Decision Making System to Determine Childbirth Process with Naïve Bayes and Forward Chaining Methods

Putri Laksita Kumalasari, Riza Arifudin, Alamsyah Alamsyah

Abstract


Childbirth is the last stage before the infant comes into the world. There may be incidents that could cause death in the process of childbirth for mothers and infants. Lack of knowledge and attention to the labor process can increase maternal mortality rate. Maternal mortality rate in Indonesia was recorded at 190 per 100,000 live births on 2015. The figure is still far from the fifth Millennium Development Goals target of 102 per 100,000. The increasing development of technology in health informatics to provide health care more effective can be used to help overcome the problems of pregnant women. To reduce maternal mortality rates, a web-based expert system is perfect one for use. Naïve Bayes method is a simple, fast and high accuracy method. Forward Chaining method is a inference method that performs a fact or statement that starts from the condition (IF) then to the conclusion (THEN). Based on analysis of the method obtained with 233 patients data on childbirth process using expert system, the Naïve Bayes method has accuracy in diagnosing by 90,987124463519% while Forward Chaining method accuracy is 86.69527897%.

Keywords


Childbirth, Naïve Bayes, Forward Chaining

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References


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DOI: https://doi.org/10.15294/sji.v7i2.25352

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