MAGWGPRS). Model MAGWGPRS dibentuk berdasarkan fungsi basis MAPRS. Model tersebut dianalisis untuk mendapatkan penaksir parameter dan statistik uji hipotesis model. Penaksir parameter model menggunakan metode Maximum Likelihood Estimation (MLE) terboboti dengan metode iterasi Berndt-Hall-Hall-Hausman (BHHH). Pengujian hipotesis parameter model secara serentak dilakukan dengan Maximum Likelihood Ratio Test (MLRT) dan secara parsial dengan uji Wald. Tujuan berikutnya menerapkan model MAGWGPRS terhadap banyaknya kasus demam berdarah dengue (DBD) di kabupaten/kota Pulau Jawa. Berdasarkan kombinasi BF, MI, dan MO, dipilih satu model MAPRS terbaik dan satu model MAPRS lainnya yang fungsi basisnya berturut-turut dijadikan sebagai fungsi basis MAGWGPRS 1 dan MAGWGPRS 2. Hasil penelitian menunjukkan bahwa penaksir parameter kedua model tidak closed form dan diselesaikan secara numerik menggunakan metode BHHH. Masing-masing model MAGWGPRS 1 dan MAGWGPRS 2 juga menghasilkan fungsi basis yang berpengaruh signifikan berbeda untuk setiap kabupaten/kota di Jawa. Model MAGWGPRS 1 lebih menggambarkan heterogenitas spasial dibandingkan dengan model MAGWGPRS 2. Berdasarkan jumlah fungsi basisnya, model MAGWGPRS 1 memiliki 23 fungsi basis dan model MAGWGPRS 2 memiliki 11 fungsi basis. Berdasarkan nilai Mean of Square Error (MSE), model MAGWGPRS 1 lebih baik dibandingkan model MAGWGPRS 2 dalam memodelkan banyaknya kasus DBD di kabupaten/kota di Pulau Jawa pada tahun 2020. ===================================================================================================================================== Multivariate Adaptive Generalized Poisson Regression Splines (MAGPRS) is a combination of Multivariate Adaptive Regression Splines (MARS) and Generalized Poisson Regression (GPR). MAGPRS is a MARS model with a count response that can overcome the problem of equidispersion. However, the MAGPRS model has not considered spatial effects on the data. Many cases are related to spatial effects, including the distribution of infectious diseases that differ between observation areas. This difference occurs due to differences in characteristics between observation locations, namely socio-economic differences, population density, education levels, environmental conditions, and so on. One of these spatial effects is spatial heterogeneity (violation of the assumption of spatial homogeneity). The purpose of this research is to develop MAGPRS that takes into account spatial heterogeneity, named Multivariate Adaptive Geographically Weighted Generalized Poisson Regression Splines (MAGWGPRS). The MAGWGPRS model is formed based on the MAPRS basis function. The model was analyzed to obtain parameter estimators and model hypothesis test statistics. The model parameter estimator uses the Maximum Likelihood Estimation (MLE) method and the Berndt-Hall-Hausman (BHHH) iteration method. Hypothesis testing of model parameters simultaneously is done with the Maximum Likelihood Ratio Test (MLRT) and partially with the Wald test. The next objective is to apply the MAGWGPRS model to the number of dengue hemorrhagic fever (DHF) cases in the districts/cities of Java Island. Based on the combination of BF, MI, and MO, one best MAPRS model and one other MAPRS model were selected whose basis functions were used as MAGWGPRS 1 and MAGWGPRS 2 basis functions, respectively. The results showed that the parameter estimators of the two models were not closed form and were solved numerically using the BHHH method. Each of the MAGWGPRS 1 and MAGWGPRS 2 models also produced significantly different basis functions for each district/city in Java. Based on the number of basis functions, the MAGWGPRS 1 model has 23 basis functions and the MAGWGPRS 2 model has 11 basis functions. Based on the Mean of Square Error (MSE) value, the MAGWGPRS 1 model is better than the MAGWGPRS 2 model in modeling the number of dengue cases in districts/cities in Java in 2020

Segmentation of toddler nutritional status using REBUS and FIMIX partial least square in Southeast Sulawesi

Nutrition is one of the important factors that play a major role in the growth and development of children so that they can develop optimally. Child malnutrition, such as stunting, underweight, and wasting, is a significant problem in Indonesia. The World Health Organization (WHO) determined that the nutritional status of children under five in Indonesia is in the chronic category, one of which is in Southeast Sulawesi Province. This study examines and analyzes the factors that influence the nutritional status of children under five in Southeast Sulawesi using the Partial Least Square Structural Equation Modeling (SEM-PLS) method and then segments the nutritional status of children under five using Response Based Unit Segmentation Modeling in Partial Least Square (REBUS-PLS) and Finite Mixture Partial Least Square (FIMIX-PLS). The number of observations in this study was 216 sub-districts. From the results of the SEM-PLS analysis conducted, it was concluded that the 10 indicators used were valid and significant in describing the latent variables, and the practice factor variable had an effect on the food factor variable, the food factor variable had an effect on the service factor variable, and the service factor variable had an effect on the under-five nutritional status variable. The REBUS-PLS analysis results in two segments, with one segment of 75 observations and the other segment of 141 observations. The same conclusion is obtained as in the SEM-PLS analysis, but the results of the analysis with REBUS-PLS have a greater value than the results of the SEM-PLS analysis. Key points of the article: 1. Comparing REBUS-PLS and FIMIX-PLS methods for overcoming the case of heterogeneity in dat. 2. Combining the SEM-PLS method with the REBUS and FIMIX methods in discussing the factors that influence the nutritional status of children under five in Southeast Sulawesi