Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Emerging Science Journal
Agung Santosa
Research Organization for Electronics and Informatics, National Research and Innovation Agency, Jakarta Pusat 10340
Environmental Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Multimodal Emotion Recognition Using Hybrid Large Language Models and Metaheuristic Algorithms Andino Maseleno; M. Teduh Uliniansyah; Agung Santosa; Lyla Ruslana Aini; Rini Wijayanti; Ahmad Fudholi; Chotirat Ann Ratanamahatana
Emerging Science Journal Vol. 10 No. 2 (2026): April
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-02-015

Abstract

Emotion recognition is a vital component of human–computer interaction and intelligent systems, yet robust multimodal emotion recognition remains challenging due to high-dimensional input space, noisy features, and the complexity of integrating heterogeneous modalities. This study proposes a novel hybrid multimodal framework that enhances both accuracy and computational efficiency by combining the semantic representation capability of Large Language Models (LLMs) with the optimization strengths of metaheuristic algorithms. In the proposed approach, an LLM is utilized to extract high-level contextual features from text and audio streams, while the Binary Artificial Hummingbird Algorithm (BAHA) performs feature selection to remove redundant attributes. Subsequently, the Goose Algorithm (GA) optimizes classifier hyperparameters, and the Komodo Mlipir Algorithm (KMA) conducts late fusion of the final multimodal outputs. Experiments conducted on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset, evaluated on six emotion categories, demonstrate that this hybrid approach successfully captures subtle affective cues and surpasses state-of-the-art baselines, achieving an accuracy of 87.5%. Integrating LLMs with multiple specialized metaheuristics therefore yields a substantially more robust emotion recognition pipeline and represents a promising direction toward the development of more emotionally intelligent systems.
Co-Authors Ahmad Fudholi Andino Maseleno Chotirat Ann Ratanamahatana Lyla Ruslana Aini M. Teduh Uliniansyah Rini Wijayanti