Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal Indonesian Journal of Information System JOURNAL OF APPLIED INFORMATICS AND COMPUTING INOVTEK Polbeng - Seri Informatika
Ozzi Suria
Unknown Affiliation
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : INOVTEK Polbeng - Seri Informatika

 1 
Public Sentiment Analysis of Danantara Policy through Social Media X Using SVM and Random Forest Djema, Gayus Gregorius Ferdinand; Ozzi Suria
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/rcr21h75

Abstract

Abstract - This study aims to analyze public sentiment toward the establishment of the Danantara Investment Management Agency (Danantara) through the X social media platform (formerly Twitter) using a machine learning-based text classification approach. While sentiment analysis has been widely applied across various domains, there remains a research gap in examining public responses to new national policies particularly Danantara on platform X. A total of 1,713 tweets were collected using Python-based web scraping via Google Colab during the period from February to June 2025. The research involved data preprocessing, manual sentiment labeling, model training using Support Vector Machine (SVM) and Random Forest algorithms, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. The classification results show that positive sentiment dominates at 55.6%, while negative sentiment accounts for 44.4%. Random Forest outperformed SVM with an accuracy of 92.36% and an F1-score of 92.19%, compared to SVM's accuracy of 85.45% and F1-score of 87.54%. These findings indicate that Random Forest is more effective in handling short-text public opinion data that is often unstructured. Practically, this study recommends the integration of real-time sentiment monitoring through social media as a strategic tool for policymakers and state-owned enterprises (SOEs) in formulating more responsive and data-driven public policies
Instagram-Based Sentiment Analysis on the Oil Refinery Project in Batam Using SVM and XGBoost Rumapea, Doni Immanuel; Ozzi Suria
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/am1zxb64

Abstract

This sentiment analysis of Instagram comments regarding the planned construction of an oil refinery in Batam classifies public opinion into three categories: positive, neutral, and negative. The initial dataset of 1,576 comments was reduced to 1,441 after text preprocessing (tokenization, stop‑word removal, and stemming), and then split into 1,152 training instances and 289 testing instances. Two machine learning algorithms, Support Vector Machine (SVM) with class_weight='balanced' and Extreme Gradient Boosting (XGBoost) with oversampling, were applied to address class imbalance. In addition to accuracy (SVM: 81.25%; XGBoost: 96%), precision, recall, and F1‑score metrics were evaluated to assess the balance between true‑positive and true‑negative classifications. The results indicate that XGBoost not only outperformed SVM in terms of accuracy but also achieved the highest F1‑score on the minority class, demonstrating its ability to detect negative opinions that have often been overlooked. This study offers a novel contribution to Instagram-based sentiment analysis a platform that is visually distinct from Twitter by focusing on public opinions surrounding the strategic issue of energy infrastructure development. The findings can be utilized for real-time sentiment mapping, supporting policy formulation, urban planning, and monitoring industry responses to critical projects in the digital era.
Co-Authors Djema, Gayus Gregorius Ferdinand Mustaqim, Habi Jiyan Norshahila Ibrahim Prasetyaningrum, Putri Taqwa Rumapea, Doni Immanuel