DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.89-93

K-means-SMOTE untuk menangani ketidakseimbangan kelas dalam klasifikasi penyakit diabetes dengan C4.5, SVM, dan naive Bayes

K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes

1Universitas Bumigora, Indonesia

2Sekolah Tinggi Manajemen Informatika dan Komputer Lombok, Indonesia

Received: 6 Nov 2019; Revised: 10 Feb 2020; Accepted: 14 Feb 2020; Available online: 15 Feb 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to cite (IEEE): H. Hairani, K. E. Saputro, and S. Fadli, "K-means-SMOTE untuk menangani ketidakseimbangan kelas dalam klasifikasi penyakit diabetes dengan C4.5, SVM, dan naive Bayes," Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 2, pp. 89-93, Apr. 2020. https://doi.org/10.14710/jtsiskom.8.2.2020.89-93
Citation Format:
Abstract
The occurrence of imbalanced class in a dataset causes the classification results to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (positive class) so that the class distribution becomes balanced and leading to better classification results. This study was conducted to overcome imbalanced class problems on the Indian Pima diabetes illness dataset using k-means-SMOTE. The dataset has 268 instances of the positive class (minority class) and 500 instances of the negative class (majority class). The classification was done by comparing C4.5, SVM, and naïve Bayes while implementing k-means-SMOTE in data sampling. Using k-means-SMOTE, the SVM classification method has the highest accuracy and sensitivity of 82 % and 77 % respectively, while the naive Bayes method produces the highest specificity of 89 %.

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Pima Indian Diabetes Dataset
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Keywords: k-means-SMOTE; SMOTE; classification performance; class imbalance
Funding: Universitas Bumigora

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Section: Original Research Articles
Language : ID
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