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All Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) Ramatekno
Erlangga, Anak Agung Gde Wahyu Sukma
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Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Kombinasi Oversampling dan Undersampling dalam Menangani Class Imbalanced dan Overlapping pada Klasifikasi Data Bank Marketing Erlangga, Anak Agung Gde Wahyu Sukma; Gunadi, I Gede Aris; Sunarya, I Made Gede
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 7 No. 1 (2024): Jurnal RESISTOR Edisi April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v7i1.1515

Abstract

Class imbalance can occur in various types of datasets, one of which is bank marketing datasets. The class imbalance can cause classification problems. To handle the problem, the SMOTE method can be used. However, the application of SMOTE can cause class overlapping and interfere with classification performance. Therefore, this research will try to handle it by combining the SMOTE method with undersampling methods consisting of ENN, NCL, and TomekLink. The classification algorithm used is Logistic Regression and the performance evaluation uses sensitivity, specificity, and g-means of the model. The results show that the SMOTE-ENN combination produces the most optimal results with sensitivity, specificity, and g-means of 94.05%, 83.22%, and 88.47% respectively on bank marketing datasets, while on credit card fraud datasets it has almost uniform results with sensitivity, specificity, and g-means ranging from 88.62%, 97.59%, and 93.00%. Finally, on cerebral stroke datasets, SMOTE-ENN produces the highest sensitivity at 80.1%, the highest specificity on SMOTE-NCL at 75.62%, and the highest g-means on SMOTE at 77.03%.
PERBANDINGAN PERFORMA SMOTE DAN SMOTE-ENN PADA DATA TIDAK SEIMBANG Erlangga, Anak Agung Gde Wahyu Sukma; Juliantara, Pande Putu Ode; Adnan
JURNAL RAMATEKNO Vol 5 No 2 (2025): Jurnal_Ramatekno_vol_5_no_2_2025
Publisher : LPPM Politeknik Enjinering Indorama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61713/jrt.v5i2.254

Abstract

Permasalahan ketidakseimbangan kelas pada data klasifikasi biner dapat menyebabkan model bias terhadap kelas mayoritas dan menurunkan performa dalam memprediksi kelas minoritas. Penelitian ini bertujuan untuk mengevaluasi pengaruh metode penyeimbangan data Synthetic Minority Over-sampling Technique (SMOTE) dan kombinasi SMOTE dengan Edited Nearest Neighbor (SMOTE-ENN) terhadap performa model klasifikasi. Dataset yang digunakan adalah Bank Marketing dengan variabel target berupa keputusan nasabah dalam menerima tawaran produk deposito. Metode yang digunakan meliputi pengumpulan data, preprocessing data, splitting data, resampling data, modeling dan evaluasi performa. Tiga algoritma machine learning yang digunakan dalam penelitian ini, yaitu Logistic Regression, Naïve Bayes, dan Random Forest. Evaluasi model dilakukan menggunakan metrik accuracy, sensitivity, specificity, dan G-Means. Hasil penelitian menunjukkan bahwa penerapan SMOTE meningkatkan sensitivity model terhadap kelas minoritas, namun cenderung menurunkan specificity. SMOTE-ENN memberikan hasil yang lebih seimbang antara sensitivitas dan spesifisitas karena mampu mengurangi data outlier dan overlapping. Model Random Forest dengan data hasil SMOTE-ENN menghasilkan performa paling seimbang secara keseluruhan berdasarkan keempat metric evaluasi. Penelitian ini menyimpulkan bahwa pemilihan metode penyeimbangan data yang tepat berpengaruh signifikan terhadap kualitas klasifikasi pada data tidak seimbang.
Co-Authors Adnan I Gede Aris Gunadi I Made Gede Sunarya Juliantara, Pande Putu Ode