Analisis Sentimen Tiktok: Wajib Militer dengan Metode Lexicon Based dan Naive Bayes Classifier

Authors

  • Arpan Mualief Saprizal Universitas Sari Mulia
  • Nor Anisa Universitas Sari Mulia

DOI:

https://doi.org/10.46880/tamika.Vol4No2.pp242-246

Keywords:

Military Conscription, Sentiment-Analysis, Lexion-Based, Naive Bayes Classifier, Wordcloud

Abstract

The issue of conscription in Indonesia has sparked a heated debate among the public, especially on the social media platform TikTok. This study aims to analyze public sentiment on the issue through analysis of TikTok user comments. The method used is lexicon-based sentiment analysis. Data of 5,212 comments were collected using web scraping techniques with the keyword "conscription in Indonesia". The results of the analysis showed that the majority of comments (53.28%) were positive, followed by neutral comments (35.79%), and negative comments (10.92%). This finding indicates that there is considerable support for the issue of military service among TikTok users. The research process includes data collection, data processing, sentiment analysis using a lexicon-based approach, and visualization of results. The results of this study are expected to provide a clearer picture of public perception of the issue of military conscription in Indonesia. 

Published

2024-12-31

Issue

Section

TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi