Implementation of CNN Algorithm for Baby Blues Detectionin Postpartum Mothers Through Facial Image Analysis - Muhammad Fikri Hidayattullah; Yustia Hapsari; Movida Tantra Putra Malani; Laela Diyah Puspita; Syeli Mutiatul Hilmy; Zielda Okkya Lorosae (Politeknik Negeri Cilacap, 2025)
The use of computer technology in the healthcare sector is growing, especially in supporting recommendation systems and early detection of various health conditions. Artificial intelligence, particularly deep learning, has made significant contributions in analysing complex data such as medical imaging. One of the leading deep learning methods is Convolutional Neural Network (CNN), which is able to extract visual features hierarchically and accurately. Baby blues is a psychological disorder often experienced by mothers after childbirth and can have a serious impact on the mother's mental health and relationship with the baby. Early detection of baby blues is crucial to provide appropriate interventions and prevent worse outcomes. This research aims to implement CNN algorithm to detect baby blues through facial image analysis. Using a dataset of postpartum mothers, a CNN model was developed to recognise visual patterns related to baby blues symptoms. The results showed that the CNN model was able to identify baby blues conditions with an accuracy of 53% on the dataset used. This research proves the effectiveness of CNN in detecting visual patterns related to babyblues disorder, and is expected to be a solution in supporting early diagnosis and appropriate treatment for postpartum mothers.