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Pangeran Fadillah Pratama
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering

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Implementasi Algoritma Naïve Bayes Classifier (NBC) untuk Klasifikasi Penyakit Ginjal Kronik Qurotul A'yuniyah; Ena Tasia; Nanda Nazira; Pangeran Fadillah Pratama; Muhammad Ridho Anugrah; Jeni Adhiva; Mustakim Mustakim
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4781

Abstract

Degenerative disease is a non-communicable disease that arises from an unhealthy lifestyle, so that it can reduce the physical and mental quality of the sufferer. Chronic Kidney Disease (CDK) is a degenerative disease that is included in the world's top 10 causes of death according to the World Health Organization (WHO). This study used CDK data with attributes of age, blood pressure, weight, albumin levels, sugar levels, red blood cells, pus cells, pus cell clots, bacteria, blood sugar levels, blood urea levels, creatinine serum, sodium, magnesium, hemoglobin, the volume occupied by red blood, indications of hypertension, indications of diabetes mellitus, indications of coronary heart disease, appetite, indications of swelling in the calves or feet, and indications of anemia. Therefore, the classification of kidney disease data is carried out with the implementation of the superior Naïve Bayes Classifier (NBC) algorithm and produces a high level of accuracy. The classification results using the RapidMiner tools carried out by the application of the NBC algorithm, the accuracy value is 96.43%, the average recall is 93.18%, the average precision is 93.02%, and the AUC is 93.2%. so it can be concluded that the performance of NBC in classifying chronic kidney disease data is excellent.
Co-Authors Ena Tasia Jeni Adhiva Muhammad Ridho Anugrah Mustakim Mustakim Nanda Nazira Qurotul A'yuniyah