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Journal : Media Statistika

PENGELOMPOKAN DAERAH PENGHASIL BAHAN DASAR TEPUNG KOMPOSIT DI INDONESIA MENGGUNAKAN METODE LATENT CLASS CLUSTER ANALYSIS (LCCA) Budiati, Shinta; Susanto, Irwan; Wibowo, Supriyadi
MEDIA STATISTIKA Vol 7, No 1 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (522.365 KB) | DOI: 10.14710/medstat.7.1.21-28

Abstract

Wheat as a base substance of flour, is a source of carbohydrate which is most used for the manufacturing of variety of foodstuffs. Substitution a part of flour with composite flour for manufacturing food will decrease dependency of imported wheat.This research aims to classify the area which produce base substance of composite flour in Indonesia.For this research we will know a group of provinces which become center of production and development target of local resources potency. One way that is used to grouping the object is cluster analysis. In development, there is another grouping technique used, namely Latent Class Cluster Analysis (LCCA).The results show that the selected model from grouping using LCCA is 3groups. The first group is the enough potential area as a production development center. While the second group have the greatest potential area. Meanwhile the last group is the less potentially area.   Keywords: Composite Flour, Cluster Analysis, Latent Class Cluster Analysis (LCCA)  
PENGELOMPOKAN RUMAH TANGGA DI INDONESIA BERDASARKAN PENDAPATAN PER KAPITA DENGAN MODEL FINITE MIXTURE Irwan Susanto; Sri Sulistijowati Handajani
MEDIA STATISTIKA Vol 13, No 1 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.616 KB) | DOI: 10.14710/medstat.13.1.13-24

Abstract

In the statistical modeling framework, the form of the income distribution can be approaching based on certain statistical distributions. The use of the finite mixture model is relatively flexible in the modeling of the income distribution that has a multimodal pattern. The multimodal pattern can be indicated as the existence of different cluster on the data. The different clusters which can reflect the economic homogeneity of income are represented by the mixture components of the finite mixture model. In this paper, the finite mixture model is implemented for modeling the distribution of household income per capita in Indonesia based on The Fifth Wave of the Indonesia Family Life Survey (IFLS5) 2014-2015. The mixture components of the finite mixture model have been build based on the heavy-tailed statistical distributions, i.e., gamma, lognormal, and Weibull distributions. The estimation of the fitting finite mixture model was conducted using the maximum-likelihood estimation method through the expectation-maximization (EM) algorithm. The suitable finite mixture models were verified with the bootstrap likelihood ratio statistics test, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Based on the results, the distribution of household income per capita in Indonesia can be modeled by the four components-lognormal mixture model.