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Forestry, agriculture, and water resources of countries are influenced by rainfall. Therefore, the need to predict the rainfall is a must. K-Nearest Neighbors is a supervised learning classification technique that seeks to categorize new objects according to their attributes and training set. The aims of this study are to determine the accuracy of rainfall prediction using KNN Methods with Python. This study found that the accuracy of rainfall prediction using KNN Methods with Python is 83.33% by removing data with values 8888 and 9999, then changing the NaN data to mean according to that parameter, and 76.66%. with changing data that is worth 8888 and 9999 to NaN.

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Machine Learning Implementation with K-Nearest Neighbor (KNN) Algorithm to Predict Rainfall in Tangerang Regency

  • 会议论文

  • 发表日期：

    2025年

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  • Veronica Yose Ardilla,
  • Timothy Daniel Pangestu,
  • Marco Suteja,
  • Simon Prananta Barus

摘要

Forestry, agriculture, and water resources of countries are influenced by rainfall. Therefore, the need to predict the rainfall is a must. K-Nearest Neighbors is a supervised learning classification technique that seeks to categorize new objects according to their attributes and training set. The aims of this study are to determine the accuracy of rainfall prediction using KNN Methods with Python. This study found that the accuracy of rainfall prediction using KNN Methods with Python is 83.33% by removing data with values 8888 and 9999, then changing the NaN data to mean according to that parameter, and 76.66%. with changing data that is worth 8888 and 9999 to NaN.