Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

Komparasi Algoritma Klasifikasi Berbasis Particle Swarm Optimization pada Analisis Sentimen Ekspedisi Barang

  • Sharazita Dyah Anggita Universitas AMIKOM Yogyakarta
  • Ikmah Universitas Amikom Yogyakarta
Keywords: Sentiment, Naïve Bayes, SVM, PSO

Abstract

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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Published
2020-04-20
How to Cite
Sharazita Dyah Anggita, & Ikmah. (2020). Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(2), 362 - 369. https://doi.org/10.29207/resti.v4i2.1840
Section
Artikel Rekayasa Sistem Informasi