K-Means for IT Asset Segmentation and Demand Forecasting Using Double Exponential Smoothing (DES) - Anita aprilianti; Ben Rahman (LPPM Universitas Islam 45 Bekasi, 2026)
IT asset management was a crucial aspect in supporting the smooth operation of a company. Poorly planned asset procurement resulted in asset shortages or excesses, which impacted cost efficiency. Frequent problems included the lack of asset grouping based on needs and difficulties in forecasting future IT asset demand. This study aimed to group IT assets using the K-Means method and to forecast IT asset demand using the Double Exponential Smoothing method. Asset grouping was used to assist companies in determining asset procurement priorities. This study used historical IT asset demand data for the period January 2024 to February 2025. The K-Means method was applied to group assets into three categories: submitted, need to consider, and not submitted. The Double Exponential Smoothing method was employed to forecast future IT asset demand by measuring the error rate using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). The results showed that the K-Means method helped companies determine IT asset management priorities, while the Double Exponential Smoothing method produced asset demand forecasts with low error rates, thereby supporting more accurate IT asset procurement planning.