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Abstract
Nowadays, the flow of information has experienced a significant increase every day, which results in the accumulation of data in the form of text documents both online and offline. The classification of Dengue Fever data in the medical field is an essential task in predicting the disease, it can even support doctors in establishing a diagnosis, so it is important to make a diagnosis quickly in order to reduce the risk of Dengue Fever spreading in the community. The classification of dengue fever level using Naïve Bayes Classifier for early detection is the Naïve Bayes Algorithm can be used to classify the level of DD (Dengue Fever), and DBD1 (Dengue Hemorrhagic Fever Level 1), DBD2 (Dengue Hemorrhagic Fever Level 2), DBD3 (Hemorrhagic Fever) Level 3), Dengue4 (Hemorrhagic Fever Level 4) for early detection, which is taken from the result of the largest Naïve Bayes probability value. In testing the Naïve Bayes method using test data as many as 60 data.
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References
- D.Moore, A. (2018) Python GUI Programming with Tkinter. mumbai.
- Ginanjar, G. (2014) Demam Berdarah.
- Patil, S. and Lokesha, V. (2022) ‘Live Twitter Sentiment Analysis Using Streamlit Framework’, SSRN Electronic Journal [Preprint]. Available at: https://doi.org/10.2139/ssrn.4119949.
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References
D.Moore, A. (2018) Python GUI Programming with Tkinter. mumbai.
Ginanjar, G. (2014) Demam Berdarah.
Patil, S. and Lokesha, V. (2022) ‘Live Twitter Sentiment Analysis Using Streamlit Framework’, SSRN Electronic Journal [Preprint]. Available at: https://doi.org/10.2139/ssrn.4119949.
Redhani, A.W. and Hidayat, N. (2021) ‘Implementasi Metode Naïve Bayes untuk Diagnosa Pengidap Demam Berdarah pada Kelurahan Antasan Besar berbasis Web’, 5(12), pp. 5320–5328.
Watratan, A.F. et al. (2020) ‘JOURNAL OF APPLIED COMPUTER SCIENCE AND TECHNOLOGY ( JACOST ) Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia’, 1(1), pp. 7–14.