<![CDATA[The increase in tomato cultivation has been consistent over the years and significantly contributes to the national economy. The increase in plant density and the expansion of harvesting areas can create ideal conditions for the spread of diseases in tomato plants, potentially threatening crop yields. This study presents a machine learning model using Convolutional Neural Networks (CNN) algorithms to detect tomato plant diseases, achieving an accuracy rate of 96%. The model demonstrates a relatively low loss rate of approximately 13%, indicating that the predictions closely align with actual conditions. These results indicate that the model's performance is effective in preventing the spread of tomato plant diseases by helping to identify diseases earlier. The machine learning model is implemented through a Telegram Bot as a user interface, which is not only effective in providing information on tomato plant disease detection, but also ensures that the information is delivered efficiently and accurately. A Quality of Service (QoS) analysis was conducted on the communication between users and the Telegram server, considering parameters such as throughput, delay, and packet delivery. The overall QoS score is indexed at 3 in the "Satisfactory" category according to TIPHON standards. This QoS score is derived from the throughput parameter with an index of 4 in the "Very Good" category, the packet delivery parameter with an index of 4 in the "Very Good" category, and the delay parameter with an index of 4 in the "Very Good" category.]]>
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