Understanding public perceptions of waste management: A comparative sentiment analysis using LSTM and IndoBERT

Riszki Wijayatun Pratiwi; Chayanita Sekar Wijaya; Vilya Lakstian Catra Mulia; Jumi Darsih

doi:10.61511/wass.v3i2.2026.3176

Authors

Riszki Wijayatun Pratiwi Department of Informatics Management, Politeknik Assalaam Surakarta, Surakarta, Central Java 57169, Indonesia
Chayanita Sekar Wijaya Department of Informatics Management, Politeknik Assalaam Surakarta, Surakarta, Central Java 57169, Indonesia
Vilya Lakstian Catra Mulia Department of Informatics Management, Politeknik Assalaam Surakarta, Surakarta, Central Java 57169, Indonesia
Jumi Darsih Department of Informatics Management, Politeknik Assalaam Surakarta, Surakarta, Central Java 57169, Indonesia

DOI:

https://doi.org/10.61511/wass.v3i2.2026.3176

Keywords:

IndoBERT, LSTM, sentiment analysis, waste management

Abstract

Background: Waste management remains one of Indonesia’s pressing environmental and social challenges, characterized by increasing waste generation and public dissatisfaction with existing waste-treatment systems. Understanding public sentiment toward waste-management policies and community-based programs is essential for supporting more effective interventions. This study aims to examine public perceptions of waste-management issues in Indonesia and compare the performance of Long Short-Term Memory (LSTM) and Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) for sentiment classification. Methods: This study used a dataset of 15,000 public text opinions collected from social media using a crawling technique. The data were manually annotated into three sentiment categories: positive, neutral, and negative. After preprocessing, two sentiment-classification models, LSTM and IndoBERT, were developed and evaluated. Model performance was assessed using accuracy, precision, recall, and F1-score. Topic-based sentiment analysis was also conducted to identify public perceptions of major waste-management issues. Findings: The results showed that IndoBERT outperformed LSTM in sentiment classification by capturing contextual meaning and sentiment nuances in Indonesian-language expressions more effectively. Topic-based analysis indicated that community-based initiatives, including waste banks and recycling programs, were generally associated with positive sentiment. In contrast, landfill-related issues were more frequently associated with negative sentiment due to concerns about pollution and poor management. Conclusion: IndoBERT provides a more reliable approach for analyzing public sentiment toward waste-management issues in Indonesia. The findings highlight the importance of understanding public perceptions to strengthen community engagement, improve public communication, and support more effective waste-management policies and programs. Novelty/Originality of this article: The novelty of this study lies in integrating deep learning-based sentiment classification with topic-based sentiment analysis to examine public opinion specifically regarding waste-management strategies in Indonesia. The comparative evaluation of LSTM and IndoBERT also provides empirical insight into the suitability of contextual language models for analyzing Indonesian-language public opinions on waste management.

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