Hilmi Pradipta, Anindya (2024) PERBANDINGAN ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK KLASIFIKASI BERAT BADAN LAHIR RENDAH (BBLR) BERDASARKAN DATA HISTORY IBU HAMIL. S1 thesis, Universitas Malikussaleh.

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Abstract

According to the World Health Organization (WHO), Low Birth Weight (LBW) is a condition when a baby weighs less than 2500 grams at birth. Babies born with low birth weight have a higher risk of death, developmental delays, and growth delays when compared to normal weight babies. The steps in this research include literature analysis, system design, integration of C4.5 Algorithm and Naive Bayes Method to a system, and system testing. The purpose of this research is to create a system that can increase efficiency in the Low Birth Weight (LBW) classification process by accelerating and simplifying the process. This study utilizes the data used from the medical records of childbirth at the Ardimulyo Health Center during the period January to August 2021. This study only focuses on factors obtained from the history data of pregnant women and utilizes several attributes such as maternal age, upper arm circumference, and height in pregnant women. The system is developed using the PHP programming language and applies two algorithms, namely the C4.5 algorithm and the Naive Bayes algorithm. This research includes identifying mothers who are at risk of Low Birth Weight (LBW) and mothers who do not have a risk of Low Birth Weight (LBW). The results of this research using 100 data stated that the C4.5 algorithm achieved an accuracy rate of 57% with an error rate of 43%, while Naive Bayes achieved an accuracy rate of 17% with an error rate of 82%. From the data collected, it can be concluded that the use of the C4.5 algorithm is more effective in the classification process compared to Naive Bayes in the context of this research. Keywords: The health risk of babies, LBW, C4.5 Algorithm, Naive Bayes.

Item Type:	Thesis (S1)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Teknik > 55201 - Jurusan Teknik Informatika
Depositing User:	Anindya Hilmi Pradipta
Date Deposited:	20 May 2024 03:20
Last Modified:	20 May 2024 03:20
URI:	https://rama.unimal.ac.id/id/eprint/2369

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