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All Journal Jurnal Informatika dan Teknik Elektro Terapan
Alfajr, Nur Halizah
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Computer Science & IT

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STUDI KOMPARASI ALGORITMA RANDOM FOREST CLASSIFIER DAN SUPPORT VECTOR MACHINE DALAM PREDIKSI PENYAKIT JANTUNG Alfajr, Nur Halizah; Garno, Garno; Yusup, Dadang
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6569

Abstract

Heart disease is a non-communicable disease with a high mortality rate both globally and in Indonesia. According to WHO, around 17.9 million deaths occur each year due to cardiovascular diseases. Early prediction is crucial to reducing mortality and improving life expectancy. This study compares the performance of machine learning algorithms Random Forest Classifier and Support Vector Machine in predicting heart disease. The dataset consists of 5432 medical records from cardiac outpatients at RSUD Kabupaten Bekasi in 2024, with two classes (labeled 1 (heart disease) = 3068 and labeled 0 (non-heart disease) = 2364). Models were developed using the Knowledge Discovery in Databases (KDD) approach. Evaluation results show that the Support Vector Machine model achieved the best performance compared to Random Forest Classifier with 65% accuracy, 70% precision, 68% recall, and 64% f-measure. Cross-validation and ROC analysis also indicated that Support Vector Machine obtained the highest AUC score, ranging from 0.67 to 0.68, which is categorized as poor classification.
PREDIKSI PENYAKIT JANTUNG MENGGUNAKAN METODE RANDOM FOREST DAN PENERAPAN PRINCIPAL COMPONENT ANALYSIS (PCA) Alfajr, Nur Halizah; Defiyanti, Sofi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5055

Abstract

Heart disease is a significant public health issue and the leading cause of death worldwide. Risk factors such as hypertension, diabetes, obesity, sedentary lifestyle, smoking, and genetic factors contribute to the development of heart disease. This study aims to develop a heart disease prediction model using the Random Forest method. The dataset used comes from the UCI Machine Learning Repository, containing data from 1026 patients with various health features. The methods used include the stages of knowledge discovery in databases (KDD), namely data selection, preprocessing, transformation, data mining, and evaluation. The study results show that the model with 100 decision trees achieved an accuracy of 0.9823. Further evaluation using the confusion matrix and classification report indicates that the Random Forest method provides 98% accuracy, 100% precision, 96% recall, and a 98% F1-score. In conclusion, the Random Forest method is effective in predicting heart disease, with features such as thal having a significant impact on model accuracy.
Co-Authors garno, Garno Sofi Defiyanti Yusup, Dadang