Implementasi Internet of Things dan Deteksi Anomali Menggunakan Algoritma Deep Learning Pada Distribusi Buah Melon

Abstract

Melon is the horticultural plants that had the potential to improve Indonesia's economy in the agricultural sector. Efforts to improve the economy must been accompanied by improvements in the quality of melon fruit production and distribution to reach consumers. One way to maintain the quality of melon fruit is to combine production and distribution processes with the use of temperature sensors. Utilizing temperature sensor with Internet of Things (IoT) technology to monitor melon temperatures during the distribution process is a form of technological innovation. This research aims to develop a melon distribution system by applied IoT devices to monitor environmental temperature and detect anomalies before transferring data to blockchain system. The anomaly detection method in this research uses deep learning algorithms. Autoencoder was chosen as the architecture model in this research because this method can help minimize data anomalies. The results of this research indicate that IoT technology and anomaly detection were successfully implemented and performed very well. Based on performance testing using quality of service parameters, the throughput was 86,152 bps, the delay was 0.041199 ms, and the packet loss was 0.064%. The evaluation results of anomaly detection model for precision, recall, and F1-score were 0.9952, 1, and 0.9658.