Implementasi Algoritma Decision Tree (ID3) Untuk Penyakit Campak
Keywords:
Measles, Decision Tree (ID3)Abstract
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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References
C. pengertian Penyakit, No Title, Yudoyono. 2005.
L. Fakta and P. Menular, “Campak,” pp. 1–3, 2008.
Santosa, B. Teknik Pemanfaatan Data untuk Keperluan Bisnis. Graha Ilmu: Yogyakarta. 2007
Wahyudin, “Metode Iterative Dichotomizer 3 (ID3) untuk Penyeleksian Penerimaan Mahasiswa Baru,” J. Pendidik. Teknol. Inf. Dan Komun., vol. 2, no. 2, pp. 5–15, 2009.
David, Mcg. Tutorial: The ID3 Decision Tree Algorithm. Monash University Faculty of Information Technology. 2004
D. Setiawati, I. Taufik, Jumadi, and W. Z. Budiawan, “Klasifikasi Terjemahan Ayat Al-Quran Tentang Ilmu Sains Menggunakan Algoritma Decision Tree Berbasis Mobile,” J. Online Inform., vol. 1, no. 1, pp. 24–27, 2016.
A. K. Nugroho and D. Iskandar, “Algoritma Iterative Dichotomizer 3 ( ID3 ) Pengambilan Keputusan Decision Making Using Iterative Dichotomizer 3 Algorithm,” vol. 3, pp. 43–47.
A. E. Tyasti, D. Ispriyanti, and A. Hoyyi, “1 , 2 , 3 1,” J. Gaussian, vol. 4, no. 2, pp. 237–246, 2015.
D. Lavanya and K. U. Rani, “Performance Evaluation of Decision Tree Classifiers on Medical Datasets,” Int. J. Comput. Appl., vol. 26, no. 4, pp. 1–4, 2011.
