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Authors: I. Gusti Ngurah Bagus Caturbawa ; Sri Asri ; I. Wayan Suasnawa ; Ni Gusti Ayu Putu Harry Saptarini and Kadek Yasa

Affiliation: Department of Electrical Engineering, Politeknik Negeri Bali, Kampus Bukit Jimbaran, Badung, Indonesia

Keyword(s): Academic Services, Support Vector Machine, Long Short Term Memory, Sentiment Analysis.

Abstract: Sentiment analysis on student satisfaction aims to obtain feedback related to quality assurance efforts, so that students’ opinions on perceived academic services can be known. This result is an evaluation for improving academic services at the Bali State Polytechnic. The method that can be used to find out the opinions of students having positive, negative or neutral perceptions is to use machine learning algorithms. In this study, two methods are used, namely Support Vector Machine and Long Short Term Memory. The results of this study indicate that student sentiment is classified into three classes positive, negative and neutral. The Support Vector Machine method obtained an accuracy rate of 0.81 (positive), 0.88 (negative) and 0.75 (neutral) while the Long Short Term Memory (LSTM) method obtained an accuracy of 0.91 (positive), 0.85 (negative) and 0.85 (neutral).

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Paper citation in several formats:
Gusti Ngurah Bagus Caturbawa, I.; Asri, S.; Wayan Suasnawa, I.; Gusti Ayu Putu Harry Saptarini, N. and Yasa, K. (2023). Implementation of Sentiment Analysis for Student Academic Services Using Support Vector Machine and Long Short Term Memory (LSTM) Methods. In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - iCAST-ES; ISBN 978-989-758-619-4; ISSN 2975-8246, SciTePress, pages 208-211. DOI: 10.5220/0011740100003575

@conference{icast-es23,
author={I. {Gusti Ngurah Bagus Caturbawa}. and Sri Asri. and I. {Wayan Suasnawa}. and Ni {Gusti Ayu Putu Harry Saptarini}. and Kadek Yasa.},
title={Implementation of Sentiment Analysis for Student Academic Services Using Support Vector Machine and Long Short Term Memory (LSTM) Methods},
booktitle={Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - iCAST-ES},
year={2023},
pages={208-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011740100003575},
isbn={978-989-758-619-4},
issn={2975-8246},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - iCAST-ES
TI - Implementation of Sentiment Analysis for Student Academic Services Using Support Vector Machine and Long Short Term Memory (LSTM) Methods
SN - 978-989-758-619-4
IS - 2975-8246
AU - Gusti Ngurah Bagus Caturbawa, I.
AU - Asri, S.
AU - Wayan Suasnawa, I.
AU - Gusti Ayu Putu Harry Saptarini, N.
AU - Yasa, K.
PY - 2023
SP - 208
EP - 211
DO - 10.5220/0011740100003575
PB - SciTePress

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