A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App

(1) * Kiki Ahmad Baihaqi Mail (Universitas Kristen Satya Wacana, Indonesia)
(2) Iwan Setyawan Mail (Universitas Kristen Satya Wacana, Indonesia)
(3) Danny Manongga Mail (Universitas Kristen Satya Wacana, Indonesia)
(4) Hendryanto Dwi Purnomo Mail (Universitas Kristen Satya Wacana, Indonesia)
(5) Hendry Hendry Mail (Universitas Kristen Satya Wacana, Indonesia)
(6) Ahmad Fauzi Mail (Universitas Buana Perjuangan Karawang, Indonesia)
(7) Aprilia Hananto Mail (Universitas Buana Perjuangan Karawang, Indonesia)
*corresponding author

Abstract


Data is the most important thing, the use of data can be useful to get an evaluation from the user of a system or application that is built based on mobile. Not only, the assessment or acceptance results of mobile applications during the trial stage are considered important, assessments and comments from direct users are also important things that can be input for mobile application developers. Data mining, or known in English as data mining, is the answer to the process of retrieving data on any media. In this research, data mining is carried out on the media mobile application download service provider Google Playstore, which provides data in the form of comments and ratings. After scraping the data and obtaining the latest data parameters determined by the latest 2000 comments, the data is pre-processed by removing the emot icon character and eliminating unneeded variables so that the data obtained can be processed to the next stage, namely classification based on ratings and sentiment comments. The algorithms used or compared in this research are Support Vector machine, logistic regression and naïve bayes which are known to be reliable in data mining processing. In this research, the accuracy results are 88% for SVM, 90.5% for Logistic Regression and 91% for naïve bayes.


Keywords


Data Mining Scraping Naïve Bayes Support Verctor Machine Logistic Regression

   

DOI

https://doi.org/10.29099/ijair.v7i1.962
      

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