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Mahmudi, Anas
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Computer Science & IT Electrical & Electronics Engineering

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Salt Quality Classification Using Backpropagation Neural Network and K-Nearest Mahmudi, Anas; Abidin, Zainul; Razak, Angger Abdul Razak Abdul
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1505

Abstract

Salt quality plays a vital role in determining its usability across various sectors, including food, pharmaceuticals, and industrial applications. Traditional methods of classifying salt quality, which rely heavily on manual inspection and laboratory testing, are often time-consuming, costly, and prone to human error. In response to these limitations, this study explores the implementation of machine learning techniques—specifically, Backpropagation Neural Network (BPNN) and K-Nearest Neighbor (K-NN)—to classify salt quality based on its physical and chemical properties. The features used in this research include NaCl concentration, moisture content, magnesium levels, sulfat, insoluble, calcium, NaCL(wb) and NaCL(db) which are commonly used indicators of salt purity and grade. The BPNN model is designed to handle complex and non-linear relationships within the dataset by adjusting weights through iterative backpropagation during training. Meanwhile, the K-NN algorithm serves as a simpler, instance-based learning method that classifies samples based on the majority class of their nearest neighbors in the feature space. Comparative experiments were conducted to evaluate the classification and computational efficiency of both models. Results indicate that both methods are effective in classifying salt into predefined quality categories. However, BPNN consistently outperforms K-NN in terms of time efficiency and generalization, particularly when handling noisy or overlapping data. The findings underscore the potential of integrating artificial intelligence into quality control systems in the salt industry, offering a faster, more objective, and scalable solution for ensuring product standards.
Co-Authors Razak, Angger Abdul Razak Abdul Zainul Abidin