<![CDATA[The development of artificial intelligence is currently experiencing very rapid growth, both in hardware and software development. Artificial intelligence has succeeded in creating various products that are used in everyday life. These products can be classified into four techniques in artificial intelligence, namely searching, reasoning, planning and learning. This research discusses the implementation of a disease detection system on cassava leaves and chili leaves based on machine learning. The aim of this research is to develop a system that can automatically detect diseases in cassava and chili plants through leaf image analysis using machine learning technology. The method used involves recording digital images and machine learning algorithms to recognize disease patterns and symptoms on plant leaves. In this research, a dataset containing images of disease-infected leaves is used to train a machine learning model. The test results show that the implemented system is able to recognize and differentiate various diseases on cassava and chili leaves with sufficient accuracy. Implementation of this system makes an important contribution in supporting the monitoring and management of plant diseases quickly and efficiently, which in turn can help farmers increase their agricultural productivity.]]>
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