Analisis Kinerja Algoritma Decision Tree Dan Random Forest Dalam Klasifikasi Penyakit Kardiovaskular
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Nisa Utami
*
Universitas Buana Perjuangan Karawang,
Indonesia
- Kiki Ahmad Baihaqi Universitas Buana Perjuangan Karawang, Indonesia
- Elsa Elvira Awal Universitas Buana Perjuangan Karawang, Indonesia
- Deden Waiddin Universitas Buana Perjuangan Karawang, Indonesia (*) Corresponding Author
Abstract
Cardiovascular disease is a disease with a fairly high number of deaths. In Indonesia, the term cardiovascular is more popular with heart disease, which is a condition that can cause narrowing and blockage of blood vessels. Cardiovascular disease has two risks, the first is a risk that can be changed, such as stress, increased blood pressure, unhealthy diet, increased glucose levels, abnormal cholesterol and lack of physical activity. Meanwhile, risks that cannot be changed include family disease, gender, age and obesity. In this research, we can examine and analyze the performance of the two best classification algorithms, namely the decision tree algorithm and the random forest algorithm, in classifying cardiovascular disease based on the cause of the disease. The aspects studied are the performance results of each algorithm and evaluated using Area Under the Curve (AUC), classification report, k-Fold Cross Validation and Confusion matrix. The dataset used was taken from the Kaggle website with the data used being Cardiovascular Disease data which consists of 68.205 rows (patient data) and 17 attributes. . Based on the evaluation results using the Area Under The Curve (AUC) value, the highest result was obtained at 0.761 by the Random Forest algorithm with balanced data conditions with Random oversampling. Meanwhile, the lowest AUC value was obtained by the Decision Tree algorithm with unbalanced data of 0.592. Based on these results, it is known that the Random Forest algorithm with a balanced data scheme is a better algorithm, with a balanced data scenario using SMOTE and Random Oversampling techniques.
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References
E. Fauziah and A. Fikri Zulfikar, “OKTAL : Jurnal Ilmu Komputer dan Science Penerapan Metode Decision Tree Menggunakan Algoritma Iterative Dichotomiser 3 (ID3) Untuk Klasifikasi Resiko Penyakit Jantung,” Jurnal Ilmu Komputer dan Science, vol. 2, no. 4, pp. 1207–1219, 2023.
A. H. Yusufi, A. Kharisma, A. D. Adinata, D. F. Ramzy, and M. M. Santoni, “Prediksi Resiko Kematian Pada Penderita Penyakit Kadiovaskular Menggunakan Metode Ensemble Learning,” Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya, pp. 531–541, 2022.
W. Nugraha, “Prediksi Penyakit Jantung Cardiovascular Menggunakan Model Algoritma Klasifikasi,” Jurnal SIGMATA, vol. 9, no. 2, pp. 78–84, 2021, [Online]. Available: https://www.kaggle.com/andrewmvd/heart-
N. Fajriati, B. Prasetiyo, and P. Korespondensi, “Optimasi Algoritma Naïve Bayes Dengan Diskritisasi K-Means Pada Diagnosis Penyakit Jantung,” Jurnal Teknologi informasi dan Ilmu Komputer (JTIIK), vol. 10, no. 3, pp. 503–512, 2023, doi: 10.25126/jtiik.2023106510.
A. E. Cahyono, “Hipertensi Artikel Review,” Jurnal Perkembangan Ilmu Dan Praktek Kesehatan, vol. 2, no. 2, pp. 100–117, 2023.
A. Khoeruddin, F. Andriansyah Sudrajat, G. Purnama, I. Kuwangid, and R. Firmansyah, “Optimasi Fitur Seleksi Random Forest Menggunakan GA Dalam Klasifikasi Data Penyakit Gagal Jantung,” JPTIS : Jurnal Penelitian Teknologi Informasi Dan Sains, vol. 1, no. 2, pp. 1–09, 2023, doi: 10.54066/jptis.v1i2.323.
J. Dwi Muthohhar and A. Prihanto, “Analisis Perbandingan Algoritma Klasifikasi untuk Penyakit Jantung,” Journal of Informatics and Computer Science, vol. 04, no. 03, pp. 298–304, 2023.
& sriyanto khodijah, “Teknika 17 (2): 419-426 Perbandingan Kinerja Algoritma C4.5. Naive Bayes Dan Random Forest Dalam Prediksi Penyakit Jantung,” IJCCS, vol. 17, no. 2, pp. 419–426, 2023.
D. V. Ramadhanti, R. Santoso, and T. Widiharih, “Perbandingan SMOTE Dan ADASYN Pada Data Imbalance Untuk Klasifikasi Rumah Tangga Miskin Di Kabupaten Temanggung Dengan Algoritma K-Nearest Neighbor,” Jurnal Gaussian, vol. 11, no. 4, pp. 499–505, Feb. 2023, doi: 10.14710/j.gauss.11.4.499-505.
R. Arisandi, “PERBANDINGAN MODEL KLASIFIKASI RANDOM FOREST DENGAN RESAMPLING DAN TANPA RESAMPLING PADA PASIEN PENDERITA GAGAL JANTUNG,” Jurnal Gaussian, vol. 12, no. 1, pp. 136–145, May 2023, doi: 10.14710/j.gauss.12.1.136-145.
B. H. Agtira, H. H. Handayani, and A. F. N. Masruriyah, “Perbandingan Algoritma NBC dan Decision Tree pada Sentimen Analisis Mengenai Vaksinasi Covid-19 Di Indonesia,” remik, vol. 7, no. 1, pp. 704–712, Jan. 2023, doi: 10.33395/remik.v7i1.12151.
R. Annisa, “Analisis Komparasi Algoritma Klasifikasi Data Mining Untuk Prediksi Penderita Penyakit Jantung,” Jurnal Teknik Informatika Kaputama (JTIK), vol. 3, no. 1, 2019.
M. Mia, A. F. N. Masruriyah, and A. R. Pratama, “The Utilization of Decision Tree Algorithm In Order to Predict Heart Disease,” JURNAL SISFOTEK GLOBAL, vol. 12, no. 2, p. 138, Sep. 2022, doi: 10.38101/sisfotek.v12i2.551.
D. J. Muthohhar and A. Prihanto, “Analisis Perbandingan Algoritma Klasifikasi untuk Penyakit Jantung,” Journal of Informatics and Computer Science, vol. 04, no. 03, pp. 298–304, 2023.
Indarto, Ema Utami, and Suanto Raharjo, “Predikso Resiko Kematian Pasien Stroke Perdarahan Dengan Menggunakan Teknik Klasifikasi Data Mining,” Jurnal Informasi interaktif, vol. 5, no. 2, pp. 39–91, 2020.
V. Khoirunnisa and S. Lestari, “Implementasi Klasifikasi Kehamilan Beresiko Dengan Metode Naive Bayes Pada Puskesmas Kelurahan Malaka Jaya,” Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 4, no. 3, pp. 1680–1693, Sep. 2023, doi: 10.35870/jimik.v4i3.396.
M. Rizki, M. Fikri Hidayattullah, and Dwi Intan Af’idah, “Klasifikasi Opini Publik di Twitter Terhadap Bakal Calon Presiden Indonesia Tahun 2024 Menggunakan LSTM Secara Realtime Berbasis Website,” Infotekmesin, vol. 14, no. 2, pp. 285–295, Jul. 2023, doi: 10.35970/infotekmesin.v14i2.1908.
C. Fanny, A. Waworuntu, J. Christian, and J. C. Young, “Implementation of Conditional Random Field for Named Entity Recognition in Indonesian Traditional Arts Digital Article,” International Journal of Multidisciplinary Research and Publications (IJMRAP), vol. 5, no. 2, pp. 51–55, 2022.
Y. Umaidah, T. Informatika, F. Ilmu Komputer, and U. Singaperbangsa Karawang, “Penerapan Algoritma K-Nearest Neighbor (K-NN) Dengan Pencarian Optimal Untuk Prediksi Prestasi Siswa,” Jurnal Of Information System, Informatics and Computing, vol. 3, no. 2, pp. 1–8, 2019, [Online]. Available: http://journal.stmikjayakarta.ac.id/index.php/jisicomTelp.+62-21-3905050,
R. Ubaidillah, M. Muliadi, D. T. Nugrahadi, M. R. Faisal, and R. Herteno, “Implementasi XGBoost Pada Keseimbangan Liver Patient Dataset dengan SMOTE dan Hyperparameter Tuning Bayesian Search,” Jurnal Media Informatika Budidarma, vol. 6, no. 3, pp. 1723–1729, Jul. 2022, doi: 10.30865/mib.v6i3.414
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Pages: 970-980
Copyright (c) 2024 Nisa Utami, Kiki Ahmad Baihaqi, Elsa Elvira Awal, Deden Waiddin
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