PEMODELAN REGRESI LOGISTIK BINER DENGAN PENDEKATAN BAYESIAN MARKOV CHAIN MONTE CARLO : KASUS INDEKS KEDALAMAN KEMISKINAN DI SUMATERA TAHUN 2021

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dc.contributor.authorSavira, Husna
dc.contributor.supervisorAdnan, Arisman
dc.date.accessioned2023-07-25T04:20:08Z
dc.date.available2023-07-25T04:20:08Z
dc.date.issued2023-05
dc.description.abstractThe Poverty Gap Index (PGI) is the average expenditure gap of each poor population towards the poverty line. This study aims to model PGI data using binary logistic regression with a classical approach using the Maximum Likelihood Estimation (MLE) method and a Bayesian approach using the Markov Chain Monte Carlo (MCMC) method. MCMC is a popular method for obtaining information about the distribution, especially for estimating the posterior distribution in Bayesian inference with the Metropolis-Hasting algorithm. Factors that have a significant influence on the IKK in Sumatera using the Bayesian approach and the classical approach are the same, namely Life Expectancy and per capita expenditure. Based on the results of the classification with training data of 80% and test data of 20% a classification accuracy of 62,50%.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alam Universitas Riauen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.otherElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/11066
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectIKKen_US
dc.subjectBinary Logistic Regressionen_US
dc.subjectMLEen_US
dc.subjectBayesianen_US
dc.subjectMCMCen_US
dc.subjectMetropolis- Hasting Algorithmen_US
dc.subjectand Classificationen_US
dc.titlePEMODELAN REGRESI LOGISTIK BINER DENGAN PENDEKATAN BAYESIAN MARKOV CHAIN MONTE CARLO : KASUS INDEKS KEDALAMAN KEMISKINAN DI SUMATERA TAHUN 2021en_US
dc.typeArticleen_US

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