Implementation Internet of Things Using Linear Regression Method For Supply Chain Management System
Abstract
The Indonesian ministry of the industry stated that the development of industry must be a formidable industrial country in the world by 2025 and has carbon neutrality by 2060. This goal must be considered related to the distance of the supplier location from the industrial and it increases the gas emissions in the air. In this research, the proposed a solution for optimizing the delivery of material supplies from suppliers to the industry. The protocol communication uses the MQTT protocol to connect the device with a broker. This system will collect the information from sensors such as DHT 11, MQ-05, RFID, and other sensors to record the data, track the location, and validate the person accessing the goods and the data in a document. The data can be used as decision-making material using the regression linear method specifically at humidity and temperature. This device will connect to the user's smartphone by using an application that is connected by an online MQTT broker and see the condition of the goods, track the location of the delivery, and control the device from anywhere. The experiment result shows that the device can monitor temperature, humidity, CO2, LPG gas, and smoke gas. It also used the linear regression method used for determine the impact of the correlation between temperature and humidity at supply storage. The model summary states that the slope (B1) value is -1.8637 with a constant (B0) of 110.1554. B1 has a negative value. So, it can be concluded that the temperature variable negatively correlates with humidity. This method will be useful for the system to do some action to stabilize the condition inside the supply room such as giving a notification to the driver or turning on several pieces of equipment automatically to recover the condition. The proposed system is an IoT system using the openHAB platform as a UI that will be used in this system with a linear regression method to find the relationship between system parameters.
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