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All Journal Infotek : Jurnal Informatika dan Teknologi ABSYARA: Jurnal Pengabdian Pada Masyarakat Jurnal PRINTER : Jurnal Pengembangan Rekayasa Informatika dan Komputer
Intan Komala Dewi Patwari
Universitas Hamzanwadi
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Other

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Journal : Infotek : Jurnal Informatika dan Teknologi

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Komparasi Metode Klasifikasi Data Mining Decision Tree dan Naïve Bayes Untuk Prediksi Penyakit Diabetes Permana, Baiq Andriska Candra; Patwari, Intan Komala Dewi
Infotek : Jurnal Informatika dan Teknologi Vol 4, No 1 (2021): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.743 KB)

Abstract

Diabetes is a group of metabolic diseases which is indicated by the occurrence of hyperglycemia caused by abnormalities in insulin secretion in the body. Many deaths are caused by diabetes, if this disease is not treated immediately, diabetes can cause damage to other organs such as blindness, stores, heart problem and even kidney problem. A best method is needed in classifying diabetes in order to detect diabetes early. Research related to the classification of diabetes using several calcification methods has been done before. In this study, two classification methods were compared, namely decision tree and naïve Bayes. Measurement methods were carried out through cross validation. The results obtained from this study are the best algorithms among the two algorithms to determine diabetes sufferers
Komparasi Metode Klasifikasi Data Mining Decision Tree dan Naïve Bayes Untuk Prediksi Penyakit Diabetes Baiq Andriska Candra Permana; Intan Komala Dewi Patwari
Infotek: Jurnal Informatika dan Teknologi Vol 4, No 1 (2021): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.743 KB) | DOI: 10.29408/jit.v4i1.2994

Abstract

Diabetes is a group of metabolic diseases which is indicated by the occurrence of hyperglycemia caused by abnormalities in insulin secretion in the body. Many deaths are caused by diabetes, if this disease is not treated immediately, diabetes can cause damage to other organs such as blindness, stores, heart problem and even kidney problem. A best method is needed in classifying diabetes in order to detect diabetes early. Research related to the classification of diabetes using several calcification methods has been done before. In this study, two classification methods were compared, namely decision tree and naïve Bayes. Measurement methods were carried out through cross validation. The results obtained from this study are the best algorithms among the two algorithms to determine diabetes sufferers
Co-Authors Ahmadi, Ahwan Alimudin Alimudin Baiq Andriska Candra Permana Hadian Mandala Putra M. Nuzuluddin SIGIT, PUTRA ARI Taufik Akbar Ulul Azmi