<![CDATA[The rapid development of AI is also supported by our entry into the digital era and the Internet of Things (IoT). In using laboratory equipment, students are required to comply with the rules so that the equipment can be maintained properly. However, the tool used will cause thirst for the tool. This becomes a problem when the tool is needed for learning, while the tool does not function properly or is completely damaged. The research method used in Predicting Labor Equipment of the Department of Electronics FT-UNP is the Cross-Industry Standard Process for Data Mining (CRISP-DM) development method. CRISP-DM is a very reliable model. By utilizing Predictive Maintenance technology on each existing equipment, we can analyze data to identify damage with failure mode, so as to obtain data regarding the frequency of occurrence of damage and the severity of damage to labor equipment and setting a warning for labor technicians, at this time there will be an alarm for technicians to perform checking of equipment.]]>
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