DOI : https://doi.org/10.62527/ijasce.1.1.10

A Study on Dengue Cases Detection based on Lazy Classifier

Nur Amiratun Nazihah Roslan (1), Hairulnizam Mahdin (2), Rahmat Hidayat (3), Hendrick (4)
(1) Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
(2) Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
(3) Department of Information Technology, Politeknik Negeri Padang, Indonesia
(4) National Kaohsiung University of Science and Technology, Taiwan
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How to cite (IJASEIT) :
Roslan, N. A. N., Mahdin, H., Hidayat, R., & Hendrick. (2019). A Study on Dengue Cases Detection based on Lazy Classifier. International Journal of Advanced Science Computing and Engineering, 1(1), 43–47. https://doi.org/10.62527/ijasce.1.1.10
Citation Format :
With the rise of social networking approach, there has been a surge of users generated content all over the world and with that in an era where technology advancement are up to the level where it could put us in a step ahead of pathogens and germination of diseases, we couldn’t help but to take advantage of that advancement and provide an early precaution measures to overcome it. Twitter on the other hand are one of the social media platform that provides access to a huge data availability. To manipulate those data and transform it into an important information that could be used in many different scopes that could help improve people’s lives for the better. In this paper, we gather a total of six algorithms from Lazy Classifier to compare between them on which algorithm suited the most with the diabetes dataset. This research are using WEKA as the data mining tool for data analyzationÂ