Hybrid LSTM-CNN-GRU Deep Learning for Integrating IoT and Social Media Sentiment Analysis in Indonesian Higher Education Reputation Management

Kresno Murti Prabowo; Ikbal  Nidauddin; Endro  Andiono

doi:10.52436/1.jutif.2026.7.2.5426

Authors

Kresno Murti Prabowo Information Management Study Program, Piksi Input Serang Polytechnic, Indonesia
Ikbal Nidauddin Information Management Study Program, Piksi Input Serang Polytechnic, Indonesia
Endro Andiono Information Management Study Program, Piksi Input Serang Polytechnic, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5426

Keywords:

Deep learning, Hybrid LSTM-CNN, Higher Education Reputation, Indonesian Universities, Internet of Things, Sentiment Analysis

Abstract

Higher education institutions in Indonesia face critical challenges in managing digital reputation. Despite 85% of prospective students using social media for university research, only 23% of institutions have integrated monitoring systems, resulting in 67% experiencing undetected reputation crises with substantial financial losses. This research proposes a novel framework integrating IoT campus data with social media sentiment analysis using hybrid deep learning architecture. The system employs LSTM-CNN networks with multi-head attention mechanisms for sentiment classification and GRU networks for reputation trend prediction, enhanced with data fusion strategy. Data collected from 428 IoT sensors and 3.2 million social media posts across five Indonesian universities over six months underwent advanced preprocessing including Indonesian-specific slang normalization and Sastrawi stemming. The hybrid LSTM-CNN architecture with attention achieved 90.3% sentiment classification accuracy (Macro-F1: 0.903), significantly outperforming baseline methods including Naive Bayes (76.2%), traditional LSTM (84.5%), and IndoBERT (87.1%). IoT integration contributed 18.2% RMSE improvement in trend prediction (R²: 0.874). The early warning system predicted reputation crises with 85.7% precision and 82.4% recall, providing critical intervention windows averaging 14.3 days before incidents. The real-time dashboard achieved 98.5% availability with sub-3-second response time and excellent usability (SUS score: 82.4). This research contributes: (1) novel IoT-sentiment integration framework with demonstrated effectiveness, (2) context-aware deep learning architecture optimized for Indonesian language achieving state-of-the-art performance, (3) validated early warning system enabling proactive reputation management, and (4) practical implementation with significant improvements over existing methods, advancing educational data analytics and AI-based decision support systems.

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