eprintid: 76851
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/07/68/51
datestamp: 2026-06-22 02:09:07
lastmod: 2026-06-22 02:09:07
status_changed: 2026-06-22 02:09:07
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Mutiara Nur Amalina, NIM.: 22106010089
title: OPTIMISASI ANALISIS REGRESI LOGISTIK LASSO MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : FAKTOR PENYEBAB PERCERAIAN DI INDONESIA TAHUN 2024)
ispublished: pub
subjects: 515.6
divisions: jur_mat
full_text_status: restricted
keywords: Multikolinearitas, Regresi Logistik, LASSO, BIC, Perceraian
note: Sri Utami Zuliana, S.Si., M.Sc., Ph.D.
abstract: Divorce is a social issue influenced by various contributing factors and shows
different patterns across provinces. This study aims to classify divorce levels in Indonesia
in 2024 using LASSO logistic regression with optimal parameter selection
based on the Bayesian Information Criterion (BIC). The data used in this study are
secondary data from the Statistics Indonesia (BPS), covering 34 provinces in Indonesia.
The response variable was categorized into two groups, namely low and
high divorce levels, based on the divorce rate per 1,000 population. Meanwhile, the
predictor variables consisted of 13 factors causing divorce. The analysis began with
the development of an initial logistic regression model, followed by multicollinearity
testing, variable standardization, the application of the LASSO method, and the
selection of the optimal λ value using BIC. The results showed that the initial logistic
regression model experienced multicollinearity, as all variables had VIF values
greater than 10. Through the LASSO-BIC method, the optimal value obtained was
λBIC = 0.003914, with a minimum BIC value of 26.77. The final model retained
six variables, namely gambling, abandonment by one spouse, imprisonment of one
spouse, polygamy, physical disability/illness, and continuous disputes or conflicts.
Compared with the initial model, LASSO-BIC produced a simpler model, reducing
the number of parameters from 14 to 7 and decreasing the BIC value from 66.055
to 26.770.
date: 2026-05-20
date_type: published
pages: 117
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Mutiara Nur Amalina, NIM.: 22106010089  (2026) OPTIMISASI ANALISIS REGRESI LOGISTIK LASSO MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : FAKTOR PENYEBAB PERCERAIAN DI INDONESIA TAHUN 2024).  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/76851/1/22106010089_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/76851/2/22106010089_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf