MACHINE LEARNING TINGKAT KEMATANGAN BUAH NANAS SUBANG BERBASIS INTERNET of THINGS MENGGUNAKAN METODE K-MEANS PADA PLATFORM THINGSPEAK: MACHINE LEARNING TINGKAT KEMATANGAN BUAH NANAS SUBANG BERBASIS INTERNET of THINGS MENGGUNAKAN METODE K-MEANS PADA PLATFORM THINGSPEAK - Timbo Faritcan Parlaungan S.; Fajar Fajar (STMIK SUBANG, 2021)
Identification of pineapples carried out by farmers is still done manually, so the importance of accuracy in determining the level of maturity of pineapples is needed for farmers. This is because many farmers do not know how ripe the pineapples they plant are. Therefore, pineapple farmers can only make visual observations or directly on pineapples which will be classified according to the level of maturity, of course this process has many obstacles, this is because human nature itself has weaknesses which ultimately lead to a lack of quality in sorting between ripe and immature fruit, therefore a tool and system for the maturity level of pineapple is made using the TCS3200 color sensor and loadcell sensor, besides that this tool is equipped with a system or website using the K-Means algorithm so that later farmers can see the number of fruits raw pineapples and ripe pineapples that have been grouped through a website that is made specifically to classify pineapples using the K-Means method based on their level of maturity, it will certainly be very helpful for pineapple farmers.