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Ani Rimadani
Jember University
Mathematics

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Klasifikasi penyakit Demam Berdarah Dengue (DBD) menggunakan algoritma C5.0 berbasis Binary Particle Swarm Optimization (BPSO) Ani Rimadani; Agustina Pradjaningsih; I Made Tirta
Jurnal Ilmiah Matematika Vol 9, No 2 (2022)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/konvergensi.v9i2.26089

Abstract

Dengue Hemorrhagic Fever, or DHF, is an infectious disease caused by the dengue virus. Diseases that are dangerous and at risk of death must be treated quickly and precisely. The determination that someone is suffering from DHF or not can be done with the classification technology, namely the decision tree method, the C5.0 algorithm. Over time, the number of features in the classification increases. Feature reduction is needed for a good model, namely the Binary Particle Swarm Optimization (BPSO) algorithm. This research used 13 features consisting of 12 independent features and one bound feature with two classes: Positive and Negative. The better model in this study is the classification C5.0 algorithm based on BPSO, which can reduce features from 12 to 9 features with an accuracy of 86% compared to classification with the C5.0 algorithm alone, which produces an accuracy of 71%.
Klasifikasi penyakit Demam Berdarah Dengue (DBD) menggunakan algoritma C5.0 berbasis Binary Particle Swarm Optimization (BPSO) Ani Rimadani; Agustina Pradjaningsih; I Made Tirta
Jurnal Ilmiah Matematika Vol. 9 No. 2 (2022)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/konvergensi.v9i2.26089

Abstract

Dengue Hemorrhagic Fever, or DHF, is an infectious disease caused by the dengue virus. Diseases that are dangerous and at risk of death must be treated quickly and precisely. The determination that someone is suffering from DHF or not can be done with the classification technology, namely the decision tree method, the C5.0 algorithm. Over time, the number of features in the classification increases. Feature reduction is needed for a good model, namely the Binary Particle Swarm Optimization (BPSO) algorithm. This research used 13 features consisting of 12 independent features and one bound feature with two classes: Positive and Negative. The better model in this study is the classification C5.0 algorithm based on BPSO, which can reduce features from 12 to 9 features with an accuracy of 86% compared to classification with the C5.0 algorithm alone, which produces an accuracy of 71%.
Co-Authors Agustina Pradjaningsih I Made Tirta