Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbor (K-NN) Untuk Klasifikasi Penyakit Gagal Jantung
- Firman Zahri Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Fitri Insani
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Jasril Jasril Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Lola Oktavia Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
A condition known as heart failure, where the heart is unable to pump enough blood to meet the body's needs for oxygen and nutrients, should not be taken lightly. This can result in a number of symptoms, such as fatigue, fluid retention, and dyspnea. The World Heart Federation estimates that up to 1.8 million people in Southeast Asia suffered from heart failure in 2014. For prompt and efficient treatment, heart failure is a medical problem that needs to be identified. This disease has the potential to worsen if not treated immediately. Several machine learning methods can be used to help diagnose and categorize this disease. One of them is the popular algorithm, namely Naive Bayes and K-Nearest Neighbors. Naive Bayes is a simple but very efficient probability-based machine learning algorithm, especially in classification applications. K-Nearest Neighbors is comparing the data to be predicted with a number of its closest data in a feature space based on a certain distance, such as Euclidean distance, Manhattan, or others. This study was conducted using Confusion Matrix to evaluate and compare the Naive Bayes and K-Nearest Neighbor algorithms in the categorization of heart failure disease by collecting data totaling 918 heart failure patient data from kaggle. Based on the research findings, the K-Nearest Neighbor method achieved an accuracy score of 76%, while the Naive Bayes approach achieved 90% accuracy using a ratio of 80:20.
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References
F. Novaldy and A. Herliana, “Penerapan Pso Pada Naïve Bayes untuk Prediksi Harapan Hidup Pasien Gagal Jantung,” Jurnal Responsif, vol. 3, no. 1, pp. 37–43, 2021, [Online]. Available: https://doi.org/10.51977/jti.v3i1.396
D. Purnama Sari, M. Mustain, and M. Maksum, “Gambaran Pengelolaan Hipervolemia pada Gagal Jantung Kongestif di Rumah Sakit,” Jurnal Keperawatan Berbudaya Sehat, vol. 1, no. 1, pp. 9–15, Jan. 2023, doi: 10.35473/jkbs.v1i1.2155.
J. Triani, Y. Pratama, and E. Yanti, “Komparasi dalam Prediksi Gagal Jantung dengan Menggunakan Metode C4.5 dan Naïve Bayes,” JAKAKOM, 2023. [Online]. Available: http://ejournal.unama.ac.id/index.php/jakakom
G. Evelyn, R. Feradwiyanti, and R. Rismayanti, “Faktor – Faktor yang Mempengaruhi Kualitas Hidup Pasien Gagal Jantung Kronik Dirsud Karawang,” Jurnal Inovasi Penelitian, vol. 2, no. 2, pp. 775–784, Jul. 2021, doi: 10.47492/JIP.V2I2.2803.
A. Khoeruddin, F. Andriansyah Sudrajat, G. Purnama, I. Kuwangid, K. Kurnia, and R. Firmansyah, “Optimasi Fitur Seleksi Random Forest Menggunakan GA Dalam Klasifikasi Data Penyakit Gagal Jantung,” Jurnal Penelitian Teknologi Informasi dan Sains, vol. 1, no. 2, pp. 01–09, Jun. 2023, doi: https://doi.org/10.54066/jptis.v1i2.323
H. Nursita and A. Pratiwi, “Peningkatan Kualitas Hidup pada Pasien Gagal Jantung: A Narrative Review Article (Improved Quality of Life in Heart Failure Patients: A Narrative Review Article),” Jurnal Berita Ilmu Keperawatan, vol. 13, no. 1, pp. 10–21, Jan. 2020, doi: 10.23917/bik.v13i1.11916.
P. Aisyiyah and R. Devi, “Klasifikasi Penyakit Gagal Ginjal Kronis dengan Metode Knn (Studi Kasus RS di Kab Gresik),” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 3, pp. 1739–1748, Sep. 2024, doi: 10.29100/jipi.v9i3.6226.
J. Homepage, B. Delvika, S. Nurhidayarnis, P. D. Rinada, N. Abror, and A. Hidayat, “Perbandingan Klasifikasi Antara Naive Bayes dan K-Nearest Neighbor Terhadap Resiko Diabetes Pada Ibu Hamil,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 2, pp. 68–75, 2022, doi: 10.23917/bik.v13i1.11916.
F. Kana, M. Ramadhan, and R. Mahyuni, “Implementasi Data Mining Menganalisa Pola Penjualan Rempah-Rempah Menggunakan Metode Fp-Growth,” Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), vol. 1, no. 4, p. 557, Jul. 2022, doi: 10.53513/jursi.v1i4.5586.
M. Y. Putra and D. I. Putri, “Pemanfaatan Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Klasifikasi Jurusan Siswa Kelas XI,” Jurnal Tekno Kompak, vol. 16, no. 2, p. 176, Aug. 2022, doi: 10.33365/jtk.v16i2.2002.
S. Bahri, D. Marisa Midyanti, R. Hidayati, J. Sistem Komputer, and F. Mipa, “Perbandingan Algoritma Naive Bayes dan C4.5 Untuk Klasifikasi Penyakit Anak,” Yogyakarta, Aug. 2018. Accessed: Dec. 31, 2024. [Online]. Available: https://journal.uii.ac.id/Snati/article/view/11152
K. Abdul Khalim, U. Hayati, and A. Bahtiar, “Perbandingan Prediksi Penyakit Hipertensi Menggunakan Metode Random Forest dan Naïve Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 1, pp. 498–504, Mar. 2023, doi: 10.36040/jati.v7i1.6376.
I. Lishania, R. Goejantoro, and Y. N. Nasution, “Perbandingan Klasifikasi Metode Naive Bayes dan Metode Decision Tree Algoritma (J48) pada Pasien Penderita Penyakit Stroke di RSUD Abdul Wahab Sjahranie Samarinda Hospital,” Jurnal EKSPONENSIAL, vol. 10, no. 2, Nov 2019. Tersedia pada: https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/571
A. R. Oktavyani et al., “Sistem Informasi, dan Teknik Informatika Perbandingan Metode Naive Bayes, K-NN dan Decision Tree Terhadap Dataset Healthcare Stroke,” SNESTIK Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika, pp. 276–281, 2023, doi: 10.31284/p.snestik.2023.4067.
I. L. F. Amien, W. Astuti, and K. M. Lhaksamana, “Perbandingan Metode Naïve Bayes dan KNN (K-Nearest Neighbor) dalam Klasifikasi Penyakit Diabetes,” eProceedings of Engineering, vol. 10, no. 2, May 2023, Accessed: Dec. 11, 2024. [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/20039
M. Bansal, A. Goyal, and A. Choudhary, “A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning,” Decision Analytics Journal, vol. 3, p. 100071, Jun. 2022, doi: 10.1016/j.dajour.2022.100071.
D. Ulfatul, M. Rachmad, H. Oktavianto, and M. Rahman, “Perbandingan Metode K-Nearest Neighbor Dan Gaussian Naive Bayes Untuk Klasifikasi Penyakit Stroke Comparison Of K-Nearest Neighbor And Gaussian Naive Bayes Methods For Stroke Disease Classification,” Jurnal Smart Teknologi, vol. 3, no 4, pp. 405-412, Mei. 2022. [Online]. Available: http://jurnal.unmuhjember.ac.id/index.php/JST/article/view/7601
F. Sholekhah, A. D. Putri, R. Rahmaddeni, and L. Efrizoni, “Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbors untuk Klasifikasi Metabolik Sindrom,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 2, pp. 507–514, Feb. 2024, doi: 10.57152/malcom.v4i2.1249.
A. Desiani, “Perbandingan Implementasi Algoritma Naïve Bayes dan K-Nearest Neighbor Pada Klasifikasi Penyakit Hati,” SIMKOM, vol. 7, no. 2, pp. 104–110, Jul. 2022, doi: 10.51717/simkom.v7i2.96.
Dewi Nasien et al., “Perbandingan Implementasi Machine Learning Menggunakan Metode KNN, Naive Bayes, dan Logistik Regression Untuk Mengklasifikasi Penyakit Diabetes,” JEKIN - Jurnal Teknik Informatika, vol. 4, no. 1, pp. 10–17, Feb. 2024, doi: 10.58794/jekin.v4i1.640.
A. Damuri, U. Riyanto, H. Rusdianto, and M. Aminudin, “Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako,” Jurnal Riset Komputer), vol. 8, no. 6, pp. 2407–389, 2021, doi: 10.30865/jurikom.v8i6.3655.
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