SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

  • Heru Sukma Utama PASCA SARJANA STMIK NUSA MANDIRI
  • Didi Rosiyadi Ilmu Komputer STMIK Nusa Mandiri
  • Dedi Aridarma Ilmu Komputer STMIK Nusa Mandiri
  • Bobby Suryo Prakoso Ilmu Komputer STMIK Nusa Mandiri
DOI: https://doi.org/10.33480/pilar.v15i2.705
Keywords: Naive Bayes, Text Mining, Kebijakan Ganjil Genap

Abstract

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

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Published
2019-09-05
How to Cite
Utama, H., Rosiyadi, D., Aridarma, D., & Prakoso, B. (2019). SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN. Jurnal Pilar Nusa Mandiri, 15(2), 247-254. https://doi.org/10.33480/pilar.v15i2.705
Issue
Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode September 2019
Section
Articles

Copyright (c) 2019 Heru Sukma Utama, Didi Rosiyadi, Dedi Aridarma, Bobby Suryo Prakoso

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