Weather Forecasting Based on Supervised Learning Using K-Nearest Neighbour Algorithm
Kata Kunci:k-Nearest Neighbou r(k-NN), weather forecasting, Locality Sensitive Hasihing (LSH)
AbstrakAbstractWeather is influenced by many natural factors causing it to change frequently at any time so that it is sometimes difficult to predict. An accurate weather prediction is needed so that people and policy-makers could anticipate this problem. Many factors that influence the weather cause difficulty in classifying the weather on a particular day. Locality Sensitive Hashing (LSH) works on training data by assigning hash values to a vectors that contain values that represent factors that affect weather and perform weather classification. Furthermore, the k-Nearest Neighbor (k-NN) algorithm will calculate the predictions of the factors that affect the weather on a certain day. Based on the tests carried out, k-NN and LSH in weather prediction has Mean Square Error (MSE) 0,301. Keywords— k-Nearest Neighbou r(k-NN), weather forecasting, Locality Sensitive Hasihing (LSH
Data unduhan belum tersedia.
Utami, A. S., Rini, D. P., & Lestari, E. (2021). Weather Forecasting Based on Supervised Learning Using K-Nearest Neighbour Algorithm. JUPITER (Jurnal Penelitian Ilmu Dan Teknologi Komputer), 13(1), 09–16. Diambil dari https://jurnal.polsri.ac.id/index.php/jupiter/article/view/3255