Open AccessPerbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek
Author(s) -
Jessica Widyadhana Iskandar,
Yessica Nataliani
Publication year - 2021
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v5i6.3588
Subject(s) - gadget , support vector machine , computer science , machine learning , generative grammar , bayes' theorem , social media , artificial intelligence , world wide web , algorithm , bayesian probability
The Samsung Galaxy Z Flip 3 is one of the gadgets that are currently popular among the public because of its unique shape and features. Youtube is one of the social media that can be accessed and enjoyed by the public, one of which is gadget review content on the GadgetIn channel. Youtube can provide information, whether people accept or are interested in this new gadget or not. This study aims to determine the sentiment of a gadget producer. Based on the results of the analysis and testing that has been carried out on the Youtube comments of the Samsung Galaxy Z Flip 3 gadget with a total of 9,597 comments, more users gave positive opinions in the design aspect and negative opinions on the price, specifications and brand image aspects. By using the CRISP-DM model and comparing the Naïve Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) classification methods, it is proven that the SVM classification model shows the best results. The average accuracy of SVM is 96.43% seen from four aspects, namely the design aspect of 94.40%, the price aspect of 97.44%, the specification aspect of 96.22%, and the brand image aspect of 97.63%.