Skripsi

IMPLEMENTASI METODE FUZZY DECISION TREE C4.5 UNTUK KLASIFIKASI STATUS KESEHATAN JANIN PADA DATA CARDIOTOCOGRAPHY BERDASARKAN K-FOLD CROSS VALIDATION

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Penilaian

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The fetus or fetus is a creature in the womb that is developing after the embryo. Unhealthy maternal health conditions can threaten the health and safety of the fetus, as well as fetal health can threaten the health conditions of the mother to the developmental stage until death during childbirth. Worldwide, an estimated 2.8 million infants and pregnant women die each year. Therefore, the importance of monitoring is to determine the health condition of the fetus. In this study using secondary data taken from kaggle.com, the data consists of 2126 cases with 3 classifications of fetal conditions. Prediction of fetal health status in cardiotocography data using the fuzzy decision tree method based on k-fold cross validation produces an average accuracy value of 90,62%, micro precision of 85,94%, macro precision of 73,66%, micro recall of 85, 94%, and macro recall of 74,76%. Keywords: Fetus, Cardiotocography, Fuzzy Decision Tree, K-Fold Cross Validation.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307001738	T94969	T949692023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T949692023

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Sriwijaya., 2023

Collation

vi, 53 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

511.307

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Fuzzy Logic
Jurusan Matematika

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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