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POSTPARTUM BLUES INCIDENCE ANALYSIS USING NAIVE BAYES ALGORITHM

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Background: Pregnancy, childbirth, and the postpartum period are physiological processes that can affect a woman’s psychological well-being. During the postpartum period, mothers are vulnerable to emotional disturbances such as postpartum blues—a temporary mood disorder caused by hormonal and psychological changes following childbirth. Risk factors contributing to postpartum blues include a mother’s lack of preparedness, limited knowledge, insufficient family support, and stress during pregnancy. If not properly managed, this condition can impact the mother’s mental health and her relationship with the baby. Therefore, early detection is necessary to identify the risk of postpartum blues, one of which is through a Naive Bayes algorithm approach based on data from postpartum mothers in the Banjarmasin region. Objective:.This study aims to analyze the factors contributing to postpartum depression in Banjarmasin City using the Naive Bayes algorithm. Methods: The research method used in this study on the detection of postpartum blues was the Adaboots algorithm with Naïve Bayes Result: This study was conducted from February to November 2024, encompassing the stages of proposal development, obtaining permits, conducting the research, and preparing the final report and results presentation. The process began with submitting a permit application through the LPPM to the Banjarmasin City Health Office, followed by contacting community health centers to obtain data on postpartum mothers. Data collection was conducted among 261 respondents using a questionnaire in both hardcopy and Google Form formats, distributed in person and via WhatsApp. The collected data was then processed, grouped by variables, and analyzed using the Naïve Bayes algorithm with AdaBoost optimization to predict postpartum depression. Conclusion: Most postpartum mothers experienced mild postpartum blues (170 out of 261 respondents), while 91 respondents experienced severe postpartum blues. The highest incidence was found among multiparous mothers. The most influential factors for severe postpartum blues include mothers who are not employed, marriage at ages 15–23, maternal age <20 years and >35 years, vaginal delivery, and educational levels of high school and college. Preventive measures can be taken by avoiding marriage and pregnancy at too young or too old an age, improving health knowledge, and providing optimal family support to postpartum mothers. Keywords: postpartum blues, Naive Bayes, maternal health, obstetrics
Title: POSTPARTUM BLUES INCIDENCE ANALYSIS USING NAIVE BAYES ALGORITHM
Description:
Background: Pregnancy, childbirth, and the postpartum period are physiological processes that can affect a woman’s psychological well-being.
During the postpartum period, mothers are vulnerable to emotional disturbances such as postpartum blues—a temporary mood disorder caused by hormonal and psychological changes following childbirth.
Risk factors contributing to postpartum blues include a mother’s lack of preparedness, limited knowledge, insufficient family support, and stress during pregnancy.
If not properly managed, this condition can impact the mother’s mental health and her relationship with the baby.
Therefore, early detection is necessary to identify the risk of postpartum blues, one of which is through a Naive Bayes algorithm approach based on data from postpartum mothers in the Banjarmasin region.
Objective:.
This study aims to analyze the factors contributing to postpartum depression in Banjarmasin City using the Naive Bayes algorithm.
Methods: The research method used in this study on the detection of postpartum blues was the Adaboots algorithm with Naïve Bayes Result: This study was conducted from February to November 2024, encompassing the stages of proposal development, obtaining permits, conducting the research, and preparing the final report and results presentation.
The process began with submitting a permit application through the LPPM to the Banjarmasin City Health Office, followed by contacting community health centers to obtain data on postpartum mothers.
Data collection was conducted among 261 respondents using a questionnaire in both hardcopy and Google Form formats, distributed in person and via WhatsApp.
The collected data was then processed, grouped by variables, and analyzed using the Naïve Bayes algorithm with AdaBoost optimization to predict postpartum depression.
Conclusion: Most postpartum mothers experienced mild postpartum blues (170 out of 261 respondents), while 91 respondents experienced severe postpartum blues.
The highest incidence was found among multiparous mothers.
The most influential factors for severe postpartum blues include mothers who are not employed, marriage at ages 15–23, maternal age <20 years and >35 years, vaginal delivery, and educational levels of high school and college.
Preventive measures can be taken by avoiding marriage and pregnancy at too young or too old an age, improving health knowledge, and providing optimal family support to postpartum mothers.
Keywords: postpartum blues, Naive Bayes, maternal health, obstetrics.

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