Implementasi Algoritma Logistic Regression pada Pembuatan Website Sederhana untuk Prediksi Penyakit Jantung
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Abstract
Heart disease is a deadly disease, early recognition is important to prevent the fairly high death rate due to this disease. There are various ways to detect heart disease early, one of which is by utilizing machine learning. In this research, the author uses secondary data, namely data taken from the website www.kaggle.com for the prediction process. The amount of data used was 297 data, with details of 160 data not detecting heart disease, and 137 data detecting heart disease. Apart from making predictions from heart disease patient data using the logistic regression algorithm, this research also implements the model that has been created on the website. The results of implementing the logistic regression algorithm in this research are an accuracy value of 0.9, precision of 0.92, recall of 0.86, and f1-score of 0.89. After measuring using these 4 parameters, the model that has been created is then implemented into a simple website using the Rapid Application Development (RAD) method.
References
A. Purnama, “Edukasi dapat meningkatkan kualitas hidup pasien yang terdiagnosa penyakit jantung koroner,” J. Kesehat. Indones., vol. X, no. 2, pp. 66–71, 2020.
E. M. Marwali, Y. Purnama, and P. S. Roebiono, “Modalitas Deteksi Dini Penyakit Jantung Bawaan di Pelayanan Kesehatan Primer,” J. Indones. Med. Assoc., vol. 71, no. 2, pp. 100–109, 2021, doi: 10.47830/jinma-vol.71.2-2021-241.
A. B. Wibisono and A. Fahrurozi, “Perbandingan Algoritma Klasifikasi Dalam Pengklasifikasian Data Penyakit Jantung Koroner,” J. Ilm. Teknol. dan Rekayasa, vol. 24, no. 3, pp. 161–170, 2019, doi: 10.35760/tr.2019.v24i3.2393.
F. Handayani, “Komparasi Support Vector Machine, Logistic Regression Dan Artificial Neural Network Dalam Prediksi Penyakit Jantung,” J. Edukasi dan Penelit. Inform., vol. 7, no. 3, p. 329, 2021, doi: 10.26418/jp.v7i3.48053.
Balakrishnan, M., Arockia Christopher, A. B., Ramprakash, P., & Logeswari, A. (2021). Prediction of Cardiovascular Disease using Machine Learning. Journal of Physics: Conference Series, 1767(1). https://doi.org/10.1088/1742-6596/1767/1/012013
J. J. Pangaribuan, H. Tanjaya, and Kenichi, “Mendeteksi Penyakit Jantung Menggunakan Mechine Learning Dengan Algoritma Logistic Regression,” Mach. Learn., vol. 45, no. 13, pp. 40–48, 2021.
M. Balakrishnan, A. B. Arockia Christopher, P. Ramprakash, and A. Logeswari, “Prediction of Cardiovascular Disease using Machine Learning,” J. Phys. Conf. Ser., vol. 1767, no. 1, 2021, doi: 10.1088/1742-6596/1767/1/012013.
P. S. Hasugian, “Perancangan Website Sebagai Media Promosi Dan Informasi,” J. Inform. Pelita Nusant., vol. 3, no. 1, pp. 82–86, 2018.
Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707
Derisma, D. (2020). Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining. Journal of Applied Informatics and Computing, 4(1), 84–88. https://doi.org/10.30871/jaic.v4i1.2152
Terrada, O., Cherradi, B., Raihani, A., & Bouattane, O. (2019). Classification and Prediction of atherosclerosis diseases using machine learning algorithms. 2019 International Conference on Optimization and Applications, ICOA 2019, April, 1–5. https://doi.org/10.1109/ICOA.2019.8727688
Santoso, B. (2019). An Analysis of Spam Email Detection Performance Assessment Using Machine Learning. Jurnal Online Informatika, 4(1), 53. https://doi.org/10.15575/join.v4i1.298
N. Hidayat and K. Hati, “Penerapan Metode Rapid Application Development (RAD) dalam Rancang Bangun Sistem Informasi Rapor Online (SIRALINE),” J. Sist. Inf., vol. 10, no. 1, pp. 8–17, 2021, doi: 10.51998/jsi.v10i1.352.
T. Pricillia and Zulfachmi, “Perbandingan Metode Pengembangan Perangkat Lunak (Waterfall, Prototype, RAD),” J. Bangkit Indones., vol. 10, no. 1, pp. 6–12, 2021, doi: 10.52771/bangkitindonesia.v10i1.153.
Cherngs, “Heart Disease Cleveland UCI,” Kaggle, 2019. https://www.kaggle.com/datasets/cherngs/heart-disease-cleveland-uci.
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