Apriandy Angdresey, Lanny Sitanayah, Tjia Valentyno Nathaniel Kairupan, Timothy Matthew Immanuel Sumajow
The Internet of Things changes how people perform their daily activities. With data mining techniques, the Internet of Things assists users to analyze certain objects by performing computations on sensed data. This article presents two monitoring and recommendation systems based on the Internet of Things, that is, for aquarium and ornamental plants. For each system, a sensor device is designed and implemented by taking into account the physical phenomena that the system needs to analyze. For the aquarium monitoring system, the sensor device senses water temperature, turbidity, and water level. For the ornamental plant monitoring system, the sensor device senses soil pH, soil moisture, air humidity, and temperature. Web-based applications utilize the data mining’s C4.5 classification algorithm to give recommendations for users regarding the real-time conditions of monitored objects. Based on the performance evaluation on training data, the aquarium and ornamental plant monitoring systems can achieve as high as 97.8% and 89.6% accuracy, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Universitas Katolik De La Salle Manado, Manado, Indonesia
