Article Per Year (5 Year)
p-Index From 2021 - 2026
1.978
P-Index
This Author published in this journals
All Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Cerdas EDUMATIC: Jurnal Pendidikan Informatika Journal of Applied Computer Science and Technology (JACOST) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Journal of Computer and Information System (J-CIS) Jurnal Minfo Polgan (JMP) Jurnal Informatika Teknologi dan Sains (Jinteks) Inspiration: Jurnal Teknologi Informasi dan Komunikasi Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Chairi Nur Insani
Universitas Sulawesi Barat, Majene, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Library & Information Science Mathematics Social Sciences

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Implementation of Random Forest for Sentiment Analysis of YouTube Comments Related To The TNI Law Revision virna afrilianty virna; Chairi Nur Insani; Nurhikma Arifin; Muhammad Furqan Rasyid
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Social media platforms such as YouTube have become important spaces for the public to express opinions on various policy issues, including the Draft Bill on the Indonesian National Armed Forces (RUU TNI). However, research on sentiment analysis of YouTube comments remains limited, particularly in the application of multi-class classification using the Random Forest algorithm. This study aims to implement Random Forest for classifying sentiments in YouTube comments related to the RUU TNI into three categories: positive, negative, and neutral. The dataset consists of 7118 comments, divided into 5694 training data and 1423 testing data. Sentiment labeling was conducted using a lexicon-based approach, while text representation was carried out using TF-IDF. To address data imbalance, class weighting was applied, and model parameter optimization was performed using the GridSearchCV technique. The optimal parameter combination obtained was n_estimators=300, max_depth=None, max_features='log2', and min_samples_split=20. The evaluation results show that the model with class weighting achieved an accuracy of 80.27%, while the model without weighting achieved 79.42%. These findings indicate that applying class weighting and parameter optimization effectively improves sentiment classification performance of public opinions on the RUU TNI policy on YouTube.
Co-Authors A.Amirul Asnan Cirua Abd Salam At Taqwa Adventus Angga Kurniawan Alim Rabbani Anhar Anhar Arifin, Nurhikma Dian Megah Sari Farid Wajidi Hasnawati Iin Indriani Indra Irmayanti Mawardi Kudin Muh Rafli Rasyid Muh. Fuad Mansyur Muhammad Furqan Rasyid Nurani Natsir Rasyid, Muhammad Rafli Safitri Safitri Siti Aulia Rachmini Sitti Aulia Rachmini Sulpiana virna afrilianty virna