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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika Jurnal Riset Statistika Pattimura Proceeding : Conference of Science and Technology Bandung Conference Series: Statistics STATISTIKA
Nusar Hajarisman
Program Studi Statistika, Fakultas MIPA Universitas Islam Bandung (S1 - Akreditasi B/2014)
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics

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Journal : Statistika

 1   2 
Pendugaan Angka Kematian Bayi Melalui Model Regresi Poisson Bayes Berhirarki Dua-Level (Studi Kasus pada Kota Bandung, Provinsi Jawa Barat) Nusar Hajarisman; Aceng Komarudin Mutaqin; Anneke Iswani A.
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 13, No 2 (2013)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v13i2.1077

Abstract

In this paper, we address the issue of estimation of the hierarchical Bayesian models, especially forcount data in small area estimation problem. This model was developed by combining the existingterminology in generalized linear models with the concept of Bayes methods, especially hierarchicalBayes methods, such that it can be implemented to address the problem of small area estimation forsurvey data in the form of the count data. Development of this model starts by assuming that theobserved random variable is a member of the exponential family conditional on a certain parameter.The main objective of the development of this model is to make inference on these parameters are alsoconsidered as random variables. Then these parameters are modeled with the Fay-Herriot model asthe basic model of the small area estimation. Furthermore, the combination of both models will bestandardized in such a way as to represent a model within the framework of Bayes methods that willeventually form a two-level hierarchical Bayes Poisson model to solve problems in small areaestimation. The results of the development of this model is implemented to estimate the infantmortality rate in Bandung district, West Java Province.
Beberapa Statistik Odds Rasio untuk Analisis Data Kategorik Terurut Nusar Hajarisman
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 2 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i2.578

Abstract

Snell (1964) telah mengusulkan model regresi umum untuk analisis data kategorik yangterurut berdasarkan pada besarnya transformasi log odds. Berdasarkan model tersebut, beberapastatistik odds rasio sederhana telah dikembangkan untuk menentukan perbedaan lokasi antara duadistribusi dari peubah-peubah kategorik terurut. Selanjutnya juga akan digambarkan statistikstatistiklain yang sejenis yang berhubungan dengan peubah seperti itu (peubah kategorik terurut)
Pendekatan Fungsi Quasi-Likelihood dan Implementasinya dalam Sistem SAS Nusar Hajarisman
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 10, No 1 (2010)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v10i1.1009

Abstract

Seringkali perhatian utama peneliti ditekankan pada bagaimana rata-rata respons atau bentukfungsional lainnya dipengaruhi oleh satu atau lebih kovariat. Biasanya terdapat informasi priordalam mengamati kealamiahan bentuk hubungan tersebut, akan tetapi seringkali diperlukaninformasi dari kumulant atau moment yang berordo lebih tinggi (McCullagh dan Nelder, 1983).Dalam makalah ini akan dibahas mengenai bagaimana statistik inferens dapat dibuat berdasarkansuatu percobaan dimana tidak tersedia cukup informasi dalam membentuk fungsi likelihood, yaitumelalui pembentukan fungsi quasi-likelihood.
Pengembangan Model Regresi pada Peubah Respon Diskrit (Model Regresi Poisson ) Nusar Hajarisman
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 1, No 1 (2001)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v1i1.484

Abstract

Dalan banyak bidang penelitian mengenai habungan antord peubah rcsryn denganpcubah-peubahp redilaor, dimanap eubah rcsponnyam erupakanp eubah diskrit yangtidak biner. Botyalorya suatu kejadian dalam suatu unit tertentu diasurnsikans ebagaifingsi dari satu anu lebih peubah prediktor. Dalarn malcalah ini kita asumsilcanbahwa bahwa rata-rata dari banyalotya kejdian dalam unit tertentu merupakantpararneter dari distribusi Poisson. Rata-rata Poisson ini juga merupalcot fwgsi furipeubah-peubahp rediktor yang dapat dikembangkanm cnjadi model regresi Poisson.Dalam penerapannya, proses penaluiran poraneternya diperoleh melalui metodekzmwgkinan naksimum. Sebagai gambamn dari penerapan model regresi Poissonalcan dherila n sebuah contoh pemalcaiannya
Implementasi Model Poisson Bayes Berhirarki Dua-Level untuk Memodelkan Data Cacahan pada Masalah Pendugaan Area Kecil Nusar Hajarisman; Aceng Komarudin Mutaqin; Anneke Iswani Ahmad
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 12, No 2 (2012)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v12i2.1064

Abstract

In this paper, we address the issue of estimation of the hierarchical Bayesian models, especially forcount data in small area estimation problem. This model was developed by combining the existingterminology in generalized linear models with the concept of Bayes methods, especially hierarchicalBayes methods, such that it can be implemented to address the problem of small area estimation forsurvey data in the form of the count data. Development of this model starts by assuming that theobserved random variable is a member of the exponential family conditional on a certain parameter.The main objective of the development of this model is to make inference on these parameters are alsoconsidered as random variables. Then these parameters are modeled with the Fay-Herriot model asthe basic model of the small area estimation. Furthermore, the combination of both models will bestandardized in such a way as to represent a model within the framework of Bayes methods that willeventually form a two-level hierarchical Bayes Poisson model to solve problems in small areaestimation.
Statistical Process Control Vibrasi Bearing untuk Identifikasi Degradasi Riyani Desriawati; Sutawanir Darwis; Nusar Hajarisman; Suliadi Suliadi; Achmad Widodo
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 20, No 1 (2020)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v20i1.5298

Abstract

Statistical Process Control (SPC) is usually applied to  the production process of goods, with the aim of detecting the quality of a production item that is within or beyond the specified specifications. In this study, SPC was applied to the bearing vibration signal to detect the first observable defect on a machine that functions as part of a prognostic tool for maintenance decision making. The detection of damage and prognostic are two important aspects in machine maintenance based on current conditions or better known as Condition (data) Based Maintenance (CBM). This paper discusses the shewhart average level chart and adaptive shewhart average level chart to detect the first observable defect. The  shewhart chart is built with two assumptions, i.e. that the data must vary randomly around an established mean and follows a normal distribution. However, the adaptive Shewhart  chart there is no need for normal assumption. The exploration of our data shows that the assumption of normality is not fulfilled, so that the Shewhart average level chart is not implemented. The adaptive Shewhart  chart shows that the warning line for bearing 1 amounted to 5.547 and 3.631, for bearing 2 amounted to 5.491 and 3.635, for bearing 3 amounted to 5.762 and 3, 573, for bearing 4 of 5.604 and 33.615. The action line for bearing 1 is 6.026 and 3.152, for bearing 2 is 5.955 and 3.171, for bearing 3 is 6.309 and 3.026, for bearing 4 is 6.101 and 3.118. The first observable defect was t = 81 for bearing 1,  t = 146 for bearing 2,  t = 40 for bearing 3 and  t = 61 for bearing 4.  The adaptive Shewart chart can be used as a toll to estimate the initiation of transition state from normal to degenerate.
Pemodelan Overdispersi dalam Analisis Data BinerMelalui Model Regresi William Nusar Hajarisman
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 5, No 1 (2005)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v5i1.914

Abstract

Dalam pemodelan data biner seringkali dijumpai suatu kasus yang disebut denganoverdispersi. Munculnya masalah overdispersi dalam pengamatan data biner dapat dijelaskan olehdua hal, yaitu: adanya keragaman dalam peluang respon dan adanya korelasi antar peubah respon.Konsekuensi dari adanya overdispersi ini adalah dapat menimbulkan kekeliruan dalam membuatsuatu kesimpulan mengenai hubungan antara respon dengan sejumlah peubah penjelasnya. Dalammakalah ini akan diusulkan suatu model yang menangani masalah overdispersi seperti yangdiungkapkan oleh William (1982).
Co-Authors 10060116101, Tanti Rudiawan Putri Aceng Komarudin Mutaqin Achmad Widodo Achmad Widodo Achmad Widodo Anggi Savitri Ani Ressa Nuryaningsih Anneke Iswani A. Anneke Iswani Ahmad Annisa Lisa Nurjanah Arya Ramadhan, Iqbal ASEP SAEFUDDIN Aulia, Githa Budiman, Aulia Sabila Dewi Rosiana Diahsty Marasabessy, Munira Erfina Agustin Hidayat Esa Nurtiara Fadia Wahyu Utami Fatiha nurfauzan, Arsyi Fera Shintarini Hardiana Prasasti Jasmine Wildani Arisa Kamilatul 'Aini Khairi A N Kusman Sadik Mardha Tri Meilani Muh. Fahmi Nur Akbar Muhammad Bahry Baidhowy Muhammad Bangkit Riksa Utama Neng Marlina Nopita Sari Murtafiah Rejeki Wulan Islamiyati Riyani Desriawati Siti Magfiratul Hasanah Suliadi Suliadi Suliadi Sutawanir Darwis Sutawanir Darwis Sutawanir Darwis Teti Sofia Yanti Winda Aya Haliza Wulan Putri Prihartini Yayat Karyana