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

*Hairani Hairani orcid scopus  -  Universitas Bumigora, Indonesia
Khurniawan Eko Saputro scopus  -  Universitas Bumigora, Indonesia
Sofiansyah Fadli  -  Sekolah Tinggi Manajemen Informatika dan Komputer Lombok, Indonesia
Received: 6 Nov 2019; Revised: 10 Feb 2020; Accepted: 14 Feb 2020; Published: 30 Apr 2020; Available online: 15 Feb 2020.
DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.89-93 View
Pima Indian Diabetes Dataset
Subject
Type Data Set
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Section: Original Research Articles
Language: ID
In Volume 8, Issue 2, Year 2020 (April 2020)
Statistics: 1536 221
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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Keywords: k-means-SMOTE; SMOTE; classification performance; class imbalance

Article Metrics:

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