Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian Pada Masyarakat METHABDI
Rangga Permana Sanjaya
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health

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

Found 1 Documents
Search

 1 
Pengembangan Perangkat Lunak Analisis Sentimen Komentar Mahasiswa pada Kegiatan Belajar Mengajar Menggunkan Metode Long Short-Term Memory Berbasis Text Mining Panggabean, Erwin; Penda Sudarto Hasugian; Amran Sitohang; Nadia Wulan Dari; Rangga Permana Sanjaya
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 5 No 2 (2025): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol5No2.pp249-258

Abstract

This research focuses on the development of sentiment analysis software designed to evaluate students’ comments at SMKS Methodist 8 Medan concerning teaching and learning activities through a text mining approach. The software aims to support educational evaluation by automatically identifying the emotional tone and opinions expressed by students in their feedback. In this study, text mining techniques are employed to preprocess and analyze comment data collected from online learning platforms and school surveys. The preprocessing stages include tokenizing, stopword removal, and stemming to prepare clean textual data for analysis. Subsequently, the Naïve Bayes classification algorithm is implemented to categorize the comments into three sentiment classes: positive, negative, and neutral. The results of experimental testing demonstrate that the developed system can accurately identify sentiment tendencies with satisfactory precision and reliability. Moreover, the visualization of sentiment results enables educators to better understand students’ perceptions and engagement levels in the learning process. This research contributes to the field of educational technology by providing a data-driven tool that helps schools evaluate teaching effectiveness, identify areas of improvement, and enhance the overall quality of learning experiences through objective analysis of student feedback.
Co-Authors Amran Sitohang Erwin Panggabean Nadia Wulan Dari Penda Sudarto Hasugian