Skripsi
KLASIFIKASI STATUS KESEHATAN JANIN PADA DATA KARDIOTOKOGRAFI MENGGUNAKAN METODE FUZZY NAIVE BAYES BERDASARKAN K-FOLD CROSS VALIDATION
During pregnancy, a mother must always pay attention to her health, because an unhealthy fetus can threaten the health of pregnant women. It is important to monitor the health condition of the fetus in order to detect any abnormal symptoms in the fetus. Cardiotocography (CTG) is a tool that serves to monitor fetal activity such as fetal heart rate (FHR), uterine contractions (UC), and several things that are needed when the fetus is in the womb. This study aims to classify fetal health status on cardiotography data using the Fuzzy Naive Bayes method based on K-Fold Cross Validation. The data used is secondary data obtained from kanggle.com with a total of 2126 data consisting of 1655 normal cases, 295 suspect cases, and 176 pathologic cases with 21 predictor variables and 1 target variable. The classification uses K-Fold Cross Validation as much as 10 folds so that the average percentage accuracy value is 88.02%, precision on macro data is 72.73%, recall on macro data is 71.01% and precision and recall on micro data produce the same value of 82.03%. Keywords: Fetal Health Status, Fuzzy Naive Bayes, K-Fold Cross Validation
Inventory Code | Barcode | Call Number | Location | Status |
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2307003935 | T94978 | T949782023 | Central Library (Referens) | Available but not for loan - Not for Loan |
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