Browsing by Author "Savira, Husna"

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    PEMODELAN REGRESI LOGISTIK BINER DENGAN PENDEKATAN BAYESIAN MARKOV CHAIN MONTE CARLO : KASUS INDEKS KEDALAMAN KEMISKINAN DI SUMATERA TAHUN 2021
    (Elfitra, 2023-05) Savira, Husna; Adnan, Arisman
    The 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%.