Penerapan Data Mining untuk Klasifikasi Hasil Panen Jamur Tiram Menggunakan Algoritma K-Nearest Neighbor
DOI:
https://doi.org/10.30865/mib.v7i1.5252Keywords:
Data Mining, Yields, Oyster mushroom, Classification, K-Nearest NeighborsAbstract
Oyster mushroom is a type of mushroom that can be consumed by humans. Lots of food products are made from processed oyster mushrooms. This makes mushroom farmers intensively cultivate oyster mushrooms because they see good economic prospects. However, not all mushroom cultivation processes can be successful so it will have an impact on the yield of the oyster mushrooms. So it is necessary to classify so that it is easier for mushroom farmers to determine the amount of yield from the oyster mushroom. The classification was carried out because of the difficulty of mushroom farmers in determining the amount of harvest by looking at the width of the mushroom caps, the number of mushroom caps to the mushroom harvest time. This study proposes a data mining technique to classify oyster mushroom yields using the K-Nearest Neighbors algorithm so that it can help mushroom farmers in determining the yield of oyster mushrooms being cultivated. This study used a dataset of 42 mushrooms as training data and 1 mushroom data to determine the classification of the crops. From the results of testing on 1 mushroom with a cap width of 8 cm, the number of caps is 14 pieces and the harvest time is 49 days, the results of classification results obtained from this mushroom are Less with a Mean absolute error of 0.1419, Root mean squared error of 0.2111, Relative absolute error of 36.2177% and Root relative squared error of 48.002%. The results of this research can help mushroom farmers in classifying oyster mushroom yields.References
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