Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL TEKNIK INFORMATIKA DAN KOMPUTER
Lusia Leilu Kaka
Universitas Stella Maris Sumba
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
ANALISIS DAN EVALUASI PERFORMA METODE NAÏVE BAYES DAN LEXICON BASED UNTUK KLASIFIKASI SENTIMEN OPINI PUBLIK TERHADAP PASOLA WANUKAKA BERBASIS DATA YOUTUBE Lusia Leilu Kaka; Antar Maramba Jawa; Maria Wilda Malo
Jurnal Teknik Informatika dan Komputer Vol. 5 No. 2 (2026): Jurnal Teknik Informatika dan Komputer
Publisher : Universitas Muhammadiyah Prof. DR. HAMKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/jutikom.v5i2.24639

Abstract

YouTube is a widely used social media platform for expressing opinions on various issues, including local cultural events like Pasola Wanukaka. However, the diverse nature of comments makes manual sentiment identification inefficient, necessitating automated sentiment classification methods. This study aims to analyze public sentiment regarding Pasola Wanukaka and compare the performance of Lexicon-Based and Naïve Bayes methods in sentiment classification. The study utilized 601 YouTube comments collected via the YouTube Data API v3; following data cleaning, 464 comments remained for analysis. The research process involved preprocessing, sentiment labeling using the Lexicon-Based method, TF-IDF weighting, and classification using the Multinomial Naïve Bayes algorithm. The results indicate that positive sentiment was dominant, accounting for 211 comments (45.47%), followed by neutral sentiment with 175 comments (37.72%) and negative sentiment with 78 comments (16.81%). Evaluation results show that the Naïve Bayes method achieved an accuracy of 65.59%, outperforming the Lexicon-Based method, which achieved 60.99%. Thus, the Naïve Bayes method demonstrated superior performance in classifying the sentiment of YouTube comments regarding Pasola Wanukaka.
Co-Authors Antar Maramba Jawa Maria Wilda Malo