<![CDATA[Population growth rate in Indonesia keeps growing each and every year. The rapid growth of population may give impacts on many aspects of the country, such as economic development, quality of life and health issues of the residents, and even educational problems. To anticipate the negative effects of population growth, projection of future population is needed as to help the government to develop city development plans. K-Nearest Neighbor (KNN) is one of many methods that could be used to predict future values, be it for classification or regression. KNN Regression is a KNN algorithm used for regression or forecasting problems. In this study, the KNN Regression method is implemented to forecast future population of Malang city, using a time series of monthly population growth consisting of 73 datas in total. The forecasting method starts with preprocessing the time series, calculate the distance between each training and testing data, and estimate the predicted value based on k nearest neighbors. From the testings done in this study, the lowest Mean Absolute Percentage Error (MAPE) value obtained is 0,02526%.]]>
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