Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
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.
Abstract viewed = 293 times
PDF downloaded = 230 times
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.