Sistem Klasifikasi Kesiapan Panen Tanaman Bawang Merah Berbasis Fitur Histogram
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Institusion
Institut Teknologi Telkom Purwokerto
Author
Angga, Bagus Prawira
Subject
TA Engineering (General). Civil engineering (General)
Datestamp
2022-09-08 08:06:37
Abstract :
Onions are one of the main commodities in the market for raw materialsand spices. One of the onions that has a high need and demand is Shallots. Shallotsare widely cultivated by farmers because they have a wide market and are easy tocultivate. In the cultivation of shallots, it will be difficult to know the level of maturity or harvest readiness of the plant. It takes an intelligent system as a mediumto help farmers in the harvest process. This study uses a camera as a medium totake the image of the shallot plant. The purpose of this study is to test the accuracyof the detection results of an object which is divided into two categories, namelyShallots "Ready to harvest" and "Not ready to harvest" by using histogram featuresas a feature extraction method and K-Nearest neighbor as an image classificationmethod, detection Shallots were tested by calculating accuracy, precision andrecall to assess whether the system was used or not. The test results obtaineddetection accuracy results of 96.5%, precision results of 94.5% and recall resultsof 94.9%. These results indicate that the system model created works well enoughto determine the readiness of the onion plant to harvest. Keywords: accuracy, Histogram, K-Nearest Neighbor, precision, recall, Shallots
Onions are one of the main commodities in the market for raw materialsand spices. One of the onions that has a high need and demand is Shallots. Shallotsare widely cultivated by farmers because they have a wide market and are easy tocultivate. In the cultivation of shallots, it will be difficult to know the level of maturity or harvest readiness of the plant. It takes an intelligent system as a mediumto help farmers in the harvest process. This study uses a camera as a medium totake the image of the shallot plant. The purpose of this study is to test the accuracyof the detection results of an object which is divided into two categories, namelyShallots "Ready to harvest" and "Not ready to harvest" by using histogram featuresas a feature extraction method and K-Nearest neighbor as an image classificationmethod, detection Shallots were tested by calculating accuracy, precision andrecall to assess whether the system was used or not. The test results obtaineddetection accuracy results of 96.5%, precision results of 94.5% and recall resultsof 94.9%. These results indicate that the system model created works well enoughto determine the readiness of the onion plant to harvest. Keywords: accuracy, Histogram, K-Nearest Neighbor, precision, recall, Shallots
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