<![CDATA[Indonesia’s national potato production faces significant threats from leaf diseases, while manual classification remains slow, subjective, and prone to error due to the high visual similarity across disease categories. This highlights the need for a precise and reliable automated classification system. However, many previous studies have not applied systematic hyperparameter optimization, leaving the capacity of deep learning architectures underutilized. Addressing this research gap, this study aims to enhance the performance of MobileNetV2 for potato leaf disease classification through a structured hyperparameter optimization process. A Sequential Search strategy validated through 3 fold Stratified Cross Validation is employed to obtain stable performance estimates. Four key hyperparameters are examined: learning rate from 0.001 to 0.009, dropout from 0.1 to 0.9, batch size from 8 to 192, and epochs from 10 to 100. The optimal configuration consists of a learning rate of 0.007, dropout of 0.2, batch size of 32, and 60 epochs, which enables MobileNetV2 to achieve an accuracy of 99 percent. Despite this strong performance, evaluation results reveal a minor limitation in the Young Blight class, where precision is slightly lower due to overlapping visual characteristics. These findings establish a new benchmark for potato leaf disease classification and provide a reproducible optimization framework for future studies. The study offers both methodological and practical contributions to the development of precise and efficient plant disease classification systems within the context of smart agriculture.]]>
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