KOMPARASI ALGORITMA DECISION TREE DAN SUPPORT VECTOR MACHINE (SVM) DALAM KLASIFIKASI SERANGAN JANTUNG
Keywords:
Classification, Decision Tree, Heart Attack, Splitting Data, Support Vector Machine
Abstract
The heart is one of the most important organs in the human body. According to the WHO, heart attacks are the most common cause of sudden death worldwide, with more than 17.8 million people dying from heart attacks. A heart attack occurs when blood flow to the coronary arteries stops, depriving the heart muscle of oxygen, and causing a heart attack. Detecting a heart attack is very difficult due to the various symptoms. The purpose of this research is to compare the performance of the accuracy values of two algorithms, namely Decision Tree and Support Vector Machine (SVM) in classifying heart attacks. The results of this study show that the Decision Tree algorithm achieves the highest accuracy results compared to the SVM algorithm. The accuracy of the Decision Tree algorithm using a 60:40 ratio data splitting is 98.11% with a negative precision of 98.01% and positive of 98.17% and a negative recall of 97.04% and positive of 98.77%. Meanwhile, the SVM algorithm using data splitting with the same ratio produces an accuracy value of 92.80% with a negative precision of 90.24% and a positive of 94.43% and a negative recall of 91.13% and a positive of 93.85%.
References
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Riyantoni, N. H., Bahreisy, M. F., Hakim, I., & Rolliawati, D. (2023). Komparasi Support Vector Machine (SVM) Dan Autoregressive Integrated Moving Average (Arima) Pada Peramalan Hujan Di Daerah Albury, Australia. Jurnal Sistem Informasi Dan Informatika (Simika), 6(1), 59–68. https://doi.org/10.47080/simika.v6i1.2412
Septhya, D., Rahayu, K., Rabbani, S., Fitria, V., Rahmaddeni, R., Irawan, Y., & Hayami, R. (2023). Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(1), 15–19. https://doi.org/10.57152/malcom.v3i1.591
Suriani, U. (2023). Penerapan Data Mining untuk Memprediksi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma Decision Tree C4.5. Journalcisa, 3(2), 55–66.
Suryani, S., & Mustakim, M. (2022). Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine. Bulletin of Informatics and Data Science, 1(2), 81. https://doi.org/10.61944/bids.v1i2.36
Aniamarta, T., Salsabilla Huda, A., & Lizariani Aqsha, F. (2022). Causes and Treatments of Heart Attack. Biologica Samudra, 4(1), 22–31. https://doi.org/10.33059/jbs.v4i1.3925
Apriyani, H., & Kurniati, K. (2020). Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus. Journal of Information Technology Ampera, 1(3), 133–143. https://doi.org/10.51519/journalita.volume1.isssue3.year2020.page133-143
Azhari, M., Situmorang, Z., & Rosnelly, R. (2021). Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes. Jurnal Media Informatika Budidarma, 5(2), 640. https://doi.org/10.30865/mib.v5i2.2937
Barata, M. A., Edi Noersasongko, Purwanto, & Moch Arief Soeleman. (2023). Improving the Accuracy of C4.5 Algorithm with Chi-Square Method on Pure Tea Classification Using Electronic Nose. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(2), 226–235. https://doi.org/10.29207/resti.v7i2.4687
Dini Serangan Jantung Saat Aktivitas Olahraga, D., & Gunardi, S. (2023). Early Detection of Heart Attack During Sports Activities. Jurnal Hasil Kegiatan Pengabdian Masyarakat Indonesia, 1(3), 257–265.
Dwifebri, M., Irvan, M., Imam, A., & Adiwijaya. (2021). JURNAL RESTI Perbandingan Support Vector Machine dan Modified Balanced Random. 1(10), 393–399.
Eina, M. F., Chrisnanto, Y. H., & Melina, M. (2024). Klasifikasi Telemarketing Menggunakan Naïve Bayes Classification Dan Wrapper Sequential Feature Selection. INTECOMS: Journal of Information Technology and Computer Science, 7(4), 1189–1198. https://doi.org/10.31539/intecoms.v7i4.10846
Ermy Pily, A. K., Oktavianda, Aprilia, F., Rahmaddeni, & Efrizoni, L. (2024). Komparasi Algoritma K-Nearest Neighbors dan Naïve Bayes dalam Klasifikasi Penyakit Diabetes Gestasional. Indonesian Journal of Computer Science, 13(1), 1195–1209. https://doi.org/10.33022/ijcs.v13i1.3714
Guntara, R. G. (2023). Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7. 2(1), 9–18.
Hartawan, M. S., Erkamim, M., Rachmawati, S., Santi, N. C., Legito, L., & Sepriano, S. (2023). Penerapan Algoritma Supervised Learning untuk Klasifikasi Program Keluarga Harapan. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 83–91. https://doi.org/10.57152/malcom.v3i2.873
Hasanah, H., & Nurmalitasari. (2023). Perbandingan Tingkat Akurasi Algoritma Support Vector Machines (SVM) dan C45 dalam Prediksi Penyakit Jantung. Prosiding Seminar Nasional Teknologi Dan Sains, 2, 13–18.
Munir, A. S., Saputra, A. B., Aziz, A., & Barata, M. A. (2024). Perbandingan Akurasi Algoritma Naive Bayes dan Algoritma Decision Tree dalam Pengklasifikasian Penyakit Kanker Payudara. Jurnal Ilmiah Informatika Global, 15(1), 23–29. https://doi.org/10.36982/jiig.v15i1.3578
Purnomo, A., Barata, M. A., Soeleman, M. A., & Alzami, F. (2020). Adding feature selection on Naïve Bayes to increase accuracy on classification heart attack disease. Journal of Physics: Conference Series, 1511(1). https://doi.org/10.1088/1742-6596/1511/1/012001
Putri, A. D., Sholekhah, F., & Dadynata, E. (2024). The Application of C4 . 5 Decision Tree Algorithm for Predicting the Survival Rate of Thyroid Cancer Patients Penerapan Algoritma Decesion Tree C4 . 5 untuk Memprediksi Tingkat Kelangsungan Hidup Pasien Kanker Tiroid. 4(October), 1485–1495.
Qibtiyah, M., & Cahyani, N. (2023). Prediksi Tingkat Kelahiran Bayi Di Kabupaten Bojonegoro Dengan Menggunakan Algoritma Naive Bayes. 1–7.
Rahayu, D. S., Afifah, J., & Intan, S. (2023). Classification of Diabetes Mellitus Using C4 . 5 Algorithm , Support Vector Machine ( SVM ) and Linear Regression Klasifikasi Penyakit Diabetes Melitus Menggunakan Algoritma C4 . 5 , Support Vector Machine ( SVM ) dan Regresi Linear. SENTIMAS: Seminar Nasional Penelitian Dan Pengabdian Masyarakat, 1(1 SE-), 56–63.
Riyantoni, N. H., Bahreisy, M. F., Hakim, I., & Rolliawati, D. (2023). Komparasi Support Vector Machine (SVM) Dan Autoregressive Integrated Moving Average (Arima) Pada Peramalan Hujan Di Daerah Albury, Australia. Jurnal Sistem Informasi Dan Informatika (Simika), 6(1), 59–68. https://doi.org/10.47080/simika.v6i1.2412
Septhya, D., Rahayu, K., Rabbani, S., Fitria, V., Rahmaddeni, R., Irawan, Y., & Hayami, R. (2023). Implementasi Algoritma Decision Tree dan Support Vector Machine untuk Klasifikasi Penyakit Kanker Paru. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(1), 15–19. https://doi.org/10.57152/malcom.v3i1.591
Suriani, U. (2023). Penerapan Data Mining untuk Memprediksi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma Decision Tree C4.5. Journalcisa, 3(2), 55–66.
Suryani, S., & Mustakim, M. (2022). Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine. Bulletin of Informatics and Data Science, 1(2), 81. https://doi.org/10.61944/bids.v1i2.36
Published
2025-01-16
How to Cite
Laili, E., Alawi, Z., Rohmah, R., & Barata, M. (2025). KOMPARASI ALGORITMA DECISION TREE DAN SUPPORT VECTOR MACHINE (SVM) DALAM KLASIFIKASI SERANGAN JANTUNG. Jurnal Sistem Informasi Dan Informatika (Simika), 8(1), 67-76. https://doi.org/10.47080/simika.v8i1.3683
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