Klasifikasi Penyakit Hati dengan Menggunakan Metode Naive Bayes
Main Article Content
Abstract
Data mining is the process of finding relationships in data that are not known to the user and understandably presenting them so that these relationships can form the basis of decision-making. The purpose of this study was to design a data mining website using the Naive Bayes method to classify liver disease symptoms and to test a data mining website using the Naive Bayes method to assist in classifying liver disease. The data was processed by 50 medical records of liver disease in Lubuk Basung Hospital which consisted of symptoms and diseases. The method used is the Naive Bayes method which has a fairly high accuracy value. one method that can be used to classify data. Bayesian classification is a statistical classifier that can be used to predict the probability of belonging to a class. Then it was developed into a website-based form using the PHP Framework Codeigniter programming language and MySQL as the database. The system to be built is based on the Naive Bayes method which is one of the Data Mining methods. The test results from 50 medical record data of patients with liver disease, by applying the Naive Bayes method which is applied to data mining, is useful for comparing hospital data with system data. From 45 training data and 5 testing data, the accuracy rate is 60%.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Takdirillah, R. (2020). Penerapan Data Mining Menggunakan Algoritma Apriori Terhadap Data Transaksi Penjualan Bisnis Ritel. Edumatic: Jurnal Pendidikan Informatika, 4(1), 37-46. DOI: https://doi.org/10.29408/edumatic.v4i1.2081
Khoirunnisa, N., Nugroho, I. M. R., & Muhyidin, Y. (2022). Penerapan data mining dengan metode naïve bayes untuk memprediksi calon terjamin potensial. Journal of Information System, Applied, Management, Accounting and Research, 6(1), 149-161. DOI: https://doi.org/10.52362/jisamar.v6i1.684
Br. Saragih, N. (2022). Sistem Pakar Mendiagnosa Penyakit Gangguan Hati Pada Manusia Menggunakan Metode Naïve Bayes Berbasis WEB. Jurnal Ilmu Komputer Dan Sistem Informasi (JIKOMSI), 5(1), 11–19. DOI: https://doi.org/10.55338/jikomsi.v5i1.202
Irmawati, I., Widianto, K., Aziz, F., Rifai, A., & Rahmawati, A. (2022). Implementasi artificial neural network dalam mendeteksi penyakit hati (liver). Journal of Information System, Applied, Management, Accounting and Research, 6(1), 193-198. DOI: https://doi.org/10.52362/jisamar.v6i1.694
Setiawan, S. I., & Kurniawan, J. (2021). Pilihan Tatalaksana Penyakit Perlemakan Hati Non-Alkohol (Non-Alcoholic Fatty Liver Disease/NAFLD). Cermin Dunia Kedokteran, 48(3), 173-175. DOI: http://dx.doi.org/10.55175/cdk.v48i3.1336
D Damuri, A., Riyanto, U., Rusdianto, H., & Aminudin, M. (2021). Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako. JURIKOM (Jurnal Riset Komputer), 8(6), 219-225. DOI: http://dx.doi.org/10.30865/jurikom.v8i6.3655
Silahudin, D., Henderi, & Holidin, A. (2020). Model expert system for diagnosis of COVID-19 using naïve bayes classifier. IOP Conference Series: Materials Science and Engineering, 1007(1). DOI: https://doi.org/10.1088/1757-899X/1007/1/012067
Doni, B. T. R., Susanti, S., & Mubarok, A. (2021). Penerapan Data Mining Untuk Klasifikasi Penyakit Hepatocellular Carcinoma Menggunakan Algoritma Naïve Bayes. Jurnal Responsif: Riset Sains dan Informatika, 3(1), 12-19. https://doi.org/10.51977/jti.v3i1.403
Zohra, F. T. (2020). Prediction of Different Diseases and Development of a Clinical Decision Support System using Naïve Bayes Classifier. International Journal for Research in Applied Science and Engineering Technology, 8(5), 8–13. http://doi.org/10.22214/ijraset.2020.5002
Irmayani, W. (2021). VISUALISASI DATA PADA DATA MINING MENGGUNAKAN METODE KLASIFIKASI NAÏVE BAYES. Jurnal Khatulistiwa Informatika, 9(1). DOI: https://doi.org/10.31294/jki.v9i1.9593
Indraputra, R. A., & Fitriana, R. (2020). K-Means Clustering Data COVID-19. Jurnal Teknik Industri, 10(3), 275-282. DOI: https://doi.org/10.25105/jti.v10i3.8428
Titimeidara, M. Y., & Hadikurniawati, W. (2021). Implementasi Metode Naive Bayes Classifier Untuk Klasifikasi Status Gizi Stunting Pada Balita. JURNAL ILMIAH INFORMATIKA, 9(01), 54-59. DOI: https://doi.org/10.33884/jif.v9i01.3741
Alfiah, N. (2021). Klasifikasi Penerima Bantuan Sosial Program Keluarga Harapan Menggunakan Metode Naive Bayes. Respati, 16(1), 32-40. DOI: https://doi.org/10.35842/jtir.v16i1.386
Dwiramadhan, F., Wahyuddin, M. I., & Hidayatullah, D. (2022). Sistem Pakar Diagnosa Penyakit Kulit Kucing Menggunakan Metode Naive Bayes Berbasis Web. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 6(3), 429–437. https://doi:10.35870/jtik.v6i3.466
Putra, D. W. T., Utami, A. O., Minarni, & Swara, G. Y. (2019). Accuracy Level of Diagnosis of ENT Diseases in Expert System. Jurnal KomtekInfo, 6(2), 127–134. https://doi:10.35134/komtekinfo.v6i2.51