Evaluation of ATM Location Placement Using the K-Means Clustering in BNI Denpasar Regional Office - I Putu Suwirya; I Made Candiasa; Gede Rasben Dantes (Journal of Computer Networks, Architecture and High Performance Computing, 2022)
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. 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. 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. 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. In addition to presenting calculations using spreadsheets, this research also produces implementations in web-based software. 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%. Although the location of ATMs that are classified as non-strategic criteria is quite small, this can be optimized by several strategic steps that can be taken by stakeholders within the company, such as evaluating the bank's business plan in 2022 and improving the supervision of machines at ATM locations.