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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Erianto Ongko
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Hybrid Approach-RSMOTE for Handling Class Imbalance with Label Noise Hartono Hartono; Erianto Ongko
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.23684

Abstract

The class imbalance problem is the main problem in classification. This issue arises because real-world datasets frequently exhibit an imbalance as a result of a class with more instances than other classes. In handling class imbalance, a Hybrid Approach that blends data-level and algorithm-level approaches produce good results. However, apart from the class imbalance, which reduces classification accuracy, the complexity of the data also has an effect. The complexity of this data causes a minority noise sample which lies between the minority and the majority. In order to determine how close minority samples are to their homogeneous and heterogeneous nearest neighbors, it is necessary to calculate the relative density. The greater the proximity to the homogeneous nearest neighbors, the greater the relative density, which causes the minority samples to be in a safe state but otherwise be categorized as noisy samples. This research will combine the application of the Hybrid Approach with A self-adaptive Robust SMOTE (RSMOTE), which is an adaptive method from SMOTE that applies the concept of relative density in the over-sampling process on minority samples. The research contribution is to implement the Hybrid Approach-RSMOTE in handling class imbalance with noise by using relative density in over-sampling and also to improve classification performance. The results showed that the Hybrid Approach-RSMOTE and Hybrid Approach-SMOTE had given good results in handling class imbalance. However, the Hybrid Approach-RSMOTE gave better results in the Precision, Recall, F1-Measure, and G-Mean and showed significant differences. Based on the results of the study, it can be stated that the performance of the Hybrid Approach in handling class imbalance is influenced by the selection of the over-sampling method. The results show that RSMOTE can be considered an over-sampling method in the Hybrid Approach.
Impact of Adaptive Synthetic on Naïve Bayes Accuracy in Imbalanced Anemia Detection Datasets Zuhanda, Muhammad Khahfi; Lisya Permata; Hartono; Erianto Ongko; Desniarti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6031

Abstract

This research aims to analyze the impact of the Adaptive Synthetic (ADASYN) oversampling technique on the performance of the Naïve Bayes classification algorithm on datasets with class imbalance. Class imbalance is a common problem in machine learning that can cause bias in prediction results, especially in minority classes. ADASYN is one of the oversampling methods that focuses on adaptively synthesizing new data for minority classes. In this study, the performance of the Naïve Bayes algorithm was tested on Anemia Diagnosis datasets before and after the application of ADASYN. This dataset contains 104 instances, 5 attributes, and 2 classes, and has an imbalance ratio of 3. The evaluation was carried out by comparing accuracy, confusion matrix, precision, recall, and F1-score to obtain a more comprehensive picture of the effectiveness of ADASYN in improving Naïve Bayes. The results of the study show that the performance of the oversampling method depends on the imbalance ratio so it is important to ensure that the oversampling method does not cause overfitting and this can be overcome by using ADASYN which only selects Selected Neighbors. The results showed that ADASYN significantly increased accuracy from 0.57 to 0.78, precision from 0.17 to 0.74, recall from 0.20 to 0.88, and F1-Score from 0.18 to 0.80. In this study, we also compared the application of ADASYN and SMOTE on the Naïve Bayes algorithm. The results show that ADASYN outperforms SMOTE across all key metrics—accuracy, precision, recall, and F1-Score—while the accuracy improvements were statistically significant (p-value = 0.00903).
Co-Authors Desniarti Hartono Hartono Lisya Permata Zuhanda, Muhammad Khahfi