![]() The results of alternative classifications based on K-Means on very strategic centroids of 27 locations or covering 22.31%, strategic several 77 locations or covering 63.64%, and non-strategic 17 locations or covering 14.05%. ![]() ![]() In addition to presenting calculations using spreadsheets, this research also produces implementations in web-based software. The application of the K-Means Algorithm in this study uses 6 clustering criteria, namely ATMs Usage, ATMs Fee-Based Income/ FBI, ATMs Service Level Agreement/ SLA, ATMs distance, competitor ATMs, and Business Distance. ![]() This study uses data sourced from the BNI ATM database and performance data in one semester or six months, namely January to June 2021, as many as 121 ATM locations spread across the island of Bali. This study aims to evaluate the placement of ATM locations using the K-Means method, and research using data obtained from data mining to obtain decisions that lead to not strategic, strategic, and very strategic ATM locations. The existence of an ATM location requires a placement evaluation that aims to support business and provide convenience and comfort to customers when using or transacting. ![]()
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