Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Veronica Agesta Adyastuti
Universitas Duta Bangsa Surakarta
Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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

Found 1 Documents
Search

 1 
CERTAINTY FACTORS FOR DETECTING PREECLAMPSIA RISK BASED ON PREGNANCY TRIMESTER: A LITERATURE REVIEW Nurhayati; Yunita Wisda Tumarta Arif; Darah Ifalahma; Veronica Agesta Adyastuti; Aril Rizal Saputra
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2026: Proceeding of the 7th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/6d5m7c05

Abstract

Preeclampsia is a leading cause of maternal and fetaldeath. Early detection of the risk of preeclampsia isessential. This study aims to analyze the application of theCertainty Factor method in an expert system for earlydetection of preeclampsia risk, identify preeclampsiasymptoms based on trimester of pregnancy, and developrecommendations for developing a knowledge base thatmatches the characteristics of preeclampsia symptoms. Thestudy used a literature review method through articlesearches in the Scopus and Google Scholar databases, aselection process based on inclusion and exclusion criteria,extraction, descriptive analysis, and synthesis of eligiblearticles. The analysis focused on the application of theCertainty Factor method, knowledge sources, symptomsused, inference techniques, diagnostic results, and thecharacteristics of preeclampsia symptoms in each trimesterof pregnancy. The results show that the Certainty Factormethod has been widely applied in expert systems tosupport early detection of preeclampsia and otherpregnancy-related diseases through rule-based reasoningand diagnostic confidence level calculations. Research stilluses a static knowledge base without considering changesin symptom characteristics according to gestational age. Itis proposed that the development of a knowledge basestructured by trimester of pregnancy aims to improveinference accuracy and reduce misinterpretation ofsymptoms.
Co-Authors Aril Rizal Saputra Darah Ifalahma Nurhayati Yunita Wisda Tumarta Arif