KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN METODE RANDOM FOREST

KATANA, MOH. LA NYALA and Utami, Alvi Syahrini and Miraswan, Kanda Januar (2021) KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN METODE RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

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Abstract

Heart disease has modifiable risk factors. Many people have a heart attack without any prior symptoms. Over the past 50 years, more and more people are getting coronary heart disease, and some of the main causative factors have been identified. Previously, heart disease was predicted to be 30% of the causes of death in humans. WHO estimates that in 2005, deaths caused by heart disease in humans. Therefore, a system was created that can classify human heart disease from symptoms that have similarities. In the field of medicine this intelligent system can help classify human heart disease. This system uses the Random Forest method to classify human heart disease using two classes. The Random Forest method uses a dataset of 303 with a total of 10 parameters. In this study, 2 classes were used, namely the detected class and the healthy class. The performance of Random Forest gives the greatest accuracy is 93.00% and the use of trees with a larger number equal to 100 will give the greatest accuracy value. Keywords : Classification, Random Forest, Accuracy, Human Heart Disease.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi, Random Forest, Akurasi, Penyakit Jantung Manusia
Subjects:	Q Science > Q Science (General) > Q1-295 General Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75.5 Mathematics--Periodicals. Computer engineering. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75.5.A142 Computer science. Information society. Information technology. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.76.I58.A3115 Computer science. Computers. Intelligent agents (Computer software) Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D3.H3474 Databases--Handbooks, manuals, etc. Web databases--Handbooks, manuals, etc. Information retrieval--Handbooks, manuals, etc. Electronic data processing--Handbooks, manuals, etc. Data structures (Computer science)--Handbooks, manuals, etc. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.H85 Human-computer interaction--Periodicals. Computer science--Periodicals. Computer science. Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.Z55 Apache Hadoop (Computer file) Electronic data processing--Distributed processing. File organization (Computer science) Data mining. Streaming technology (Telecommunications)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Mr. Moh. La Nyala Katana
Date Deposited:	23 Sep 2021 05:43
Last Modified:	23 Sep 2021 05:43
URI:	http://repository.unsri.ac.id/id/eprint/54585

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