Analisis Perbandingan Kinerja Algoritma K- Nearest Neighbor (KNN) Dan C4.5 Untuk Klasifikasi Teknik Persalinan

Alfianah, Adinda Bunga (2024) Analisis Perbandingan Kinerja Algoritma K- Nearest Neighbor (KNN) Dan C4.5 Untuk Klasifikasi Teknik Persalinan. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

Labor is the expulsion of a fetus and placenta that has reached full term through the birth canal or other means, with or without medical assistance. There are two types of childbirth, namely vaginal or vaginal delivery and Sectio Caesarean delivery. In 2018, the Indonesian Ministry of Health conducted a survey which found that the number of births using the Caesarean method had exceeded the maximum standard set by WHO, which was 17.6%. At Aulia Hospital Pekanbaru, the prevalence of Sectio Caesarea delivery reached 76% per 1000 births. This type of research is analytic quantitative research with the Secondary Data Analysis (ADS) method. The dataset used was 500 data with 11 variables. The test results show that the ratio of training data and testing data, K value and sampling type have an influence on the accuracy value. The accuracy results of the KNN algorithm are better than the C4.5 algorithm in predicting labor techniques, namely 94%. While the results of the Confusion Matrix calculation with the C4.5 method are superior to the precision value which is 84.62%. The result of the rule tree that partus history is the main key in determining labor.

Item Type: Thesis (Undergraduate)
Contributors:
ContributionContributorsNIDN/NIDK
Thesis advisorPratama, Mudafiq RiyanNIDN0709058903
Uncontrolled Keywords: C4.5, KNN, Kinerja
Subjects: 100 - Rumpun Matematika dan Ilmu Pengetahuan Alam (MIPA) > 110 - Ilmu IPA > 113 - Biologi
340 - Rumpun Ilmu Kesehatan > 370 - Ilmu Keperawatan dan Kebidanan > 372 - Kebidanan
340 - Rumpun Ilmu Kesehatan > 370 - Ilmu Keperawatan dan Kebidanan > 373 - Administrasi Rumah Sakit
340 - Rumpun Ilmu Kesehatan > 370 - Ilmu Keperawatan dan Kebidanan > 379 - Analis Medis
340 - Rumpun Ilmu Kesehatan > 350 - Ilmu Kesehatan Umum > 355 - Epidemiologi
Divisions: Jurusan Kesehatan > Prodi D4 Manajemen Informasi Kesehatan > Tugas Akhir
Depositing User: Adinda Bunga Alfianah
Date Deposited: 08 May 2024 06:42
Last Modified: 08 May 2024 06:42
URI: https://sipora.polije.ac.id/id/eprint/31752

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