Grouping Data in Predicting Infant Mortality Using K-Means and Decision Tree

Authors

  • Ridwansyah Ridwansyah Universitas Nusa Mandiri
  • Verry Riyanto Universitas Bina Sarana Informatika
  • Abdul Hamid Universitas Bina Sarana Informatika
  • Sri Rahayu Universitas Nusa Mandiri
  • Jajang Jaya Purnama Universitas Nusa Mandiri

DOI:

https://doi.org/10.31294/paradigma.v24i2.1399

Keywords:

Decission Tree, K-Means, Baby Death

Abstract

Death is something that we cannot avoid where, when and how death comes. The high infant mortality rate is the main thing and the Indonesian government must prioritize, one of the government's efforts to reduce infant mortality is by conducting a surveillance program, namely PWS KIA where the program is uniting the health of mothers and babies in the local area, basically there are several infant deaths that have causes from the time of pregnancy, accidents, disasters, diseases or because it is destiny from God, for that research is carried out in classifying infant mortality data. For grouping infant mortality data, a K-Means method is needed to analyze data by carrying out a data modeling process without supervision or also known as unsupervised learning. In showing the centroid in the early stages of the k-means algorithm, it is very influential on the results of the cluster carried out on the infant mortality dataset. taken from data.go.id with different centroid results. The results of the clustering model pattern that can be trusted by the government or the Health department to prevent infant mortality. From the clustering results, four labels are tested again using the decision tree algorithm.

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Published

2022-09-14

How to Cite

Ridwansyah, R., Riyanto, V., Hamid, A., Rahayu, S., & Purnama, J. J. (2022). Grouping Data in Predicting Infant Mortality Using K-Means and Decision Tree. Paradigma - Jurnal Komputer Dan Informatika, 24(2), 168-174. https://doi.org/10.31294/paradigma.v24i2.1399