Enhanced Classification of Lombok Pearl Quality Based on Shape and Size Using PSO-Optimized Artificial Neural Network

Muhammad Izzul   Anshori; Pulung Nurtantio  Andono; Arief  Soeleman

doi:10.59395/ijadis.v6i3.1434

Authors

Muhammad Izzul Anshori Universitas Dian Nuswantoro Semarang
Pulung Nurtantio Andono Universitas Dian Nuswantoro Semarang
Arief Soeleman Universitas Dian Nuswantoro Semarang

DOI:

https://doi.org/10.59395/ijadis.v6i3.1434

Keywords:

GLCM, Artificial Neural Network, Particle Swarm Optimization, Pearl Quality Classification, Feature Selection

Abstract

This study aims to develop an intelligent classification model for pearl quality assessment using an integrated approach combining Gray Level Co-occurrence Matrix (GLCM), Particle Swarm Optimization (PSO), and Artificial Neural Network (ANN). Sixteen texture features were extracted from four directional orientations using GLCM. PSO was employed as a feature selection algorithm to reduce dimensionality and enhance classification performance. Two ANN models were compared: a baseline model using all GLCM features and an optimized model utilizing only PSO-selected features. The models were trained and validated using 10-fold cross-validation. Results showed that the PSO-enhanced ANN achieved an accuracy of 94.72%, outperforming the baseline model which reached only 89.17%. Further evaluations using confusion matrix, Receiver Operating Characteristic (ROC) analysis, and Principal Component Analysis (PCA) confirmed the superior discriminative capability and improved class separability of the optimized model. These findings highlight the effectiveness of combining swarm intelligence with neural networks in texture-based classification tasks, offering a robust and scalable solution for automated quality inspection in the pearl industry and related domains.

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