<![CDATA[This research aims to design and implement a monitoring and classification system for Pakcoy (Brassica rapa L.) plant conditions based on the Internet of Things (IoT) and machine learning algorithms in the Smart Greenhouse of Universitas Islam Nusantara. This study represents one of the applications of IoT and machine learning technology advancements to improve efficiency and effectiveness in the agricultural sector. The developed system utilizes CO?, SHT30, BH1750, and DHT22 sensors to monitor environmental parameters in real-time, including temperature, humidity, light intensity, panel box temperature, and CO? concentration. The monitoring data are used as input for classifying plant conditions using five machine learning methods: Support Vector Machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Multilayer Perceptron (MLP). The results show that the Random Forest algorithm achieves the best performance, with an accuracy of 84%, precision of 86%, recall of 87%, and F1-score of 86%. The implementation of this system serves as a concrete step toward enhancing the efficiency, sustainability, and modernization of hydroponic agriculture in Indonesia]]>
