eprintid: 67408
rev_number: 10
eprint_status: archive
userid: 12460
dir: disk0/00/06/74/08
datestamp: 2024-10-02 01:54:29
lastmod: 2024-10-02 01:54:29
status_changed: 2024-10-02 01:54:29
type: thesis
metadata_visibility: show
contact_email: muh.khabib@uin-suka.ac.id
creators_name: Adelia Disty Nariswari, NIM.: 20106010037
title: OPTIMISASI ANALISIS REGRESI LASSO DENGAN ALGORITMA LARS MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : TINGKAT PENGANGGURAN TERBUKA DI PROVINSI JAWA TIMUR TAHUN 2022)
ispublished: pub
subjects: 560
divisions: jur_mat
full_text_status: restricted
keywords: BIC, Multikolinearitas, LASSO, LARS, Tingkat Pengangguran
note: Pembimbing: Sri Utami Zuliana, S.Si., M.Sc., Ph.D. dan 
Muchammad Abrori, S.Si., M.Kom
abstract: Linear regression is one of the modeling methods often used in data analysis.
However, this model must fulfill several basic assumptions, one of which is the absence
of multicollinearity. Multicollinearity occurs when the independent variables
have a strong correlation, which can lead to instability and inaccuracy in the estimation
of regression coefficients. Multicollinearity identification uses the Variance
Inflation Factor (VIF) value or an examination of the correlation coefficient between
variables in a regression model. One method that can overcome multicollinearity
problems is Least Absolute Shrinkage and Selection Operator (LASSO) regression,
which is able to shrink the regression coefficients of highly correlated independent
variables to near zero or exactly zero. The Least Angle Regression and Shrinkage
(LARS) algorithm is used to determine the regression coefficients, while the selection
of the optimal lambda value in LASSO regression is done using the Bayesian
Information Criterion (BIC), where the best model is determined based on the lowest
BIC value. This research is applied to data on the open unemployment rate in
East Java in 2022. The results showed that the LASSO regression would produce
81 lambdas and the optimal lambda was chosen at 0.0499297734 with a BIC value
of 0.8010647. The conclusion obtained based on the research that has been done is
that three of the five independent variables are included in the model, namely the
labor force participation rate, the human development index, and the percentage of
poor people.
date: 2024-08-20
date_type: published
pages: 83
institution: UIN SUNAN KALIJAGA YOGYAKARTA
department: FAKULTAS SAINS DAN TEKNOLOGI
thesis_type: skripsi
thesis_name: other
citation:   Adelia Disty Nariswari, NIM.: 20106010037  (2024) OPTIMISASI ANALISIS REGRESI LASSO DENGAN ALGORITMA LARS MENGGUNAKAN BAYESIAN INFORMATION CRITERION (BIC) (STUDI KASUS : TINGKAT PENGANGGURAN TERBUKA DI PROVINSI JAWA TIMUR TAHUN 2022).  Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.   
document_url: https://digilib.uin-suka.ac.id/id/eprint/67408/1/20106010037_BAB-I_IV-atau-V_DAFTAR-PUSTAKA.pdf
document_url: https://digilib.uin-suka.ac.id/id/eprint/67408/2/20106010037_BAB-II_sampai_SEBELUM-BAB-TERAKHIR.pdf