Comparison of Data Mining Methods Using the Naïve Bayes Algorithm and K-Nearest Neighbor in Predicting Immunotherapy Success

Authors

  • Budi Harto Universita Buddhi Dharma
  • Rino Rino Universitas Buddhi Dharma

DOI:

https://doi.org/10.31253/te.v2i2.139

Keywords:

Immunotherapy, Data Mining, K-NN, Naïve Bayes, Expert System, machine learning

Abstract

tumor or cancer is a disease that is a problem for people who are increasing every year. This disease in both the early and final stages requires attention because in this disease sufferers have a large risk of death. along with the rapid development of technology, we can use the technology to facilitate in all fields one of which is to predict success in a therapy. Data mining is one of the techniques used by the author in testing the dataset used in this study to get the best algorithm between Naïve Bayes and the K-Nearest Neighbor algorithm by using the Rapid Miner S

tudio application and applying the best algorithm into the expected application or expert system. can help users predict the success of a therapy.

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Published

2019-02-20

Issue

Section

Articles