AI-Based Education and Early Detection of Baby Blues Syndrome Among Postpartum Mothers

Citra Dewi megawati; hapsari dian sylvatri; asril kurniadi; Bima Romadhon Parada Dian Palevi

doi:10.32734/jst.v9i1.26673

Authors

Citra Dewi megawati Brawijaya University
hapsari dian sylvatri
asril kurniadi
Bima Romadhon Parada Dian Palevi

DOI:

https://doi.org/10.32734/jst.v9i1.26673

Keywords:

Mental Health, Postpartum Mothers, Baby Blues Syndrome, Artificial Intelligence, Community Engagement

Abstract

Postpartum maternal mental health is an essential aspect that is often overlooked in maternal healthcare services. Many mothers experience emotional changes after childbirth known as baby blues syndrome; however, these symptoms are often perceived as normal and do not receive adequate medical attention. The lack of public understanding and awareness regarding this condition is the main cause of delayed detection and treatment. Therefore, this community service project aims to promote postpartum maternal mental health awareness through the socialization of baby blues syndrome and the implementation of an Artificial Intelligence (AI)-based early detection system for baby blues syndrome in the Malang region. The method employed is a quantitative descriptive approach involving 30 postpartum mothers who completed the Edinburgh Postnatal Depression Scale (EPDS) questionnaire. The collected data were processed using machine learning algorithms to classify levels of mental health risk. The results showed that 43% of mothers experienced mild symptoms of baby blues, 27% moderate, 10% severe, and 20% showed no indication of the disorder. The AI system demonstrated an accuracy rate of 87.5%. The conclusion of this community engagement program indicates that the integration of AI technology and public education is effective in enhancing early detection and increasing postpartum mothers’ awareness of the importance of mental health.

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