Aplikasi Analisis Sentimen Terhadap Isu Pengelolaan Sampah untuk Solusi Sistem Cerdas Sebagai Upaya Mewujudkan Indonesia Sehat

Riszki Wiajayatun Pratiwi; Rifqi Fauzi Rahmadzani; Chayanita Sekar Wijaya; Vilya Lakstian Catra Mulia; Jumi Darsih

doi:10.30591/jpit.v10i4.9784

Aplikasi Analisis Sentimen Terhadap Isu Pengelolaan Sampah untuk Solusi Sistem Cerdas Sebagai Upaya Mewujudkan Indonesia Sehat

Riszki Wiajayatun Pratiwi, Rifqi Fauzi Rahmadzani, Chayanita Sekar Wijaya, Vilya Lakstian Catra Mulia, Jumi Darsih

Abstract

The issue of waste management in Indonesia has become a crucial problem that significantly affects both public health and the environment. A lack of public awareness and suboptimal management practices can further worsen environmental conditions and health risks. Therefore, a smarter and data-driven approach is required to understand public opinion and encourage greater community participation. Social media reviews provide valuable insights into waste management issues; however, the overwhelming volume of such data makes analysis challenging.To address this, a sentiment analysis application is needed to summarize public opinions on waste management issues and classify them into positive, negative, and neutral sentiments. The goal is to support more accurate decision-making in formulating effective waste management policies.Sentiment analysis using deep learning methods has demonstrated superior performance compared to lexicon-based and traditional machine learning approaches. BERT, as a deep learning method, has proven to be highly effective in handling textual data. Therefore, this study adopts the BERT method.The research method is divided into two stages. The first stage involves building a sentiment analysis model using BERT, while the second stage focuses on software development using the Software Development Life Cycle (SDLC). The first stage produces the best-performing BERT model, which is then applied in developing a sentiment analysis application for waste management issues.The findings of this study indicate that the BERT method is highly suitable for datasets on waste management issues obtained from Twitter crawling, achieving an accuracy of over 90%. The best-performing model was successfully implemented in a web-based sentiment analysis application.

Keywords

sentiment analysis; waste management issues; Deep Learning, BERT

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DOI: https://doi.org/10.30591/jpit.v10i4.9784