Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Informatika dan Teknik Elektro Terapan Akrab Juara : Jurnal Ilmu-ilmu Sosial Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Fuad Nur Hasan
Unknown Affiliation
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Medicine & Pharmacology

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Public Opinion Sentiment Analysis of Government Fuel Purchasing Policy by the Private Sector Using Support Vector Machine (SVM) Methods Muhammad Rossi Satria Fitrah; Afif Al Qifary; Ahmad Maulana Wahyudi; Dea Deswina Sumarna; Muhammad Nabiel Alfarizi; Fuad Nur Hasan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1970

Abstract

Government policies that provide opportunities for the private sector to participate in the purchasing and distribution of fuel oil (BBM) have triggered various reactions within society. The diversity of opinions expressed on social media reflects public perceptions of the effectiveness and potential impacts of these policies. This study aims to examine public sentiment toward the government policy by applying the Support Vector Machine (SVM) method. Data were collected from various social media platforms containing public responses to the issue of private sector involvement in fuel purchasing. The analysis process consisted of several stages, including data collection, data preprocessing (comprising cleansing, tokenizing, stopword removal, and stemming), feature extraction using the Term Frequency Inverse Document Frequency (TF-IDF) approach, and sentiment classification using the SVM algorithm. The results show that the SVM algorithm performs well in classifying public opinions into two sentiment categories, positive and negative, with a relatively high level of accuracy. The analysis indicates that the majority of public opinions tend to be negative, driven by concerns over potential price disparities, weakened government oversight, and possible socio-economic impacts. The findings of this study are expected to provide constructive input for the government in evaluating and developing energy policies that are more transparent and oriented toward public interest.
Sentiment Analysis of Public Opinion on Rupiah Redenomination Policy Using Support Vector Machine and SMOTE Haidar Aslam; Haikal Nurul Barki; Adhi Prasetyo Wibowo; Faqih Al Araf; Abdul Hamid Musawir; Fuad Nur Hasan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1982

Abstract

The government’s planned rupiah redenomination has generated a substantial wave of public opinion across social media platforms. This study aims to analyze public sentiment by examining comments on YouTube and classifying them into two categories: positive and negative. The data are collected through web scraping conducted on December 21, 2025, using the keyword “rupiah redenomination.”Given the pronounced imbalance between negative and positive opinions, this study applies the Synthetic Minority Over-sampling Technique (SMOTE) to balance the class distribution within the training data. The research pipeline consists of text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification using a linear-kernel Support Vector Machine (SVM). Experimental results indicate that the SVM model achieves an accuracy of 88.28%. The application of SMOTE is shown to effectively enhance the model’s ability to identify the minority class, with the recall for positive sentiment reaching 0.71. Furthermore, the analysis reveals that public opinion is predominantly negative (83.93%), reflecting widespread concern regarding the potential economic implications of the policy.
Co-Authors Abdul Hamid Musawir Adhi Prasetyo Wibowo Afif Al Qifary Ahmad Maulana Wahyudi Anisa Permata Sari Ari Abdilah Dea Deswina Sumarna Eka Kusuma Pratama Elah Nurlelah Faqih Al Araf Ferdinan Restu Ramadhan Gata, Windu Haidar Aslam Haikal Nurul Barki Kurani Mega Asteroid Mochamad Wahyudi Muhammad Nabiel Alfarizi Muhammad Rossi Satria Fitrah Nur Fachmi, Andhika Nurlaelatul Maulidah Reihan Dwi Patria Salma Pudjiati Yusuf Bachtiar