Analysis of Delivery Data by Medical Staff Using The K-Means Algorithm in Sleman District

Sefitriani Khasanah, Adinda Rifi Yanti, Dwi Puspa Sari, Imam Tahyudin, Dhanar Intan Surya Saputra


The process of childbirth has many factors that result in the death of the baby and the mother during the delivery process, namely the lack of medical staff or health workers (midwife, doctor, or another paramedic). There needs to be an analysis of the delivery process assisted by medical staff. This analysis maps the readiness of medical staff with the needs in the field. Both natural and cesarean births have the same main goal, to make labor run smoothly and ensure that the mother and baby are safe. Deliveries assisted by health workers use secure, clean, and sterile equipment to prevent infection and other health hazards. The hope is to minimize the number of mothers who are not helped during childbirth. This study aims to analyze data on deliveries assisted by medical staff for case studies in Sleman District, Province of Yogyakarta Special Administrative Region, Indonesia, with the K-Means Algorithm. K-means is an unsupervised learning algorithm that has a function to group data into data clusters. This algorithm can accept data without any category labels, the learning process requires a relatively fast time, is quite easy to understand and implement, and the algorithm is quite popular. The research used 13,869 data in 2018. In 2019, the decrease in the number of mothers giving birth from 13,470 who were rescued was 13,469. The 2018 data produced 3 (three) clusters. In 2019 data produced 4 (four) clusters. With different levels of levels assisted by medical staff starting from the high, medium, and low groups.


Childbirth, Clustering, Data Mining, Delivery, K-Means Algorithm

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