Article Per Year (5 Year)
p-Index From 2020 - 2025
2.042
P-Index
This Author published in this journals
All Journal JURNAL TEKNOLOGI INFORMASI Respati Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) JAMU : Jurnal Abdi Masyarakat UMUS
Siti Rihastuti
STMIK Amikom Surakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Algoritme Jurnal Mahasiswa Teknik Informatika

 1 
Perbandingan Kinerja Support Vector Machine Dan Random Forest Untuk Klasifikasi Sentimen Pengguna Aplikasi Gojek Dengan Optimasi Smote Rihastuti, Siti; Rosyidi, Afnan
Jurnal Algoritme Vol 5 No 3 (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.v5i3.13463

Abstract

This study compares the performance of Support Vector Machine (SVM) and Random Forest in classifying Gojek user sentiment using 2,000 Indonesian-language reviews (1,351 positive, 566 negative, 83 neutral). After data preprocessing and TF-IDF feature extraction, SMOTE was applied to balance the training data in each fold. Using Stratified K-Fold Cross-Validation, results showed that Random Forest achieved higher and more consistent accuracy (84.1%) than SVM (76.1%). The Paired t-test and McNemar’s Test (p-value < 0.05) confirmed that the Random Forest’s superiority was statistically significant. Overall, both models were effective, but Random Forest performed better for Gojek sentiment classification, supporting user satisfaction monitoring and complaint detection.
Co-Authors Afnan Rosyidi Cisde Mulyadi Cisde Mulyadi Febrianta Surya Nugraha Handoko Handoko Hanif Al Fatta Ina Sholihah Widiati, Ina Sholihah Kusrini Kusrini Lashwaty, Nina Dewi Lilik Sugiarto Nina Dewi Lashwaty Purwidiantoro, Moch. Hari Tinuk Agustin Tiya Suryani