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AM, Armadi
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Computer Science & IT Health Professions Public Health

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DISEASE DETECTION SYSTEM IN COFFEE PRUNS USING MOBILENET ALGORITHM AM, Armadi; Mardin, Muhammad Ikhwan; Saleh, Husna
Jurnal Kesehatan dan Teknologi Vol. 1 No. 1 (2024): Jurnal Kesehatan dan Teknologi
Publisher : Institut Kesehatan dan Teknologi Buton Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64879/jkestek.v1i1.33

Abstract

Coffee fruit is a crop that has a strategic role in the economy and is a source of livelihood for most farmers in various tropical regions. The disease detection system on coffee fruit using the MobileNet algorithm is a technology that combines artificial intelligence and image processing to identify infected diseases in coffee plants. In this study, researchers used the Cobb-Douglas method and regression conducted by researchers using E-Views software. The results showed that the MobileNet algorithm in detecting diseases in coffee is quite efficient in terms of computation and the size of the detection system model has achieved the best accuracy, namely with 92% training accuracy and 85% testing and validation accuracy.
Co-Authors Muhammad Ikhwan Mardin Saleh, Husna