Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Teknika
Endang Lestari Ruskan
Computer System, Faculty of Computer, Sriwijaya University, Palembang, South Sumatera, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering

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

Found 1 Documents
Search

 1 
Implementation of Random Forest Algorithm with Random Oversampling for Sentiment Analysis of X Users Toward the Sekolah Rakyat Program Deni Agus Hendrawan; Ali Ibrahim; Yadi Utama; Endang Lestari Ruskan; Fathoni
Teknika Vol. 15 No. 1 (2026): March 2026
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v15i1.1446

Abstract

Social media site X has emerged as a significant platform for voicing public views on government initiatives, such as the Sekolah Rakyat Program. Nevertheless, utilizing social media information for sentiment analysis often faces challenges due to class imbalance, which may result in skewed predictions from models. This research seeks to examine public sentiment and assess how well the Random Forest algorithm performs when paired with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction and Random Oversampling (ROS) methods to mitigate class imbalance. A dataset comprising 8,623 tweets was gathered and split into training and testing sets using an 80:20 ratio. The results of the experiments indicate that the suggested method demonstrates robust and realistic classification performance, achieving an accuracy of 80.99%, along with a weighted average score in precision, recall, and F1-score of 0.81. Additionally, the sentiment analysis indicates that the majority of public opinions are largely positive, with roughly 69.4% of the testing data reflecting a favorable outlook toward free education access and school improvement efforts. These findings suggest that the proposed model provides reliable performance in capturing public sentiment patterns.
Co-Authors Ali Ibrahim Deni Agus Hendrawan Fathoni Yadi Utama