Phan, Nando (2024) Klasifikasi penyakit jantung pada manusia dengan menggunakan metode decision tree c4.5. Bachelor thesis, Universitas Pelita Harapan.
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Abstract
Penyakit jantung adalah salah satu penyakit yang mematikan, alasan penyakit ini disebut mematikan adalah dikarenakan gejala awalnya yang tidak terdeteksi. Di Indonesia, tingkat penderita penyakit jantung meningkat 1% dalam 5 tahun. Untuk mengatasi masalah ini, dunia medis memerlukan pendeteksi gejala awal seseorang menderita penyakit jantung dengan data mining guna dapat tanggap mencari langkah pengobatan yang tepat. Penulis meggunakan data pasien penyakit jantung cardiovascular untuk mengklasifikasi seseorang mengidap penyakit jantung atau tidak dengan metode Decision Tree berdasarkan algoritma C4.5 dari kaggle yang bernama Cardiovascular Disease Dataset. Dari hasil penelitian, ditemukan 5 atribut (Tekanan Darah, Cholesterol, Glucose, Merokok/Alkohol dan Aktif) yang mempengaruhi klasifikasi positif atau negatif cardiovascular. Hasil penelitian yang menggunakan Decision Tree Algoritma C4.5 menghasilkan 7 buah peraturan tentang klasifikasi apakah seseorang mengidap penyakit jantung dan memiliki tingkat keakuratan 73,25%. / Heart disease is one of the deadliest diseases, the reason it is called deadly is because of the undetecable early systoms. In Indonesia, heart disease patient percentage increases by 1% in 5 years. To solve this problem, medical world needs detector for ealy systoms of heart disease patients by using data mining to be able to responsively search for appropiate treatments. The writter uses heart disease patient data from kaggle named Cardiovascular Disease Dataset to classify whether a person suffers cardiovascular or not by using Decision Tree method based on C4.5 Algorithm. Based on the results, 5 atributes (Blood Pressure, Cholesterol, Glucose, Smoking/Alcohol and Active) was found to affect cardiovascular classifications. The results of using Decision Tree C4.5 Algoritm produces 7 rules of classification about whether a person suffers cardiovascular or not and has the accuracy rates of 73,25%.
Item Type: | Thesis (Bachelor) | ||||||||
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Uncontrolled Keywords: | Cardiovascular; Decision Tree; Algoritma C4.5; Data Mining | ||||||||
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science | ||||||||
Divisions: | University Subject > Current > Faculty/School - UPH Medan > School of Information Science and Technology > Information Systems Current > Faculty/School - UPH Medan > School of Information Science and Technology > Information Systems |
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Depositing User: | Nando phan | ||||||||
Date Deposited: | 09 Aug 2024 06:54 | ||||||||
Last Modified: | 09 Aug 2024 06:54 | ||||||||
URI: | http://repository.uph.edu/id/eprint/64783 |
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