PENERAPAN ALGORIMA K - MEANS CLUSTERING PADA PENGELOMPOKKAN INVESTOR INVESTASI REKSADANA - Stephen Kurnia, (2023)
In the current technological era, there are many roles in the use of technology in all areas of people's lives, especially in financial and economic activities. One of the investment digital products currently circulating is Mutual Funds. Mutual funds are a digital investment product that has minimal risk and can be used by people who are beginners to learn and start investing. There are several types of Mutual Funds that are currently being sold to the public, such as Fixed Income Mutual Funds, Money Market Mutual Funds, Mixed Mutual Funds, Sharia Mutual Funds and Stock Mutual Funds. Because there are so many types of mutual funds that exist today, investors are often confused about choosing a mutual fund investment with good profits and the safest risk. Especially with Investors who are still beginners in investing activities. Therefore, a study was conducted entitled "Application of the K - Means Clustering Algorithm in Grouping Mutual Fund Investment Investors" with the aim that Mutual Fund providers can find out the Mutual Fund products that have the safest risk profile and also the benefits obtained according to the needs of Investors in finding mutual fund products that are suitable for themselves and also Investors can find out the risk profile of the Mutual Fund investments they take. The method used in this study uses the K - Means Clustering Algorithm with a dataset originating from OJK Mutual Funds with a total of 1529 data which aims to classify investor profile types using a value of K = 3 with cluster types being types of Investors with their risk profiles in investing , then the cluster evaluation process is carried out using the Silhouette score method. The results obtained from this study are data visualization in the form of a graph of the cluster results of the mutual fund investor risk profile group and then calculating the average to facilitate the analysis process and evaluating the cluster value with the Silhouete score which produces a score for clustering k = 3 which is 0.8283326733985543