Inventories are materials or products that are owned and stored for future use by the company. Inventory control is a prime factor in the operation of a company because it affects various things, such as profits, costs, and corporate planning and strategy. However, several problems are often experienced, for example, excess or lack of stock in storage. These problems arise because of the mismatch in the number of pre-orders (PO). This research proposes to overcome the problem with a data mining approach for grouping fast-moving and slow-moving products by applying k-medoids clustering with the Python programming language. The research location is PT. Lenko Surya Perkasa Branch Office Sidoarjo. K-Medoids method is applied to the company's data from January 2018 to July 2020. Then, it will group the data based on several parameters, namely the number of products sold each year or period, the number of sales transactions per year or period, products that are damaged or returned every year or period, inventory turnover ratio (TOR) partial or annually, and days inventory (DIS) or inventory saving time (WSP). The result obtains the grouping works with 5 clusters, according to the request of the company in 2018, 2019, and 2020 datasets. In 2018, C1 is slow-moving, C2 is fast-moving, C3 is super slow-moving or non-moving, C4 is fairly slow-moving, and C5 is fairly fast-moving consisting 61, 25, 2, 2, and 1 member with a silhouette score of 0.547. In 2019, C1 and C2 are super slow-moving or non-moving, C3 is slow-moving, C4 is fast-moving, and C5 is fairly fast-moving containing 65, 1, 1, 22, and 2 members respectively with a silhouette score of 0.654. In 2020, C1, C2, and C5 is super slow-moving or non-moving, C3 is fairly fast-moving, C4 is slow-moving consisting 84, 3, 1, 2, and 1 member for C1, C2, C3, C4, and C5 respectively with a silhouette score of 0.75. From the results obtained, k-medoids clustering can classify health products at PT. Lenko Surya Perkasa Branch Office Sidoarjo into 5 clusters that can assist company management in determining appropriate inventory recommendations.