Internet of Things (IoT) and Telegram Based Palm Fruit Ripeness Detection System With K-Nearest Neighbor (K-NN) Algorithm
DOI:
https://doi.org/10.37859/coscitech.v7i2.12121
Abstract
Abstract
This research aims to overcome the difficulty in determining the level of maturity of oil palm fruit visually at PT Agro Muara Rupit by developing a more accurate fruit maturity detection system. This often results in suboptimal harvest results, there are some fruits that are harvested not always in a ripe state. As a result, yields are often not optimal, potentially causing losses for the company. To solve this problem, the research methods used include data collection through interviews, observations, and literature studies. The developed system is designed using a TCS3200 color sensor to detect fruit color, which is then processed by a NodeMCU microcontroller. RGB data obtained from the sensor will be calibrated and analyzed using the K- Nearest Neighbor algorithm to determine the level of ripeness based on the detected color sample data. The output of this system will be sent to the user via the Telegram application, the test results show that the developed system has an accuracy rate of 85% in detecting the maturity of oil palm fruit, with a value of K = 1. The application of Internet of Things (IoT) technology and the K-Nearest Neighbor algorithm is expected to increase efficiency and accuracy in detecting the maturity of oil palm fruit. Thus, this system will help optimize crop yields and reduce losses due to harvesting of immature fruit at PT Agro Muara Rupit.
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