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All Journal Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Riemann : Research of Mathematics and Mathematics Education
Ulfasari Rafflesia
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Agriculture, Biological Sciences & Forestry Humanities Education Mathematics Social Sciences Other

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PENERAPAN UJI T-BERPASANGAN UNTUK MELIHAT PENGARUH PEMBINAAN SISWA DALAM MENGHADAPI PERSIAPAN LOMBA OLIMPIADE MATEMATIKA Ulfasari Rafflesia; Pepi Novianti; Dian Agustina; Idhia Sriliana
DHARMA RAFLESIA Vol 12, No 1 (2014): JUNI
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/dr.v12i1.3418

Abstract

ABSTRACTBengkulu is an active participant in the National Science Olympic competition in junior level. But the achievement of Bengkulu’s students is not prominent, especially in the fieldof Mathematics. Until now Bengkulu has never been quite encouraging achievement in theOSN. Department of Mathematics University of Bengkulu has a human resource that ispromising, the competent faculty who can nurture, guide, and prepare students of SMPNegeri 2 Bengkulu to compete in the OSN. Therefore, students of SMP 2 Bengkulu will becoached to understand, analyze and answer the questions, so that they know the mathOlympic the strategy and the ways that fast in answering the Olympics questions correctly,so they will be a champion at the provincial and national competition. After coaching isdone, it can be concluded that this activity has not been a significant influence. However,development activities that have been implemented are able to provide the knowledge andinsight to junior high school students about the material and questions in math Olympic.Keywords: SMP Negeri 2, Math Olympic, paired T-test
Improving Classification Performance of Imbalanced Data Using SMOTE: empirical studies Ulfasari Rafflesia; Dedi Rosadi
Riemann: Research of Mathematics and Mathematics Education Vol. 8 No. 1 (2026): EDISI APRIL
Publisher : Program Studi Pendidikan Matematika Universitas Katolik Santo Agustinus Hippo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38114/riemann.v8i1.199

Abstract

Data balancing methods in multi-class settings continue to evolve as the importance of balanced data conditions for classification analysis grows. However, limited studies have provided comprehensive empirical comparisons across both binary and multi-class imbalanced datasets. Data imbalance can affect model predictions, particularly by leading to inaccurate identification of minority classes. Therefore, this study aims to evaluate the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in improving classification performance. Three benchmark datasets from the UCI Machine Learning Repository—Breast Cancer, Ecoli, and Glass—were selected to represent imbalanced classification problems in both binary and multi-class settings. The proposed framework addresses class imbalance during data preprocessing using SMOTE. Each dataset is first divided into training and testing subsets. SMOTE is applied only to the training data to address class imbalance, while the test data is kept unchanged for evaluation. Then, the classification process is applied to the original (imbalanced) data and to the balanced data generated by SMOTE. The classifiers used in this study are SVM, a decision tree, and AdaBoost. The classification results are evaluated based on accuracy, sensitivity, and F1-score. The results show that the decision tree and AdaBoost improve classification performance under imbalanced data conditions. In particular, AdaBoost achieves the best overall performance in terms of prediction accuracy and class balance, demonstrating the effectiveness of combining SMOTE with ensemble methods for handling imbalanced datasets.
Co-Authors Dedi Rosadi Dian Agustina Idhia Sriliana Nur Afandi Pepi Novianti Zulfia Memi Mayasari