Analisis Metode Smoote pada Klasifikasi Penyakit Jantung Berbasis Random Forest Tree
DOI:
https://doi.org/10.30865/mib.v8i3.7712Keywords:
Decision Tree, Classification, Machine Learning, Tuning Parameters, PredictionAbstract
Cardiovascular disease is the number one cause of death globally. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels. At present, there are many predictive tools that use machine learning as a basis, including predictions on heart disease in particular. There are many methods in machine learning to predict heart disease, as well as many parameters to look for to find the highest level of accuracy. This study, aims to obtain the best methods and parameters for the classification of heart disease.References
R. Nurcahyo, A. Z. Fanani, A. Affandy, and M. I. Aziz, “Peningkatan Algoritma C4. 5 Berbasis PSO Pada Penyakit Kanker Payudara,†J. Media Inform. Budidarma, vol. 7, no. 4, pp. 1758–1765, 2023, doi: 10.30865/mib.v7i4.6841.
M. A. Al-Barrak and M. Al-Razgan, “Predicting Students Final GPA Using Decision Trees: A Case Study,†Int. J. Inf. Educ. Technol., vol. 6, no. 7, pp. 528–533, 2016, doi: 10.7763/ijiet.2016.v6.745.
P. Ananto, “Menentukan Kemungkinan Masuknya Calon Mahasiswa Baru Pada Sebuah Perguruan Tinggi Swasta Manggunakan Teknik Klasifikasi Pohon Keputusan Dengan Aplikasi RapidMiner 5.1,†J. Inform. Darmajaya, vol. 15, no. 1, pp. 45–55, 2015.
D. Mukhibah and A. Kurniawati, “Implementasi Data Mining Dalam Prediksi Performance Software Engineer Pt. Emerio Menggunakan Decision Tree the Implementation of Data Mining in Predicting the Performance Software Engineer At Pt Emerio Using Decision Tree,†J. Inform. dan Komput., vol. 22, no. 1, pp. 31–43, 2017.
P. Kasih, “Pemodelan Data Mining Decision Tree Dengan Classification Error Untuk Seleksi Calon Anggota Tim Paduan Suara,†Innov. Res. Informatics, vol. 1, no. 2, pp. 63–69, 2019, doi: 10.37058/innovatics.v1i2.918.
N. Yannuansa, M. Safa’udin, and M. I. Aziz, “Pemanfaatan Algoritma K-Means Clustering dalam Mengolah Pengaruh Hasil Belajar Terhadap Pendapatan Orang Tua Pada Mata Pelajaran Produktif,†J. Tecnoscienza, vol. 6, no. 1, pp. 43–55, 2021, doi: 10.51158/tecnoscienza.v6i1.530.
T. Novianti and I. Santosa, “PENENTUAN JADWAL KERJA BERDASARKAN KLASIFIKASI DATA KARYAWAN MENGGUNAKAN METODE DECISION TREE C4.5 (Studi Kasus Universitas Muhammadiyah Surabaya),†J. Komunika J. Komunikasi, Media dan Inform., vol. 5, no. 1, p. 1, 2016, doi: 10.31504/komunika.v5i1.633.
Y. BaÅŸtanlar and M. Ozuysal, Introduction to Machine Learning Second Edition, vol. 1107. 2014. doi: 10.1007/978-1-62703-748-8_7.
D. M. BrTarigan and D. P. Rini, “Samsuryadi,‘Seleksi Fitur pada Klasifikasi Penyakit Gula Darah Menggunakan Particle Swarm Optimization (PSO) pada Algoritma C4. 5,’ J,†RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 3, pp. 569–575, 2020.
M. I. Aziz, A. Z. Fanani, and A. Affandy, “Analisis Metode Ensemble Pada Klasifikasi Penyakit Jantung Berbasis Decision Tree,†J. Media Inform. Budidarma, vol. 7, no. 1, pp. 1–12, 2023, doi: 10.30865/mib.v7i1.5169.
A. Saifudin, “Metode data mining untuk seleksi calon mahasiswa pada penerimaan mahasiswa baru di Universitas Pamulang,†J. Teknol., vol. 10, no. 1, pp. 25–36, 2018.
A. Suryadi, “Sistem Pendukung Keputusan Seleksi Ujian Masuk Perguruan Tinggi Menggunakan Nbc (Naïve Bayes Classifier),†Kinetik, vol. 1, no. 3, p. 173, 2016, doi: 10.22219/kinetik.v1i3.120.
S. Wijayanto, D. Napitupulu, K. Adiyarta, and A. P. Windarto, “Decision Support System of New Student Admission Using Analytical Hierarchy Process and Simple Additive Weighting Methods,†J. Phys. Conf. Ser., vol. 1255, no. 1, 2019, doi: 10.1088/1742-6596/1255/1/012054.
F. Yunita, „Penerapan Data Mining Menggunkan Algoritma K-Means Clustring Pada Penerimaan Mahasiswa Baru (Studi Kasus : Universitas Islam Indragiri‟, Vol. 7, P. 12, 2018.
Y. Mardi, „Data Mining : Klasifikasi Menggunakan Algoritma C4.5‟, P. 7.
E. Alpaydin, Introduction To Machine Learning, 2nd Ed. Cambridge, Mass: MIT Press, 2010.
Fayyad, Usama. 1996. Advances in Knowledge Discovery and Data Mining. MIT Press.
L. Breiman, “Bagging Predictors,†Machine Learning, 24, 123-140.
Breiman, L., 2001. Random forests, Machine Learning, Kluwer Academic Publishers, 45, 5-32.
L. Breiman, “Bagging Predictors,†Machine Learning, 24, 123-140.
N. V Chawla, K. W. Bowyer, and L. O. Hall, “Handling Imbalance Data Prediksi Churn menggunakan metode SMOTE dan KNN Based on
Kernel,â€e-Proceeding of Engineering., vol. 4, no. 117, pp. 1-15, 2017.
R. Siringoringo, “Klasifikasi Data Tidak Seimbang Menggunakan Algoritma SMOTE dan K-Nearest Neighbor,†J. ISD, vol. 3, no. 1, pp. 44–49, 2018
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
