TY  - THES
N1  - Aulia Khifah Futhona, M.Sc. dan Sri Utami Zuliana, S.Si., M.Sc., Ph.D.
ID  - digilib78703
UR  - https://digilib.uin-suka.ac.id/id/eprint/78703/
A1  - Robit Ma?rufi Sabila, NIM.: 22106010081
Y1  - 2026/08/03/
N2  - Regression is one of the statistical methods used to analyze the relationship
between dependent and independent variables. For count data, the Poisson
regression model is commonly used. Poisson regression assumes equidispersion,
where the mean and variance of the dependent variable are equal. However, in
practice, Poisson regression often encounters overdispersion, where the variance
is greater than the mean, making Poisson regression less appropriate. One method
for addressing overdispersion is Negative Binomial regression, which has a
dispersion parameter that can accommodate overdispersion in the data. In addition
to overdispersion, multicollinearity, characterized by high correlations among
independent variables, can cause regression coefficients to become unstable. The
use of high-dimensional data can also increase the risk of overfitting. Therefore,
a variable selection method is needed to produce a simpler and more efficient
model. To address these issues, Lasso regularization is applied by imposing an L1
penalty on the regression coefficients, thereby producing more stable parameter
estimates while simultaneously performing variable selection. This study uses the
number of Dengue Hemorrhagic Fever (DHF) cases in Central Java in 2024 as the
dependent variable and 12 independent variables related to health, sanitation, and
socioeconomic factors. This study aims to apply Negative Binomial regression
with Lasso regularization using K-Fold Cross Validation. The results show that the
optimal ? value of 0.4247 selects one variable, namely the Number of Community
Health Centers (JSKP), resulting in a more parsimonious and efficient model.
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - Binomial Negatif
KW  -  Lasso
KW  -  K-Fold Cross Validation
KW  -  Demam Berdarah (DBD)
M1  - skripsi
TI  - OPTIMALISASI REGRESI BINOMIAL NEGATIF DENGAN REGULARISASI LASSO MENGGUNAKAN K-FOLD CROSS VALIDATION (STUDI KASUS : JUMLAH KASUS DBD DI JAWA TENGAH TAHUN 2024)
AV  - restricted
EP  - 123
ER  -