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All Journal Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI)
Azza, Nazilatul
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Computer Science & IT Education Other

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PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE DAN LONG SHORT-TERM MEMORY UNTUK KLASIFIKASI EMOSI MAHASISWA PADA  PLATFORM X   Azza, Nazilatul; Hadian, Nur
Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI) Vol. 7 No. 1 (2026): Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI)
Publisher : Information Technology Education Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/jipti.v7i1.4041

Abstract

Platform X is widely used by students to express emotions related to academic activities, yet it has not been optimally analyzed. This study aims to classify student emotions and compare the performance of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). The method used is a computational experiment involving data crawling, preprocessing, lexicon-based labeling, and modeling using TF-IDF for SVM and word embedding for LSTM. The dataset consists of 2,914 data points. The results show that SVM achieved an accuracy of 71.18%, outperforming LSTM at 68.95%. This indicates that SVM is more stable on limited datasets, while LSTM tends to overfit. The study concludes that algorithm selection should consider data characteristics and contributes to the comparison of machine learning and deep learning methods.   Keywords: LSTM, Sentiment Analysis, Social Media X, Student Emotion, SVM, TF-IDF
Co-Authors Hadian, Nur