KOMPARASI METODE KLASIFIKASI DECISION TREE ALGORITMA C4.5 DAN RANDOM FOREST UNTUK PREDIKSI PENYAKIT STROKE

AZIZAH, NUR and Resti, Yulia and Kresnawati, Endang Sri (2021) KOMPARASI METODE KLASIFIKASI DECISION TREE ALGORITMA C4.5 DAN RANDOM FOREST UNTUK PREDIKSI PENYAKIT STROKE. Undergraduate thesis, Sriwijaya University.

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Abstract

Stroke is a disease that disrupts the nervous system of the human brain. Stroke disease as one of the leading causes of death and serious disability with a high possibility of becoming an epidemic in the world in the next few decades. Therefore, it is necessary to predict as a first step in anticipating the occurrence of stroke in order to prevent or minimize the occurrence of disability. This research uses secondary data obtained from kaggle.com. This data has 11 variables and 5110 data. Prediction of occurrence using the Decision Tree and Random Forest methods. In the Random Forest method, 120 trees were built. The results of this research are the accuracy of the decision tree of 92.56% and the random forest of 93.80%. Precision decision tree is 95.45% and random forest is 98.76%. Recall decision tree is 97.09% and random forest is 94.91%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Stroke, Decision Tree, Random Forest
Subjects:	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.D343 Data mining. Database searching. Big data. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	NUR AZIZAH
Date Deposited:	03 Dec 2021 03:53
Last Modified:	03 Dec 2021 03:53
URI:	http://repository.unsri.ac.id/id/eprint/58331

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