Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE
Abdul Latif
Sistem Informasi, Universitas Bina Sarana Informatika
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimization of Random Forest Model with SMOTE for Fetal Health Classification Based on Cardiotocography Abdul Latif; Siti Khotimatul Wildah
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4360

Abstract

Fetal health during pregnancy is a crucial aspect in ensuring the optimal growth and development of a child, particularly during the golden period of life from the womb to the age of two. In the medical field, monitoring fetal conditions is vital to detect potential risks as early as possible. One of the tools commonly used in this process is cardiotocography (CTG), which provides essential data on fetal heart activity and movement. With technological advancements, machine learning-based approaches are increasingly being utilized to process CTG data more effectively. However, a major challenge in classifying medical data such as CTG lies in class imbalance, where the distribution between majority and minority classes is uneven. This study evaluates the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in addressing this imbalance and assesses the performance of the Random Forest algorithm in classifying fetal health conditions. The results show that the combination of SMOTE and Random Forest achieves the best performance compared to other methods, with an accuracy of 94.40%, precision of 94.45%, recall of 94.40%, and an F1-score of 94.38%. These findings indicate that SMOTE is effective in improving the representation of minority classes, while Random Forest demonstrates superior and consistent classification performance on CTG data
Co-Authors Siti Khotimatul Wildah