Perbandingan Antara Algoritma ID3 dan Naive Bayesian Classification dalam Mengelompokkan Status Gizi

Authors

  • Tri Astuti Universitas Pamulang
  • Zanuar Rifai STMIK Amikom Purwokerto
  • Diyo Mardiyanto STMIK Amikom Purwokerto
  • Bagas Luka A. L. STMIK Amikom Purwokerto
  • Angga Tri S. STMIK Amikom Purwokerto
  • Widihastuti Widihastuti STMIK Amikom Purwokerto
  • Tia Dwi STMIK Amikom Purwokerto

Keywords:

Naive Bayesian Classification, ID3, Data Mining, Nutritional status

Abstract

Changes in body weight of infants from time to time an early indication of changes in nutritional status. It is important for parents to control their nutritional status. For parents who already know the nutritional status of children, can control the weight so as to achieve ideal weight according to KMS. It is necessary for the proper method in determining the nutritional status of infants. The method used in this study includes the data collection is done by observation and interviews, then data will be tested using Weka 3.8.1 application in some algorithms. That where the latter algorithm has the highest accuracy can be used as a method to classify the nutritional status of children. From the results of testing several algorithms, obtained Naive Bayesian Classification algorithm that has the highest accuracy of 91.4634% compared with other algorithms. Naive Bayesian Classification algorithm so that it can be used as a method to classify the nutritional status of infants.
Vol.2 No.1 : Peningkatan Inovasi Teknologi Terkini

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Published

2017-06-30

Issue

Vol. 2 No. 1 (2017): Vol.2 No.1 : Peningkatan Inovasi Teknologi Terkini

Section

Article

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