Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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SISTEM PENUNJANG KEPUTUSAN KESEHATAN UNTUK HIPERTENSI MENGGUNAKAN ALGORITMA C4.5
AbstractHypertension is a cardiovascular disease that causes 4.5% of the global disease burden. Hypertension is a major risk factor for heart problems and can be said as the "silent killer" because there are no specific signs and can cause serious illness if left untreated for a long time. Decision support system for hypertension can be used to obtain the results of the decision of the cases, one of them using the decision tree method. Hypertension data will be processed using the method of decision tree algorithm C4.5 through software RapidMiner and will result in a decision support rules, the value of accuracy, and AUC than the rule. After testing the accuracy of the values obtained on the C4.5 algorithm by 76.6%, AUC values for 0862 with a good level of diagnostic classification.
Depkes. (2006). Pharmaceutical Care Untuk Penyakit Hipertensi. Jakarta: Direktorat Bina Farmasi Komunitas Dan Klinik Ditjen Bina Kefarmasian Dan Alat Kesehatan Departemen Kesehatan.
Hamdani. (2010). Sistem Pakar Untuk Diagnosa Penyakit Mata Pada Manusia. Jurnal Informatika Mulawarman. Vol 5 No. 2 Juli 2010 - 13
Han, J., & Kamber, M. (2007). Data Mining Concepts and Techniques. San Fransisco: Mofgan Kaufan Publisher.
Jr, McLeod Raymond. Sistem Informasi Manajemen. Edisi ketujuh. Jilid satu. PT. Penhelindo.Jakarta. 2001.
Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. Yogyakarta: Andi Publishing.
Larose, D. T. (2005). Discovering Knowledge in Databases. New Jersey: John Willey & Sons Inc.
Riduwan. (2008). Metode dan Teknik Menyusun Tesis. Bandung: Alfabeta.
The Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure: The Seventh Report of the Joint National Committee on Detection, Evaluation and Treatment of High Blood Pressure; Hypertension 2003;42:1206-52.
Vercellis, C. (2009). Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate: John Willey & Sons Inc.
Witten, H. I., Eibe, F., & Hall, A. M. (2011). Data Mining Machine Learning Tools and Techniques. Burlington: Morgan Kaufmann Publisher.
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