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) |
---|---|
Creators: | Creators NIM Email ORCID Phan, Nando NIM03081190049 nandophan@gmail.com UNSPECIFIED |
Contributors: | Contribution Contributors NIDN/NIDK Email Thesis advisor Barus, Okky NIDN0127068803 okky.barus@uph.edu |
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 |
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 |