K-Nearest Neighbor Algorithm for Predicting Land Sales Price

Tua Holomoan Harahap

Abstract


Until now, there are still many people who have difficulty making choices in choosing strategic land at a price according to their abilities due to lack of knowledge about land prices based on market prices. Based on these problems, the design and manufacture of applications that can be used to predict the selling price of land with the K-Nearest Neighbor (KNN) algorithm approach. This application is expected to provide more accurate and efficient information about the selling price of land and help prospective buyers or sellers of land to predict the value of land according to the specified criteria. The data collected is secondary data. The method used is a combination of data mining stages known as the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the Waterfall Model software development method. Overall, this application is able to predict land value with a fairly long processing because the KNN algorithm is basically comparing testing data (new data) with training data (old data) one by one. The accuracy of the testing data prediction is 80%.


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References


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DOI: https://doi.org/10.55311/aiocsit.v3i2.235

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