Article Per Year (5 Year)
p-Index From 2020 - 2025
3.249
P-Index
This Author published in this journals
All Journal Teknosains: Media Informasi Sains dan Teknologi Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Media Informatika Brilliance: Research of Artificial Intelligence Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication Jurnal INFOTEL
gasim, Gasim
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics Physics Social Sciences Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search
Journal : Algoritme Jurnal Mahasiswa Teknik Informatika

 1   2 
Analisis Sentimen Supporter Sriwijaya FC Berbasis Manchine Learning Farhan, Muhammad; Puspasari, Shinta; Gasim, Gasim
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i1.11288

Abstract

This study analyzes the sentiment of Sriwijaya FC supporters toward the club's management through comments on Instagram. Data was collected from 6,601 comments on the official @SriwijayaFC account and processed through text preprocessing stages with an 80:20 split for training and testing data. The analysis was conducted using four machine learning algorithms: SVM, Random Forest, Naïve Bayes, and KNN. The results indicate that neutral sentiment dominates (50.92%), followed by positive (25.07%) and negative (24.01%) sentiment, suggesting that most comments are informative or impartial, although there are both supporting and opposing opinions. Model performance evaluation using a confusion matrix and accuracy, precision, recall, and F1-score metrics shows that SVM achieved the highest accuracy (89%), followed by Random Forest (82%), Naïve Bayes (74%), and KNN (65%). These findings demonstrate that machine learning is effective in classifying social media sentiment. Future research may explore deep learning algorithms and expand data sources to other platforms for a more comprehensive analysis.
Co-Authors A, Satria Andika, Rizki Chandra B, Leonardo Derry Alamsyah Desy Iba Ricoida DEWI SARTIKA Dicky Pratama Dina Agustina, Dina Faza Nujjiya, Muhammad Alief Hartati, Ery Heriasyah, Rudi Inayatullah, Inayatullah Indah Permatasari Juniawan, Rycci Michael Michael Muhammad Farhan Muhammad Haviz Irfani Naufal, Muhammad Afif Purnamasari, Evi Rizani Teguh Rudi Heriansyah, Rudi rusbandi rusbandi, rusbandi Samsuryadi Samsuryadi Saputri, Nurdiana Dewi Shinta Puspasari Siregar, Muhammad Azwar Tsar Sudiadi, Sudiadi Suhanto, Kevita Titany Tanjungan, Erven Tinaliah, Tinaliah Triana Elizabeth, Triana Wilsen, Erick Brilliant Yoannita, Yoannita