Implementasi Algoritma Decision Tree (ID3) Untuk Penyakit Campak


  • Inda Srimenganti Teknik Informatika, UIN Sunan Gunung Djati Bandung
  • Ichsan Taufik Teknik Informatika, UIN Sunan Gunung Djati Bandung
  • Edi Mulyana Teknik Elektro, UIN Sunan Gunung Djati Bandung


Measles, Decision Tree (ID3)


Community Health Centre (Puskesmas) is a place for the outpatients. Often, a Community Health Centre does not provide a complete set of medical service. The medical workers –especially doctors often find it challenging to determine a patient’s disease. One of the challenges is to diagnose measles. Iterative Dichotomizer (ID3) is one of the algorithms to help diagnose measles by obtaining data from several attributes. The attributes of the algorithms may vary depends to patients’ need. The algorithm works by calculating the entropy of every attribute and the Information Gain (IG) of every attribute to construct a decision tree. The decision tree helps doctors to diagnose measles. There are 118 data at this research, most of the respondents were diagnosed with measles. The accuracy test of the ID3 algorithm-based application showed the number of 89.83%, categorized as excellent. This concludes that the ID3 algorithm is effective to diagnose measles.


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How to Cite

I. Srimenganti, I. Taufik, and E. Mulyana, “Implementasi Algoritma Decision Tree (ID3) Untuk Penyakit Campak”, SENTER, pp. 235-242, Jan. 2019.

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