K-Nearest Neighbors (KNN) to Determine BBRI Stock Price

Sales prediction is a calculation aimed at forecasting future conditions by analyzing past situations. The research method used in this study is the Research and Development (RnD) method. The modeling employs the K-Nearest Neighbor algorithm, utilizing data processed through the Knowledge Discovery in Database (KDD) stages. The objective of this research is to obtain a predictive model that can preprocess structured product data, enabling it to present a forecast for the public regarding the general overview of BBRI stock price determination, as well as to provide recommendations for BBRI stock prices that have been classified by the researcher using the K-Nearest Neighbor method. The results of the stock price prediction indicate fluctuations in value during the period, where the model is capable of capturing trends in stock price changes based on historical data. For example, on February 10, 2025, the stock price is predicted to be 4867.020, while on February 15, 2025, it rises to 5101.620. This demonstrates that the k-NN method can analyze stock price movement patterns by considering the nearest neighbors from previous data. The k-NN method has proven effective in studying historical data patterns and generating structured predictions.