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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika)
Arini, Luthfia Hanun Yuli
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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ENSEMBLE BAGGING WITH ORDINAL LOGISTIC REGRESSION TO CLASSIFY TODDLER NUTRITIONAL STATUS Arini, Luthfia Hanun Yuli; Solimun, Solimun; Efendi, Achmad; Fernandes, Adji Achmad Rinaldo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp1-12

Abstract

One problem in classifying stunting data is that the data used does not have a balanced proportion. This study aims to apply the logistic regression classification method with ordinal scale response variables to overcome class imbalance through the ensemble bagging approach. The data used is secondary data in the form of final research reports that have been tested for validity and reliability. The predictor variables used are economic conditions, health services and the environment with categorical response variables, namely the nutritional status of toddlers in the categories of stunting, normal and high. The methods used are ordinal logistic regression and ensemble bagging on ordinal logistic regression with bootstraps of 100, 500, and 1000. The variables that influence the nutritional status of toddlers are Economic Conditions, Health Services, and the Environment. The results of the study showed that the accuracy, sensitivity, specificity, and F1-Score for ordinal logistic regression were smaller than ensemble bagging in ordinal logistic regression. The best classification method obtained was bagging logistic regression with a bootstrap number of 500 and obtained an accuracy value of 85%, sensitivity of 87.2%, specificity of 72.6%, and F1-Score of 79.3%.
CART Classification on Ordinal Scale Data with Unbalanced Proportions using Ensemble Bagging Approach Arini, Luthfia Hanun Yuli; Solimun, Solimun; Efendi, Achmad; Ullah, Mohammad Ohid
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20201

Abstract

CART is one of the algorithms in data exploration techniques with decision tree techniques. Unbalanced class proportions in the classification process can cause classification results of minor data to be incorrect. One way to overcome the problem of data imbalance is to use an ensemble bagging algorithm. The bagging algorithm utilizes the resampling method to carry out classification so that it can reduce bias in imbalanced data. The data used is secondary data from Fernandes and Solimun's 2023 research report. The number of sample are 100 respondents that has been valid and reliable. The sample for this research was mothers with toddlers in Wajak village, Malang Regency. The results showed that the ensemble bagging CART method is better at overcoming the problem of imbalance in the proportion of classes with a performance value of accuracy, sensitivity, specificity, and F1-Score values of 85%, 94.1%, 66.7%, and 78%. This research is limited to the Sumberputih Village area. So, the results of this research are only representative for the Wajak District area. 
ORDINAL XGBOOST FOR MULTICLASS NUTRITIONAL STATUS CLASSIFICATION WITH IMBALANCED DATA Arini, Luthfia Hanun Yuli; Siniwi, Lutfiah Maharani
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.45-60

Abstract

Stunting is a major global health problem that affects children’s physical growth and cognitive development, particularly in developing countries. The classification of toddlers’ nutritional status to detect stunting risk often faces two primary challenges: the ordinal nature of the labels and the imbalance in class distribution, where minority classes such as stunted and tall are much smaller than the majority class (normal). This study aims to develop an Ordinal Extreme Gradient Boosting (Ordinal XGBoost) method using a Binary Decomposition approach to classify toddlers’ nutritional status in imbalanced ordinal data. Secondary data from 100 respondents were analyzed, with 80% allocatedfor training and 20% for testing. The Binary Decomposition approach transforms the three-class ordinal classification problem into two binary subproblems, each trained using XGBoost with weighted logistic loss to address class imbalance. Model performance was evaluated using four key metrics: accuracy, ordinal Mean Absolute Error (MAE), Quadratic Weighted Kappa (QWK), and Macro-F1. Results showed an accuracy of 70%, ordinal MAE of 0.30, QWK of 0.45, and Macro-F1 of 0.53. The MAE and QWK values indicate the model’s ability to preserve class order while reducing large prediction jumps, although performance on minority classes remains limited. These findings suggest that the proposed approach is effective for imbalanced ordinal data and has potential applications in toddler nutritional status monitoring systems.
Co-Authors Achmad Efendi Fernandes, Adji Achmad Rinaldo Siniwi, Lutfiah Maharani Solimun, Solimun Ullah, Mohammad Ohid