Perancangan Aplikasi Smart Notes untuk Monitoring Alat Praktik Kelistrikan Berbasis Machine Learning
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Abstract
The digital transformataion of the Industrial 4.0 era demands intelligent monitoring systems to enhance the reliability and efficiency of electrical equipment, including within technical education environments. The main problem encountered is that monitoring of practical electrical equipment is still performed manually through logbook entries, which are prone to errors, data loss, and delays in fault detection. This study aims to develop a Smart Notes application that integrates user-generated notes with Internet of Things (IoT)-based sensor data and a machine learning predictive model to support real-time preventive maintenance. A quantitative approach was employed by analyzing parameters such as current, voltage, temperature, power, and frequency using rule-based algorithms and drift detection methods to identify anomalies. The results show that the system can classify equipment conditions into three main statuses NORMAL, ATTENTION, and OVERLOAD with high reliability while providing additional context through user narrative input. It is concluded that the integration of sensor data and user notes in Smart Notes effectively improves monitoring accuracy, maintenance efficiency, and digital literacy in technical education laboratories.
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