Comparative Evaluation of CNN-LSTM Model for Emotion Detection in Indonesian Text

Wella, Wella (2024) Comparative Evaluation of CNN-LSTM Model for Emotion Detection in Indonesian Text. Journal of Logistics, Informatics and Service Science, 11 (5). pp. 457-470. ISSN 2409-2665

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Abstract

This study developed an emotion detection model for Indonesian text using a dataset from prior research. The data was refined through multiple pre-processing steps before applying CNN-LSTM machine learning techniques. Comparative analysis indicated the model achieved 58% accuracy, lower than baseline methods. The results imply need for larger annotated corpora, improved text normalization, and integration with state-of-the-art deep learning approaches to enhance performance for Indonesian emotion detection.

Item Type:	Article
Creators:	Wella, Wella
Contributors:
Keywords:	CNN, emotion detection model, LSTM, machine learning, pre-processing
Subjects:	000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods > Artificial Intelligence, Machine Learning, Pattern Recognition, Data Mining
Divisions:	Faculty of Engineering & Informatics > Information System
Date Deposited:	02 Dec 2025 07:33
URI:	https://kc.umn.ac.id/id/eprint/42537

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