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All Journal Media Statistika Jurnal Ketahanan Nasional Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Indonesian Journal of Statistics and Its Applications
Septiadi Padmadisastra
Departemen Statistik, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Padjajaran
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Meningkatkan Ketahanan Wilayah Melalui Estimasi Underreported Data Kejahatan Menggunakan Pendekatan Bayes Herlin Venny Johannes; Septiadi Padmadisastra; Bertho Tantular
Jurnal Ketahanan Nasional Vol 23, No 3 (2017)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jkn.29197

Abstract

ABSTRACTThis paper present a study for the number of crime that run into underreporting counts. The purpose of the analysis is to estimate parameter of the model which is the actual number of crime. The model is a mixture of the poisson and the binomial distributions developed by Winkelmann (1996). The parameters of the model are estimated by Bayesian approach and Markov Chain Monte Carlo simulation using Gibbs sampling algorithm. Determination the convergence of the algorithm using trace plot, autocorrelation plot and ergodic mean plot. In the end, estimator of the parameters of the underreported counts model are the simulation sample mean that calculated from the simulation sample of iteration after burn in period until the last iteration.ABSTRAKPenelitian ini mengkaji permodelan data tingkat kejahatan yang mengalami underreporting counts. Tujuan analisis ini adalah untuk menaksir parameter model yaitu banyaknya jumlah tindak kejahatan yang sebenarnya.  Model yang digunakan adalah hasil penggabungan antara distribusi poisson dan distribusi binomial yang dikembengkan oleh Winkelmann (1996). Penaksiran parameter model dilakukan melalui pendekatan bayes dan simulasi Markov Chain Monte Carlo menggunakan algoritma gibbs sampling. Penentuan konvergensi algoritma akan dilakukan melalui trace plot, autocorrelation plot, dan ergodic mean plot. Taksiran parameter model diperoleh dari rata-rata nilai sampel hasil simulasi yang dihitung dari iterasi setelah burn in period sampai dengan iterasi yang terakhir.
PENGELOMPOKAN RUMAH TANGGA MISKIN DI KECAMATAN TABIR BARAT MENGGUNAKAN METODE LATENT CLASS CLUSTER ANALYSIS Irtania Muthia Rizki; Septiadi Padmadisastra; Bertho Tantular
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 9 No 2 (2017): Jurnal Ilmiah Matematika dan Pendidikan Matematika
Publisher : Jurusan Matematika FMIPA Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2017.9.2.2867

Abstract

Poverty is one of the problems that becomes concern in all countries. In Indonesia, one of the provinces that has high poverty rates is Jambi (9.12% in 2015).Result of coordination meeting of all Camat in Jambi Province, reported that the Tabir Barat is the poorest sub-district. The condition is caused mostly by inadequate household infrastructure. Therefore it is necessary for grouping households based on the household infrastructure condition to find the household groups which should be prioritized in the development of poverty alleviation. To describe the poverty variable based on household infrastructure, Bappeda uses 9 indicators, that are residential building status, the widest type of floor, the widest type of wall, the widest type of roof, drinking water source, defecation facility, stool drainage, main lighting and cooking fuel. Because of the folowing reasons: the poverty is an unmeasurable latent variable, and indicators of poverty are categorial variables, the Latent Class Cluster analysis were used in this research as a grouping method. The result shows that there are 5 clusters / latent classeswith their respective characteristics of the household in the Tabir Barat.
ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI Yogo Aryo Jatmiko; Septiadi Padmadisastra; Anna Chadidjah
MEDIA STATISTIKA Vol 12, No 1 (2019): 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 (403.528 KB) | DOI: 10.14710/medstat.12.1.1-12

Abstract

The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. Results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.
PENGELOMPOKAN RUMAH TANGGA MISKIN DI KECAMATAN TABIR BARAT MENGGUNAKAN METODE LATENT CLASS CLUSTER ANALYSIS Irtania Muthia Rizki; Septiadi Padmadisastra; Bertho Tantular
Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Vol 9 No 2 (2017): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2017.9.2.2867

Abstract

Poverty is one of the problems that becomes concern in all countries. In Indonesia, one of the provinces that has high poverty rates is Jambi (9.12% in 2015).Result of coordination meeting of all Camat in Jambi Province, reported that the Tabir Barat is the poorest sub-district. The condition is caused mostly by inadequate household infrastructure. Therefore it is necessary for grouping households based on the household infrastructure condition to find the household groups which should be prioritized in the development of poverty alleviation. To describe the poverty variable based on household infrastructure, Bappeda uses 9 indicators, that are residential building status, the widest type of floor, the widest type of wall, the widest type of roof, drinking water source, defecation facility, stool drainage, main lighting and cooking fuel. Because of the folowing reasons: the poverty is an unmeasurable latent variable, and indicators of poverty are categorial variables, the Latent Class Cluster analysis were used in this research as a grouping method. The result shows that there are 5 clusters / latent classeswith their respective characteristics of the household in the Tabir Barat.
POISSON REGRESSION OF DAMAGE PRODUCT SALES USING MCMC Reny Rian Marliana; Septiadi Padmadisastra
Indonesian Journal of Statistics and Applications Vol 2 No 1 (2018)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i1.53

Abstract

In this paper a model for the number of “damage” product sales is studied. The product sales are run into underreporting counts, caused by a delay on input process of the system called sales cycle. The goal of the study is to estimate the parameters of the regression model of product sales on an explanatory variable. It is the actual number of product sales. The model used is a mixture of the Poisson and the Binomial distributions. The parameters of the regression model are estimated by a Bayesian approach and Markov Chain Monte Carlo simulation using Gibbs sampling algorithm. The results of estimation clearly showed a gap between undamage product sales and the actual number. The gap is the number of damaged product sales.
Co-Authors Anna Chadidjah Bernik Maskun Bertho Tantular Denta Anggakusuma Herlin Venny Johannes Irtania Muthia Rizki Jadi Suprijadi Reny Rian Marliana Yogo Aryo Jatmiko