Expert System for Early Detection of Preeclampsia Using Hybrid Fuzzy Tsukamoto and Certainty Factor
Authors
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Fachruddin Fahma Khoiri
Politeknik Negeri Jember, Jember, Indonesia
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Fatimatuzzahra
Politeknik Negeri Jember, Jember, Indonesia
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Khen Dedes
Politeknik Negeri Jember, Jember, Indonesia
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Zilvanhisna Emka Fitri
Politeknik Negeri Jember, Jember, Indonesia
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Nadzirotul Fitriyah
Universitas Ibrahimy, Situbondo, Indonesia
DOI:
https://doi.org/10.30812/bite.v8i1.6579Keywords:
Certainty Factor, Early Detection, Expert System, Fuzzy Tsukamoto, Preeclampsia, WebsiteAbstract
Background: Preeclampsia is one of the pregnancy complications that remains a leading cause of maternal and infant mortality in Indonesia, making its early detection critically important. However, manual screening for preeclampsia still faces limitations in terms of time, availability of medical personnel, and subjectivity in symptom interpretation.
Objective: This study aims to develop a web-based expert system to detect the risk of preeclampsia in pregnant women.
Methods: This study applying a hybrid method combining Fuzzy Tsukamoto and Certainty Factor (CF). The Fuzzy Tsukamoto method is used to process numerical clinical data, such as systolic and diastolic blood pressure and urine protein levels, while the Certainty Factor method is used to represent the confidence level of subjective symptoms reported by patients, such as severe headache and visual disturbances.
Result: Testing results show that the system is able to produce diagnoses consistent with expert assessments across all tested case studies. For instance, in one case study, the system produced a diagnosis of Severe Preeclampsia with a confidence level of 82.04%, closely matching the expert's confidence level of 80%, with a difference of only 2.04%.
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Copyright (c) 2026 Fachruddin Fahma Khoiri, Fatimatuzzahra, Khen Dedes, Zilvanhisna Emka Fitri, Nadzirotul Fitriyah
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