Perbandingan Algoritma Klasifikasi Data Mining Dalam Diagnosa Penyakit Arteri Koroner
-
Bernadus Gunawan Sudarsono
*
Universitas Bung Karno, Jakarta,
Indonesia
- Edy Winarno Universitas Muhammadiyah Semarang, Semarang, Indonesia (*) Corresponding Author
Abstract
Coronary artery disease is one of the diseases that often attacks humans. The cause of this disease is due to narrowing or blockage of the coronary blood vessels that supply blood to the heart. The diagnosis of coronary artery disease by medical personnel has so far been constrained by the limited number of doctors, in terms of the number of doctors and time, because the number of specialist doctors is limited. The limited number of doctors causes several difficulties for medical personnel who diagnose the patient's disease and over time can become a serious problem. Information technology that can help medical personnel is by applying data mining techniques which are techniques to help diagnose coronary artery disease. Data mining can identify patterns or relationships between disease symptoms and diagnostic results, so that patients with a high risk of developing the disease can be identified. The Naïve Bayes algorithm is one of the algorithms of the Data Mining classification technique, which is based on Bayes' theorem. The C4.5 algorithm is one of the algorithms of the Data Mining classification technique, which uses decision trees in classifying data. Algorithm comparisons are carried out in order to obtain the appropriate or best algorithm for use in diagnosing a disease. The comparison process of the Naïve Bayes algorithm and the C.45 algorithm in diagnosing coronary artery disease, obtained the best algorithm results based on the largest percentage value, namely the C4.5 algorithm, with a value of 46.9%.
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References
A. F. Riany and G. Testiana, “Penerapan Data Mining untuk Klasifikasi Penyakit Jantung Koroner Menggunakan Algoritma Naïve Bayes,” MDP Student Conf., vol. 2, no. 1, pp. 297–305, 2023, doi: 10.35957/mdp-sc.v2i1.4388.
S. Riska, J. I. Alham, E. Yosrita, and ; Rizqia Cahyaningtyas, “Sistem Diagnosis Penyakit Jantung Koroner Dengan Menggunakan Algoritma C4.5 Berbasis Website (Studi Kasus: RSUD Dr. Soedarso Pontianak),” PETIR J. Pengkaj. dan Penerapan Tek. Inform., vol. 14, no. 2, 2021, doi: 10.33322/petir.v14i2.1388.
H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, 2020, doi: 10.33096/ilkom.v12i2.507.81-86.
D. Derisma, “Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 84–88, 2020, doi: 10.30871/jaic.v4i1.2152.
M. Dewi, T. H. Saragih, and R. Herteno, “Penerapan SMOTE-NCL untuk Mengatasi Ketidakseimbangan Kelas pada Klasifikasi Penyakit Jantung Koroner,” J. Inform. Polinema, vol. 10, no. 1, pp. 27–34, 2023, doi: 10.33795/jip.v10i1.1394.
D. Sitanggang, N. Nicholas, V. Wilson, A. R. A. Sinaga, and A. D. Simanjuntak, “Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Menggunakan Metode K-Nearest Neighbor Dan Logistic Regression,” J. Tek. Inf. dan Komput., vol. 5, no. 2, p. 493, 2022, doi: 10.37600/tekinkom.v5i2.698.
Alvina Felicia Watratan, Arwini Puspita. B, and Dikwan Moeis, “Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia,” J. Appl. Comput. Sci. Technol., vol. 1, no. 1, pp. 7–14, 2020, doi: 10.52158/jacost.v1i1.9.
Syahril Dwi Prasetyo, Shofa Shofiah Hilabi, and Fitri Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN,” J. KomtekInfo, vol. 10, pp. 1–7, 2023, doi: 10.35134/komtekinfo.v10i1.330.
S. Ucha Putri, E. Irawan, and F. Rizky, “Implementasi Data Mining Untuk Prediksi Penyakit Diabetes Dengan Algoritma C4.5,” KESATRIA J. Penerapan Sist. Inf. (Komputer Manajemen), vol. 2, no. 1, pp. 39–46, 2021.
Y. S. Eirlangga and A. E. Syaputra, “Klasifikasi Penjurusan pada Sekolah Menengah Atas (SMA) dengan Metode Algoritma C4.5,” J. Inf. dan Teknol., vol. 4, no. 3, pp. 160–165, 2022, doi: 10.37034/jidt.v4i3.235.
Farmawati and Narti, “Perbandingan Algoritma C4.5 dan Naive Bayes dalam Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Daring,” JTIM J. Teknol. Inf. dan Multimed., vol. 4, no. 1, pp. 1–12, 2022.
U. K. Pemilihan Penggunaan Jenis Di Bidan Swasta HEnok Hayati Novia Cahya Utami, A. lia Hananto, and S. Shofiah Hilabi, “Komparasi Metode Klasifikasi Data Mining Algoritma C4.5 Dan Naïve Bayes,” J. Soc. Sci. Res., vol. 3, pp. 503–513, 2023.
N. Gusrialni Fitri, S. Adilya, and F. Azizi, “Comparison of the Naive Bayes Classification System and C4.5 for the Diagnosis of Stroke Perbandingan Sistem Klasifikasi Naive Bayes dan C4.5 Untuk Diagnosa Penyakit stoke,” SENTIMAS Semin. Nas. Penelit. dan Pengabdi. Masy., pp. 49–55, 2023.
O. Prasetia and S. Machfud, “Analisis Prediksi Penyakit Demensia Alzheimer Menggunskan Mrtode Decusion Tree C4.5 Dan Naive Bayes,” J. Inform. Utama, pp. 14–25, 2022.
M. F. Rizqullah, N. T. Raihana, and M. I. Jambak, “Komparasi Penerapan Algoritma C4.5, K-Nearest Neighbor, dan Naïve Bayes untuk Keberlangsungan Pasien Gagal Jantung,” Klik Kaji. Ilm. Inform. Dan Komput., vol. 4, no. 5, pp. 2580–2587, 2024, doi: 10.30865/klik.v4i5.1788.
A. Novriandy, “Implementasi Algoritma Naive Bayes dan Algoritma C4. 5 dalam Klasifikasi Kelayakan Bantuan UMKM,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 1, pp. 208–217, 2023, doi: 10.30865/klik.v4i1.1099.
I. Nurjanah, J. Karaman, I. Widaningrum, D. Mustikasari, and S. Sucipto, “Penggunaan Algoritma Naïve Bayes Untuk Menentukan Pemberian Kredit Pada Koperasi Desa,” Explorer (Hayward)., vol. 3, no. 2, pp. 77–87, 2023.
N. S. Fauziah and R. D. Dana, “Implementasi Algoritma Naive bayes dalam Klasifikasi Status Kesejahteraan Masyarakat Desa Gunungsari,” Blend Sains J. Tek., vol. 1, no. 4, pp. 295–305, 2023, doi: 10.56211/blendsains.v1i4.234.
C. Nas, “Data Mining Prediksi Minat Calon Mahasiswa Memilih Perguruan Tinggi Menggunakan Algoritma C4.5,” J. Manaj. Inform., vol. 11, no. 2, pp. 131–145, 2021, doi: 10.34010/jamika.v11i2.5506.
F. F. Nugraha, I. Sunandar, and C. Juliane, “Penerapan Data Mining Dengan Metode Klasifikasi Menggunakan Algoritma C4.5,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. March, pp. 10–20, 2022.
R. Girsang, E. F. Ginting, and M. Hutasuhut, “Penerapan Algoritma C4.5 Pada Penentuan Penerima Program Bantuan Pemerintah Daerah,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 1, no. 4, p. 449, 2022, doi: 10.53513/jursi.v1i4.5727.
E. I. Scandea, M. Aqsha, R. Sugiarto, F. Lestari, and D. Hartanti, “Penerapan Data Mining Untuk Menganalisis Data Faktor Resiko Penyakit Jantung Menggunakan Metode Logistic Regression,” in Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023, 2023, pp. 683–688.
D. A. M. Reza, A. M. Siregar, and Rahmat, “Penerapan Algoritma K-Nearest Neighbord Untuk Prediksi Kematian Akibat Penyakit Gagal Jantung,” Sci. Student J. Information, Technol. Sci. , vol. III, no. 1, pp. 105–112, 2022.
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