Build Up Aplikasi Verifikasi Kemurnian Balok Karet dengan Whale Optimization Algorithm

Abstract

The rubber industry requires precise quality control of rubber blocks to maintain product consistency and customer satisfaction. This study develops an application to verify the purity of rubber blocks using the Whale Optimization Algorithm (WOA). The application aims to provide an accurate, efficient, and automated solution for detecting impurities. Inspired by the bubble-net hunting strategy of humpback whales, WOA is effective in solving complex optimization problems. In this research, WOA optimizes parameters for impurity detection, enhancing verification accuracy. The application integrates image processing techniques and machine learning algorithms. Images of rubber blocks are captured and processed to extract relevant features, which are then analyzed using WOA to identify impurities. Extensive testing demonstrated that the application achieves high accuracy in impurity detection, outperforming traditional methods. The use of WOA significantly reduces processing time, making the application suitable for real-time industrial verification. This study highlights the potential of the Whale Optimization Algorithm to improve quality control processes in the rubber industry. The developed application offers a reliable and efficient tool for ensuring rubber block purity, thereby enhancing product quality and operational efficiency.