Sistem Prototype Klasifikasi Risiko Kehamilan Dengan Algoritma k-Nearest Neighbor (k-NN)

Atma Deharja (Politeknik Negeri Jember)
Maya Weka Santi (Politeknik Negeri Jember)
Muhammad Yunus (Politeknik Negeri Jember)
Ervina Rachmawati (Politeknik Negeri Jember)

Jurnal Teknologi Informasi dan Multimedia
Vol 4 No 1 (2022): May

The increasing maternal mortality rate (MMR) in Indonesia in the last two decades has become a serious concern for the government. Moreover, Jember Regency is one of the areas with the highest MMR in East Java. Where in 2018 the AKI of Jember Regency was ranked 10th with an AKI of 114/100,000 KH and was the 5th highest rank in 2019 with 133.4/100,000 KH. The process of recording pregnancy data that is still done manually can also affect the AKI process because it can slow down the decision-making process for pregnant women who are at risk. In this study, the focus is on creating a recording system for pregnant women according to cohort data and equipped with features to support pregnancy risk classification according to the KSPR standard. So that it is expected to provide an early decision on the risk of pregnancy to related parties. The results of the system trial show that the k-NN system developed is able to help the computational process faster by complementing the classification results with an accuracy rate of up to 80%.