Komparasi Algoritma Data Mining Sebagai Prediksi Harapan Hidup Pasien Gagal Jantung Komparasi Algoritma Data Mining Sebagai Prediksi Harapan Hidup Pasien Gagal Jantung
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Abstract
The heart is the most vital organ of the body. Heart failure is the leading cause of death with the largest number of cases. Therefore, it is necessary to estimate the biggest factor in life expectancy in patients with heart failure, so as to reduce mortality. In predicting the life expectancy of heart failure by using Knowledge Discovery in Database (KDD) it is possible to find predictive patterns of life expectancy for heart failure, so that it can reduce mortality. In this study using the C4.5 algorithm and the C4.5 algorithm with PSO (Particle Swarm Optimization) to obtain a predictive pattern of life expectancy for heart failure which then obtained the percentage of precision, recall and accuracy. This research is to produce a predictive pattern of life expectancy for heart failure with the criteria for the length of time the action has a top priority. By using the C4.5 algorithm, an accuracy of 73.33% is obtained, while using the C4.5 and PSO algorithms an accuracy of 99.00% is obtained, so it can be concluded based on the accuracy level that the C4.5 and PSO algorithm modeling has a higher accuracy than the C4.5 algorithm. . By using the C4.5 algorithm, the ROC graph accuracy is 0.897%, while using the C4.5 and PSO algorithms the ROC graph accuracy is 1.00%, so it can be concluded based on the ROC graph accuracy level that the C4.5 and PSO algorithm modeling has more accuracy. higher than the C4.5 algorithm.
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References
Rokom, “Penyakit Jantung Koroner Didominasi Masyarakat Kota,” kemkes.go.id, 2021. https://sehatnegeriku.kemkes.go.id/
D. P. T. Astuti and I. K. Suardamana, “Gagal Jantung Tinjauan pustaka,” Ilmu Penyakit Dalam, no. 1002005139, p. 1513, 2017.
M. Beyer, R. Lenz, and K. A. Kuhn, Health Information Systems, vol. 48, no. 1. 2006. doi: 10.1524/itit.2006.48.1.6.
D. Cahya Putri Buani, “Penerapan Algoritma Naïve Bayes dengan Seleksi Fitur Algoritma Genetika Untuk Prediksi Gagal Jantung,” EVOLUSI J. Sains dan Manaj., vol. 9, no. 2, pp. 43–48, 2021, doi: 10.31294/evolusi.v9i2.11141.
F. Novaldy and A. Herliana, “Penerapan Pso Pada Naïve Bayes Untuk Prediksi Harapan Hidup Pasien Gagal Jantung,” J. Responsif Ris. Sains dan Inform., vol. 3, no. 1, pp. 37–43, 2021, doi: 10.51977/jti.v3i1.396.
M. Yunus, H. Ramadhan, D. R. Aji, and A. Yulianto, “Penerapan Metode Data Mining C4.5 Untuk Pemilihan Penerima Kartu Indonesia Pintar (KIP),” Paradig. - J. Komput. dan Inform., vol. 23, no. 2, 2021, doi: 10.31294/p.v23i2.11395.
R. S. Asa, S. Defit, and J. Na’am, “Identifikasi Penyaluran Zakat Menggunakan Algoritma C4.5 (Studi Kasus Di Baznas Kabupaten Agam),” J. Sains dan Inform., vol. 4, no. 1, pp. 44–53, 2019, doi: 10.22216/jsi.v4.
D. Safira and Mustakim, “Perbandingan Algoritma C4.5 dengan C4.5+Particle Swarm Optimization untuk Klasifikasi Angkatan Kerja,” J. Politek. Caltex Riau, vol. 7, no. 2, pp. 272–279, 2021.
S. Ucha Putri, E. Irawan, F. Rizky, S. Tunas Bangsa, P. A. -Indonesia Jln Sudirman Blok No, and S. Utara, “Implementasi Data Mining Untuk Prediksi Penyakit Diabetes Dengan Algoritma C4.5,” Januari, vol. 2, no. 1, pp. 39–46, 2021.
E. Fitriani, “Perbandingan Algoritma C4.5 Dan Naïve Bayes Untuk Menentukan Kelayakan Penerima Bantuan Program Keluarga Harapan,” Sistemasi, vol. 9, no. 1, p. 103, 2020, doi: 10.32520/stmsi.v9i1.596.