KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG

  • Hendri Mahmud Nawawi STMIK Nusa Mandiri
  • Jajang Jaya Purnama Ilmu Komputer STMIK Nusa Mandiri
  • Agung Baitul Hikmah Sistem Informasi Universitas Bina Sarana Informatika
Keywords: Heart Disease, Neural Network Algorithm, Naïve Bayes Algorithm

Abstract

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer.  Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body.  By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not.

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References

dedaunan.com. (2019). Ciri-Ciri Penyakit Jantung yang Wajib Anda Ketahui. Retrieved from https://dedaunan.com/ciri-ciri-penyakit-jantung-yang-wajib-anda-ketahui/

Effendy, N., Subagja, & Faisal, A. (2008). Prediksi penyakit jantung koroner ( PJK ) berdasarkan faktor risiko menggunakan jaringan syaraf tiruan backpropagation. Seminar Nasional Aplikasi Teknologi Informasi 2008 (SNATI 2008), (January), E19–E24.

Habibi, M. Y. (Institut S. M., & Riksakomara, E. I. S. M. (2017). Peramalan Harga Garam Konsumsi Menggunakan Artificial Neural Network Feedforward-Backpropagation ( Studi Kasus : 6(2), A306–A310.

Kusumodestoni, R. H., & Zyen, A. K. (2015). PREDIKSI KECEPATAN ANGIN MENGGUNAKAN MODEL NEURAL NETWORK UNTUK MENGHETAHUI BESAR DAYA LISTRIK YANG DIHASILKAN. Jurnal DISPROTEK, 6(1), 53–39.

Lestari, M. E. I. (2014). PENERAPAN ALGORITMA KLASIFIKASI NEAREST NEIGHBOR ( K-NN ) UNTUK MENDETEKSI PENYAKIT JANTUNG. Faktor Exacta, 7(September 2010), 366–371.

Muhamad, H., Prasojo, C. A., Sugianto, N. A., Surtiningsih, L., Cholissodin, I., Ilmu, F., … Optimization, P. S. (2017). OPTIMASI NAÏVE BAYES CLASSIFIER DENGAN MENGGUNAKAN PARTICLE. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 4(3), 180–184.

Nawawi, H. M., Purnama, J. J., & Hikmah, A. B. (2019). KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG. Jurnal PILAR Nusa Mandiri.

Supriyatna, A., & Mustika, W. P. (2018). Komparasi Algoritma Naive bayes dan SVM Untuk Memprediksi Keberhasilan Imunoterapi Pada Penyakit Kutil. Jurnal Sains Komputer & Informatika (J-SAKTI), Volume (2)(2), 152–161.

Widiastuti, N. A., Santosa, S., & Supriyanto, C. (2014). ALGORITMA KLASIFIKASI DATA MINING NAÏVE BAYES BERBASIS PARTICLE SWARM. Jurnal Pseudocode, Volme 1 No, 11–14.

Yunita. (2015). Prediksi Cuaca Menggunakan Metode Neural Network. PARADIGMA VOL. XVII NO. 2 MARET 2015, XVII(2), 47–53.

Published
2019-09-05
How to Cite
Nawawi, H., Purnama, J., & Hikmah, A. (2019). KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG. Jurnal Pilar Nusa Mandiri, 15(2), 189-194. https://doi.org/10.33480/pilar.v15i2.669