Skripsi

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

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Penilaian

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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%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107003458	T58331	T583312021	Central Library (REFERENCES)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T583312021

Publisher

Inderalaya : Prodi Ilmu Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam., 2021

Collation

xiii, 80 hlm. : ilus. ; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

518.107

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Algoritma
Prodi Ilmu Matematika
Penyakit Stroke

Specific Detail Info

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Statement of Responsibility

DS

Other version/related

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