RANCANG BANGUN SISTEM DETEKSI KEMATANGAN BUAH KELAPA SAWIT BERDASARKAN DETEKSI WARNA MENGGUNAKAN ALGORITMA K-NN - Ade chandra Saputra; Enny Dwi Oktaviyani (Universitas Palangka Raya, 2023)
The rapid growth of the palm oil industry has made it increasingly important to develop applications that can detect the maturity level of oil palm fruit. This paper presents the design and development of an application for detecting the maturity level of oil palm fruit based on color composition using the K-NN algorithm. The K-NN algorithm is used to classify the oil palm fruit based on the color composition that is related to its maturity level. The application uses image processing technology to measure the qualitative and quantitative parameters of various maturity indicators, such as color, size, and texture. Different color compositions of the oil palm fruit indicate different maturity levels, and using the K-NN algorithm, the fruit can be classified based on its maturity level. The application helps reduce production costs and losses caused by errors in harvesting the fruit. The application is designed to be user-friendly and accessible to farmers and plantation managers. The user interface is simple and intuitive, allowing users to easily input the image of the oil palm fruit and get a quick analysis of its maturity level. The results are displayed in a clear and understandable way, making it easy for users to make informed decisions about when to harvest the fruit. In conclusion, the application for detecting the maturity level of oil palm fruit based on color composition using the K-NN algorithm is a useful tool in the palm oil industry. It helps farmers and plantation managers determine the optimal time for harvesting the fruit, reducing production costs and increasing productivity. The user-friendly interface makes it accessible to a wider range of users and facilitates informed decision-makin