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ITS » Master Theses » Statistika - S2
Posted by dewi007 at 21/08/2009 11:48:46  •  4344 Views


PERBANDINGAN MISKLASIFIKASI ANTARA METODE MAXIMUM LIKELIHOOD ESTIMATION (MLE) DENGAN BAYESIAN PADA REGRESI LOGISTIK MULTINOMIAL (STUDI KASUS : PENJURUSAN SMAN I GRATI PASURUAN)

COMPARISON OF MISSCLASSIFICATION BETWEEN MAXIMUM LIKELIHOOD ESTIMATION (MLE) AND BAYESIAN METHODS IN MULTINOMIAL LOGISTIC REGRESSION (Case Study : SMAN I Grati Pasuruan)

Created by :
Arieska, Permadina Kanah ( 1307201710 )



SubjectAnalisis regresi
Alt. Subject Regression analysis
KeywordRegresi logistik Multinomial
Bayesian
Misklasifikasi
Jurusan

Description:

Regresi logistik multinomial klasik menggunakan metode Maximum Likelihood untuk mengestimasi parameter - parameternya. Sedangkan pada regresi logistik multinomial dengan menggunakan metode Bayesian, menggunakan distribusi prior untuk mendapatkan distribusi posterior. Distribusi posterior tersebut akan digunakan untuk mengestimasi parameter – parameternya. Penerapan dua metode ini dilakukan untuk mengklasifikasikan siswa SMAN 1 Grati Pasuruan pada 3 jurusan yang telah ditetapkan oleh pihak sekolah yaitu jurusan IPA, IPS dan Bahasa. Variabel prediktor yang digunakan ada 5 yaitu tuntas IPA, tuntas IPS, tuntas Bahasa, IQ dan minat. Dari kelima variabel prediktor tersebut ternyata yang berpengaruh secara signifikan pada penjurusan siswa adalah variabel tuntas bahasa dan IQ. Misklasifikasi pada regresi logistik multinomial klasik lebih besar dibandingkan pada regresi logistik multinomial metode Bayesian. Misklasifikasi dengan metode pertama adalah sebesar 46,1% sedangkan dengan menggunakan metode kedua yaitu metode Bayesian, misklasifikasinya dalah sebesar 39,5%. Sehingga dalam penelitian ini, regresi logistik multinomial dengan metode Bayesian lebih baik dalam pemodelan jurusan di SMAN 1 Grati Pasuruan jika dibandingkan dengan regresi logistik Multinomial klasik.


Alt. Description

Multinomial logistic regression uses Maximum Likelihood Estimation (MLE) to estimate it’s parameter. Whereas, Bayesian Multinomial Logistic Regression uses Bayesian methods. In Bayesian Methods, prior distribution was needed to build posterior distribution. These two methods will be compared each other based on it’s misclassification. This Research will employ these two methods to classify student of SMAN 1 Grati Pasuruan. There are three pograms : IPA, IPS and Bahasa. At the end of the first year each student have to be placed in one of these three program. Some factors as predictor to classify are tuntas IPA, tuntas IPS, tuntas Bahasa, IQ, and minat. From the analysis, two predictor, tuntas bahasa and IQ, are significant. Misclasification of Multinomial logistic regression using MLE is 46,1% whereas using Bayesian Methods, 39,5%. So, Bayesian Multinomial Logistic Regression is better than Multinomial logistic regression using MLE in the case of determining program study for student in SMAN 1 Grati Pasuruan.

Contributor:
  1. Prof. Drs. Nur Iriawan, MIKom, PhD
    Dra. Kartika Fitriasari, M.Si
Date Create:21/08/2009
Type:Text
Format:pdf.
Language:Indonesian
Identifier:ITS-Master-3100009035145
Collection ID:3100009035145
Call Number:RTSt 519.536 Ari p


Source :
Master Theses of Statistics, RTSt 519.536 Ari p, 2009

Coverage :
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Rights :
Copyright @2009 by ITS Library. This publication is protected by copyright and permission should be obtained from the ITS Library prior to any prohibited reproduction, storage in a retrievel system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permission(s), write to ITS Library


Publication URL :
http://digilib.its.ac.id/perbandingan-misklasifikasi-antara-metodemaximum-likelihood-estimation-mle-dengan-bayesian-pada-regresi-logistik-multinomialstudi-kasus--penjurusan-sman-i-grati-pasuruan-5232.html




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