Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Fisika dan Teknologi SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam
Johana Oktavia Ramadhani
Dian Nuswantoro University
Education Physics

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

Found 2 Documents
Search

 1 
Impact of SMOTE Oversampling on Classifying Band Gap Types in Imbalanced ABO₃ Perovskite Oxides Desvita Maharani; Johana Oktavia Ramadhani; Aliyah Zahratu Rizqi; Muhamad Akrom
Jurnal Pendidikan Fisika dan Teknologi (JPFT) Vol 12 No 1 (2026): January-June
Publisher : Department of Physics Education, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpft.v12i1.11479

Abstract

This study investigates the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on the classification of direct and indirect band gap types in imbalanced ABO₃ perovskite oxide datasets. In the dataset used, the direct band gap class constitutes approximately 84% of the samples, while the indirect class represents only 16%, leading conventional classification models to become biased toward the majority class. To address this issue, SMOTE was employed to balance the class distribution, and its performance was evaluated using several machine learning algorithms, including Multi-Layer Perceptron (MLP), Extra Trees, CatBoost, and Gradient Boosting. Model performance was assessed using 5-fold stratified cross-validation, with particular emphasis on F1-macro and recall metrics to ensure adequate evaluation of the minority class. The results show that although SMOTE did not significantly improve overall accuracy (baseline: 0.89; SMOTE: 0.88), it enhanced the models’ ability to recognize the minority class. Notable improvements in F1-macro were observed, increasing from 0.76 to 0.78 for MLP and from 0.75 to 0.78 for CatBoost. These findings highlight the importance of using F1-macro as a more informative evaluation metric than accuracy for imbalanced datasets and provide methodological insights for developing more robust predictive models in materials informatics.
Impact Analysis Of Data Imbalance Handling Using Edited Nearest Neighbors In The Classification Of Abo₃ Perovskite Oxide Bandgap Materials Aliyah Zahratu Rizqi; Muhamad Akrom; Desvita Maharani; Johana Oktavia Ramadhani
SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Vol. 15 No. 1 (2026): Volume 15 Nomor 1 (Maret 2026)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/xh3va614

Abstract

The classification of bandgap types in perovskite ABO₃ materials presents a significant challenge due to overlapping feature distributions and class imbalance between direct and indirect bandgap classes. This study investigates the impact of data cleaning using the Edited Nearest Neighbour (ENN) method on the performance of multiple machine learning classifiers, including Multilayer Perceptron (MLP), Gradient Boosting, CatBoost, and Extra Trees. Model evaluation focuses on accuracy and F1 macro to capture both overall performance and class wise balance. Under baseline conditions, the best performing model achieved a test accuracy of 0.8934 with an F1 macro of 0.7783, indicating strong predictive capability but limited sensitivity to the minority class. After applying ENN exclusively to the training data, the class distribution became more balanced, resulting in improved recall for the minority class across several models. However, this improvement was accompanied by a reduction in overall accuracy. The highest F1 macro under ENN was 0.7632, achieved by the Gradient Boosting model, demonstrating enhanced class balance despite a lower accuracy of 0.8646. Cross validation results confirm that ENN does not significantly increase performance stability but effectively refines decision boundaries by removing ambiguous majority class samples. These findings highlight the trade off between accuracy and class fairness and emphasize that model selection should be guided by the evaluation objective. For imbalanced bandgap classification tasks, F1 macro oriented evaluation combined with ensemble based models provides a more reliable and equitable predictive framework.
Co-Authors Aliyah Zahratu Rizqi Desvita Maharani Muhamad Akrom