This research aims to improve the efficiency of data transmission to wireless sensor networks in agriculture by compression using the IoT-based Python programming language. This research provides novelty in the field of Internet of Things (IoT) and Wireless Sensor Networks (WSN) for smart agricultural systems, especially in terms of bandwidth optimization and data compression by proposing a Python-based data compression method. This study uses a soil moisture sensor to measure soil moisture as a source of experimental data, which will collect data for 12 hours within 7 days. This research succeeded in significantly reducing the size of the data transmitted from the field sensor to the data center, without sacrificing the accuracy of the data needed to monitor crop conditions. The results of the comparison of data before and after compression can be seen in the boxplot diagram and bar chart to see the average. From the following research results, it opens up opportunities for optimizing bandwidth usage and reducing operational costs in IoT-based agricultural systems.
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