Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Prosiding Seminar Nasional Official Statistics Scientific Journal of Informatics
Pangestika, Dhita Elsha
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Classification Performance of Stacking Ensemble with Meta-Model of Categorical Principal Component Logistic Regression on Food Insecurity Data Pangestika, Dhita Elsha; Fitrianto, Anwar; Sadik, Kusman
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.15315

Abstract

Purpose: Stacking is one type of ensemble whose base-models use different algorithms. The classification results from its base-models are categorical and tend to be associated with each other. They then become input for the stacking meta-model. However, there are no currently definite rules for determining the classifier that becomes the meta-model in stacking. On the other hand, recent research has found that CATPCA-LR can work well on categorical predictor variables associated with each other. Therefore, this study focuses on the classification performance of the stacking algorithm with the CATPCA-LR meta-model. Methods: The study compared the classification performance stacking with CATPCA-LR meta-model to stacking with other meta-models (random forest, gradient boost, and logistic regression) and its base-models (random forest, gradient boost, extreme gradient boost, extra trees, light gradient boost). This research used food insecurity data from March 2022. Result: The stacking algorithm with the CATPCA-LR meta-model performs better insecurity data regarding sensitivity, balanced accuracy, F1-Score, and G-Means values. This model offers a sensitivity of 46.28%, a balanced accuracy of 59.82%, an F1-Score of 37.82%, and a G-Means of 58.26%. Meanwhile, regarding specificity values, the light gradient boost (LGB) algorithm gives the highest value compared to other algorithms. This model provides a specificity value of 88.40%. Generally, the stacking with the CATPCA-LR meta-model algorithm provides the best performance compared with other algorithms on food insecurity data. Novelty: This research has explored a stacking classification performance with CATPCA-LR as meta-model.
Perbandingan Performa Metode Klasifikasi Regresi Logistik, Classification Tree, dan Random Forest (Studi kasus: Perkawinan Anak pada Perempuan Usia Muda di Nusa Tenggara Barat Tahun 2022) Pangestika, Dhita Elsha; Mustika, Diva Arum; Rahman, Ayub Abdul
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2440

Abstract

Child marriage remains a persistent issue in Indonesia, particularly in West Nusa Tenggara Province. This study compares the performance of three classification methods—logistic regression, classification tree, and random forest—in predicting child marriage among young women. The analysis uses 2022 National Socio-Economic Survey (Susenas) data, which comprises 69 women aged 20–24 who had married and were still living with their parents. Model performance was evaluated using the Area Under the Curve (AUC) metric with 50 validation repetitions. Logistic regression yielded the highest AUC (77.86%), followed by random forest (76.07%) and classification tree (75.49%). These results indicate that logistic regression is more stable and suitable for linear, low-dimensional, and limited observational data. Additionally, education level and the household head’s type of employment were identified as key predictors of child marriage.
Co-Authors Fitrianto, Anwar Kusman Sadik Mustika, Diva Arum Rahman, Ayub Abdul