JTIM : Jurnal Teknologi Informasi dan Multimedia
Vol 3 No 4 (2022): February

Analisis Sentimen Pada Agen Perjalanan Online Menggunakan Naïve Bayes dan K-Nearest Neighbor

Eka Wahyu Sholeha (Politeknik Tanah Laut)
Selviana Yunita (Universitas Darwan Ali)
Rifqi Hammad (Universitas Bumigora)
Veny Cahya Hardita (STMIK Palangka Raya)
Kaharuddin Kaharuddin (Universitas Universal)

Article Info

Publish Date
25 Jan 2022


Social media has impact for decision maker to get more insights broadly. Including for online travel agent company, where costumer’s interest to use online travel agent for their chosen agent will grows along with the high number of customer’s satisfaction. As a one of the most important point in distribution, company provides a platform that reliable and effective to purchase a trip and share information of their experience through Online travel agent. It is important to know how consumer considerate which one the online travel agent they choose. One of their method is looking at the reviews. Facebook is one of social media that provide numerous reviews through comments sections. The research purposes are twofold, algorithm comparison and reveal the effect of uppercase as well as punctuation mark. The accuracy comparison between Naïve Bayes and K-Nearest Neighbor is provided against the datasets. This research collects the data from user comments on Facebook about the biggest three online travel agents in Indonesia. We classify the comments into three categories which are positive, negative, and neutral. The result of this research is found that K-Nearest Neighbor have slightly higher accuracy than the Naïve Bayes. Moreover, lowercase text without punctuation achieves better accuracy for both of algorithm.

Copyrights © 2022

Journal Info





Computer Science & IT


Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, ...