PEMODELAN REGRESI LOGISTIK BINER BIVARIAT BAYESIAN UNTUK RESPON YANG UNBALANCE Studi Kasus: Konsumen Produk Low Price Software Antivirus Perusahaan ‘X’

SUSILA, MUKTAR REDY (2016) PEMODELAN REGRESI LOGISTIK BINER BIVARIAT BAYESIAN UNTUK RESPON YANG UNBALANCE Studi Kasus: Konsumen Produk Low Price Software Antivirus Perusahaan ‘X’. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Tujuan dari penelitian ini adalah memodelkan regresi logistik biner Bayesian dan regresi logistik biner bivariat Bayesian pada kasus konsumen Perusahaan ‘X’. Perusahaan ‘X’ merupakan suatu perusahaan yang beroperasi dengan sambungan internet (Cloud Based Software). Perusahaan tersebut tengah menyelesaikan masalah perilaku konsumen dalam perihal pembelotan produk dan jawaban perpanjangan kontrak. Model regresi logistik biner yang memiliki dua variabel dependen yang saling berkaitan dapat dimodelkan menjadi satu model yang disebut regresi logistik biner bivariat. Keuntungan dari model regresi logistik biner bivariat yaitu odds ratio yang diperoleh menggambarkan hubungan berpasangan antara dua variabel respon biner. Metode Bayesian merupakan metode yang menggunakan informasi-informasi sebelumnya dalam bentuk distibusi probabilitas. Metode tersebut sering digunakan untuk pemodelan pada saat sampel dengan variabel dependen yang unbalance. Penelitian ini menghasilkan sampel parameter yang telah diiterasi dan belum memenuhi sifat strongly ergodic untuk data sebanyak 500000. Sehingga dilakukan sampling dari data keseluruhan. Didapatkan lima model regresi logistik biner Bayesian dan model regresi logistik biner bivariat Bayesian. Model tersebut merupakan model dari sampel. Secara univariat, variabel yang konsiten mempengaruhi Pembelotan dan Jawaban Kontrak adalah Akumulasi Update dan Status Pengiriman e-mail. Secara bivariat, variabel yang memberikan pengaruh terhadap kedua variabel respon adalah Status Pengiriman. Hasil klasifikasi yang diperoleh menunjukan bahwa regresi logistik biner univariat maupun bivariat Bayesian belum mampu menangkap efek dari variabel Jawaban Kontrak yang unbalance. ===================================================================================================== The aim of this study is to resolve the case in 'X' company by using Bayesian binary logistic regression and Bayesian bivariate binary logistic regression models. The 'X' Company is a company that operates with internet connection (Cloud-Based Software). The company is solving customer loyalty issues in term of customer defection and contract renewal. Binary logistic regression model that has two interrelated dependent variables can be modeled into one model called bivariate binary logistic regression. The advantage of the bivariate binary logistic regression model is that the odds ratio can describes the relationship between the two pairs of binary response variables. One of the important stages in the modeling is parameter estimation. Common method for parameter estimation in a logistic regression model is maximum likelihood. Bayesian method is a method that uses prior information in the form of probability distribution. The method is often used for modeling when the sample has unbalance dependent variable. This research yields parameter estimates that have not strongly ergodic after iteration with 500.000 observations. Then five samples are drawn to form five Bayesian binary logistic regression and Bayesian bivariate binary logistic regression models. Those models are formed based on each samples. In term of univariate, variables that consistently affect the defection and contract response are update accumulation and e-mail delivery status. In term of bivariate, variables that affect both response variables is the delivery status. The classification results showed that both Bayesian bivariate and univariate binary logistic regression have not been able to capture the effects of unbalance contract response.

Item Type: Thesis (Masters)
Additional Information: RTSt 519.536 Sus p
Uncontrolled Keywords: Regresi Logistik Biner, Regresi Logistik Biner Bivariat, Bayesian, Unbalance.
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA278.2 Regression Analysis
Divisions: Faculty of Mathematics and Science > Statistics > (S2) Master Theses
Depositing User: Mr. Tondo Indra Nyata
Date Deposited: 10 Jan 2017 06:45
Last Modified: 27 Dec 2018 07:24
URI: http://repository.its.ac.id/id/eprint/1440

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