An HHO-Optimized LSTM Framework for Predicting Adverse Effects Associated with Reproductive and Breast Disorders

ANGEL METANOSA AFINDA (Telkom University)
IGA NARENDRA PRAMAWIJAYA (Telkom University)
FAUZAN FIRDAUS (Telkom University)

ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
Vol 14, No 3: Published July 2026

Reproductive toxicity prediction is a major challenge in drug development as side effects are often difficult to detect early. SMILES representations provide a compact sequential format suitable for deep learning. This study proposes an HHO-optimized LSTM model to predict reproductive and breast-related side effects. Four architectural schemes were evaluated including L (LSTM only), CL (Convolution + LSTM), LD (LSTM + Dense), and CLD (Convolution + LSTM + Dense). Results show that the tuned L scheme achieved the best performance with accuracy increasing from 0.6304 to 0.6739 and F1-score from 0.6792 to 0.7097. These findings highlight the effectiveness of metaheuristic optimization in computational toxicology modeling.