PERBANDINGAN KINERJA ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST DALAM MEMPREDIKSI RESIKO PENYAKIT JANTUNG BERDASARKAN DATA KESEHATAN

TAMAM, MUHAMAD AZRIL FAHRI (2025) PERBANDINGAN KINERJA ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST DALAM MEMPREDIKSI RESIKO PENYAKIT JANTUNG BERDASARKAN DATA KESEHATAN. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Heart disease remains one of the leading causes of death worldwide, making datadriven early detection essential to support prevention efforts. This study compares the performance of two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), in predicting heart disease risk using public health data. The dataset was processed through cleaning, normalization, categorical variable encoding, and class balancing using the SMOTEENN method. Model evaluation was conducted using accuracy, F1-score, and AUC-ROC metrics. The results show that Random Forest achieved the best overall performance with an accuracy of 87%, a positive class F1-score of 0.36, and an AUC-ROC of 0.7945. Meanwhile, SVM obtained an accuracy of 74%, a positive class F1-score of 0.33, and an AUC-ROC of 0.7909. These findings indicate that Random Forest outperforms SVM in overall predictive accuracy, while SVM remains competitive with slightly better sensitivity in distinguishing positive heart disease cases.kompetitif dalam membedakan kelas positif penyakit jantung. Kata kunci: Heart disease, Support Vector Machine, Random Forest, Prediction, Machine Learning. Penyakit jantung merupakan salah satu penyebab utama kematian di dunia, sehingga deteksi dini berbasis data menjadi penting untuk mendukung upaya pencegahan. Penelitian ini membandingkan kinerja dua algoritma machine learning, yaitu Support Vector Machine (SVM) dan Random Forest (RF), dalam memprediksi risiko penyakit jantung dengan menggunakan data kesehatan publik. Dataset diproses melalui tahap pembersihan, normalisasi, pengkodean variabel kategorikal, serta penyeimbangan kelas menggunakan metode SMOTEENN. Evaluasi model dilakukan dengan menggunakan metrik akurasi, F1-score, dan AUC-ROC. Hasil pengujian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi 87%, F1-score kelas positif 0,36, dan AUC-ROC 0,7945. Sementara itu, SVM menghasilkan akurasi 74%, F1-score kelas positif 0,33, dan AUC-ROC 0,7909. Hal ini mengindikasikan bahwa Random Forest lebih unggul dalam prediksi keseluruhan, sedangkan SVM memiliki kinerja yang sedikit lebih rendah namun tetap kompetitif dalam membedakan kelas positif penyakit jantung. Kata kunci: Penyakit jantung, Support Vector Machine, Random Forest, Prediksi, Machine Learning

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 25 192
NIM/NIDN Creators:	41521010087
Uncontrolled Keywords:	Penyakit jantung, Support Vector Machine, Random Forest, Prediksi, Machine Learning
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 600 Technology/Teknologi > 610 Medical, Medicine, and Health Sciences/Ilmu Kedokteran, Ilmu Pengobatan dan Ilmu Kesehatan > 616 Diseases/Penyakit > 616.1 Diseases of Cardiovascular System/Penyakit pada Sistem Kardiovaskular
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	15 Oct 2025 03:41
Last Modified:	15 Oct 2025 03:41
URI:	http://repository.mercubuana.ac.id/id/eprint/99287

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