PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE DAN LONG SHORT-TERM MEMORY UNTUK KLASIFIKASI EMOSI MAHASISWA PADA  PLATFORM X

Nazilatul Azza; Nur Hadian

doi:10.52060/ck4e1y15

Authors

Nazilatul Azza ( Institut Teknologi dan Sains Nahdlatul Ulama Pekalongan )

Nur Hadian ( Institut Teknologi Dan Sains Nahdlatul Ulama Pekalongan )

DOI:

https://doi.org/10.52060/ck4e1y15

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

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Keywords	:	Keywords: Analisis Sentimen, Emosi Mahasiswa, LSTM, Media Sosial X, SVM, TF-IDF
Galleys	:	Pdf
Published	:	2026-04-07
Issue	:	Vol. 7 No. 1 (2026): Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI) Section Articles