Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

Analisis Sentimen Pemindahan Ibu Kota Negara dengan Feature Selection Algoritma Naive Bayes dan Support Vector Machine

  • Faried Zamachsari STMIK Nusa Mandiri Jakarta
  • Gabriel Vangeran Saragih STMIK Nusa Mandiri Jakarta
  • Susafa'ati STMIK Nusa Mandiri Jakarta
  • Windu Gata STMIK Nusa Mandiri Jakarta
Keywords: naive bayes, support vector machine, feature selection, analisis sentimen, ibu kota baru

Abstract

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

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Published
2020-06-20
How to Cite
Zamachsari, F., Gabriel Vangeran Saragih, Susafa’ati, & Windu Gata. (2020). Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(3), 504 - 512. https://doi.org/10.29207/resti.v4i3.1942
Section
Artikel Teknologi Informasi

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