Penerapan Sistem Pakar Berbasis Android Dengan Metode Decision Tree Untuk Memprediksi Postpartum Haemorrhage Pada Wanita Hamil - Wiyanto W; Mutiara Ihdina Maulida; Sifa Fauziah (Jurnal Pelita Teknologi, 2021)
Postpartum haemorrhage factor is a contributor to the Maternal Mortality Rate (MMR) 19.7% in the range 12.9 - 28.9 with 480,000 deaths worldwide and 479,000 from developing countries such as Indonesia. In Indonesia the MMR is 305/100,000 Live Births (LB) of the Millennium Development Goals (MDGs) target of only 102/100,000 LB. To achieve the MDGs target, the MMR needs to be lowered, then formulated the problem of how to make an Android-based expert system using the decision tree method so that it can predict Postpartum Haemorrhage from an early age. With the aim of being able to produce an Android-based expert system to predict Postpartum Haemorrhage, so that cases of death caused by Postpartum Haemorrhage receive medical attention from an early age. The expert system makes predictions from logic in an Android-based program using the SDLC structured design system design method and a parallel development model. This logic has gone through the process of classifying a dataset using the Decision Tree method manually and using Rapid Miner. The Decision Tree logic produces three statements of PPH, NO PPH and Potential PPH which are entered using the Java programming language on Android to become an expert system. Pregnant women with predicted PPH and Potential PPH from the expert system can consult a doctor to get the medical personnel they need early to prevent maternal death caused by Postpartum Haemorrhage.