# Classification of Community Responses to Service Offices using a Combined CNN-LSTM Algorithm and Random Forest
> Rohalia A.

URL kanonis: https://discover.unhas.ac.id/publications/classification-of-community-responses-to-service-offices-using-a-combined-cnn-ls
Jurnal / Konferensi: Proceeding 2023 International Conference on Artificial Intelligence Robotics Signal and Image Processing Airosip 2023
Tahun terbit: 2023
DOI: https://doi.org/10.1109/AIRoSIP58759.2023.10873903
Citations: 1

## Authors
- Rohalia A.

## Abstract
This study analyzes urban issues by examining people's social media responses. The government requires public feedback to assess its performance in addressing these issues. The study employs a combined CNN-LSTM & RF method to classify the responses into 16 agency categories based on their respective responsibilities. We utilize Continuous Bag-of-Words (CBOW) to convert text into vector form, conduct feature extraction using Convolutional Neural Network (CNN) followed by Long Short-Term Memory (LSTM) combination and then perform classification with Random Forest (RF). This research contributes to developing a practical combination algorithm for analyzing and classifying public responses. The combined results of these algorithms provide a multi-class service classification with 83% precision, 80% recall, and 81% F1 score, which enhances accuracy compared to separate models.

## Keywords
- Random forest
- Computer science
- Service (business)
- Artificial intelligence
- Algorithm
- Business
- Marketing

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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