cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nusar Hajarisman
Contact Email
statistika@unisba.ac.id
Phone
+628124569091
Journal Mail Official
statistika@unisba.ac.id
Editorial Address
Program Studi Statistika Universitas Islam Bandung Jl. Ranggagading No. 8 Bandung 40116 Phone: 081572198581 Email: nusarhajarisman@unisba.ac.id
Location
Kota bandung,
Jawa barat
INDONESIA
STATISTIKA
Published by Universitas Islam Bandung
ISSN : 14115891     EISSN : 25992538     DOI : https://doi.org/10.29313/statistika.v23i1.1628
Core Subject : Science, Education,
Decision Sciences, Operations Research & Management Mathematics
STATISTIKA published by Department of Statistics, Faculty of Mathematics and Natural Sciences, Bandung Islamic University as pouring media and discussion of scientific papers in the field of statistical science and its applications, both in the form of research results, discussion of theory, methodology, computing, and review books. Published biannually in May and November each.
Arjuna Subject : -
Articles 91 Documents
 6   7   8   9   10 
LASSO-Regularized Binary Logistic Regression on Imbalanced Mode Choice Data Hendri, Eko Primadi; Urbaningrum, Novi; Fadhlia, Sarah
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.8126

Abstract

Abstract. Binary logistic regression is a widely used method for modeling mode choice, but it often suffers from reduced predictive accuracy when dealing with high-dimensional datasets and class imbalance. This study implements binary logistic regression with LASSO regularization to identify significant factors influencing transportation mode choice between motorcycles and Trans Metro buses in the CBD of Pekanbaru. Data from 100 respondents were collected through revealed-preference and stated-preference surveys, with class imbalance (71% motorcycle, 29% bus) addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using accuracy, AUC, precision, recall, and F1-score via Repeated Random Subsampling Validation (RRSV). Results show that the LASSO model with SMOTE increased recall from 0.125 to 0.25 and F1-score from 0.143 to 0.267 compared to the non-SMOTE model, with an accuracy of 0.621 and an AUC of 0.613, indicating improved ability to detect the minority class. Statistically significant predictors include occupation, monthly income, and ownership of an alternative vehicle. This study demonstrates that combining LASSO and SMOTE is effective in handling imbalanced data, providing strong quantitative evidence to support urban transport policy planning.

Page 10 of 10 | Total Record : 91

 6   7   8   9   10 

Filter by Year

2021 2025


Reset
Filter By Issues
All Issue Vol. 25 No. 2 (2025): Statistika Vol. 25 No. 1 (2025): Statistika Vol. 24 No. 2 (2024): Statistika Vol. 24 No. 1 (2024): Statistika Vol. 23 No. 2 (2023): Statistika Vol. 23 No. 1 (2023): Statistika Vol. 22 No. 2 (2022): Statistika Vol. 22 No. 1 (2022): Statistika Vol. 21 No. 2 (2021): Statistika