Identifikasi pohon keputusan hipertensi dengan sistem RapidMiner dan metode klasifikasi

Iskandar Zulkarnaen; Okty Eka Wulandari; Padulah; Hibarkah Kurnia

doi:10.32734/trophico.v3i2.13226

Authors

Iskandar Zulkarnaen Departemen Teknik Industri, Fakultas Teknik, Universitas Bhayangkara, Bekasi, Indonesia https://orcid.org/0000-0003-1057-4772
Okty Eka Wulandari Program Studi Teknik Industri, Fakultas Teknik, Universitas Bhayangkara, Bekasi, Indonesia https://orcid.org/0009-0007-1534-733X
Padulah Departemen Teknik Industri, Fakultas Teknik, Universitas Bhayangkara, Bekasi, Indonesia https://orcid.org/0000-0002-0410-921X
Hibarkah Kurnia Departemen Teknik Industri, Fakultas Teknik, Universitas Pelita Bangsa, Bekasi, Indonesia https://orcid.org/0000-0002-1263-230X

DOI:

https://doi.org/10.32734/trophico.v3i2.13226

Keywords:

Classification Method, Decision Trees, High Blood, Identification, RapidMiner System

Abstract

At present health is very important to support all activities, both work and sports. Sometimes people ignore it in health checks, especially related to blood health. Early detection is very important in controlling the health of the body in everyday life. One of the factors that people often complain about is high blood pressure, which hinders all activities. High blood pressure is a disease caused by increased blood pressure. This study aims to identify the decision tree for high blood pressure for residents around the Bekasi area with sample data used, namely data on age, weight, blood type, and susceptibility to high blood pressure by distributing questionnaires. This study uses the RapidMiner system and data classification methods through questionnaires as respondents and health service test samples. The findings of this study are 6 people who allow for high blood pressure of various ages. The potential for high blood pressure is detected from the age of over 34 with a total of 3 people from blood groups B and O. The results of this study based on an accuracy rate of 77.78% show that of the 32 people in the high blood questionnaire data, 3 people are aged over 34 and 3 people aged 3 people.

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Identifikasi pohon keputusan hipertensi dengan sistem RapidMiner dan metode klasifikasi

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