PREDIKSI DIAGNOSIS PENYAKIT JANTUNG MENGGUNAKAN METODE RANDOM FOREST
| dc.contributor.author | Ferdian, Ferdian | |
| dc.contributor.supervisor | Salambue, Roni | |
| dc.date.accessioned | 2023-02-07T02:35:28Z | |
| dc.date.available | 2023-02-07T02:35:28Z | |
| dc.date.issued | 2022-11 | |
| dc.description.abstract | throughout the body. Heart health must be maintained because the mortality rate caused by heart disease is among the highest in the world. So early action is needed to predict the diagnosis of heart disease as a form of prevention or treatment efforts so that there is no increase in cases of heart disease. Therefore, a system is needed that can help predict the diagnosis precisely and accurately and on time based on a computer. This study aims to build a prediction model for the diagnosis of heart disease using the Random Forest method. This prediction model uses the RSI Ibnu Sina medical record dataset of 336 data, including 268 used as training data and 68 testing data. The data attributes used were 11 attributes, namely gender, age, chest pain, systolic blood pressure, diastolic blood pressure, cholesterol, current blood sugar (GDS), RestingECG, heart rate, ST Slope, and diagnosis. This study produced a prediction model with an accuracy of 85.29% measured using the Confusion Matrix. | en_US |
| dc.description.sponsorship | Fakultas Matematika dan Ilmu Pengetahuan Alam | en_US |
| dc.identifier.citation | Perpustakaan | en_US |
| dc.identifier.other | Elfitra | |
| dc.identifier.uri | https://repository.unri.ac.id/handle/123456789/10844 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elfitra | en_US |
| dc.subject | Random Forest | en_US |
| dc.subject | Data Mining. | en_US |
| dc.subject | Prediction | en_US |
| dc.subject | Heart Desease | en_US |
| dc.title | PREDIKSI DIAGNOSIS PENYAKIT JANTUNG MENGGUNAKAN METODE RANDOM FOREST | en_US |
| dc.type | Article | en_US |