Penerapan Algoritma Apriori Dalam Memprediksi Hasil Penjualan Sparepart PC (Studi Kasus : Toko Sentra Computer)

Abstract

Technological advances are increasing and becoming an important need for everyday life both in the fields of education, economics, social, culture and politics. One of the technologies needed by society is computers. Sentra Computer is a store engaged in the sale of computers. The researcher has conducted research at the Computer Center Store and found a problem when the stock of PC spare parts that cost more than Rp. 450,000 is often insufficient because the stock is bought with a small amount so that capital can be used on a cheaper PC spare part. The purpose of this thesis research is to find out the sales data of PC spare parts at the Sentra Computer Shop which are most purchased by consumers. The methodology used is a priori algorithm by calculating the value of support and the value of confidence carried out manually and computerized using rapidminer. The result is that the Sentra Computer Store owner can provide sufficient stock.

Classification of Drug Data Usage Using the K-Means Deep Algorithm to Minimize Drug Stock Shortages (Case Study: South Cikarang Community Health Center)

“…Applying association methods like the Apriori algorithm in analyzing consumer shopping patterns and purchase behaviors can provide valuable insights into how drugs are procured and utilized [10] [11]. Healthcare centers can proactively adjust their stock levels based on historical data and predicted trends by extrapolating these methodologies to drug inventory management [12] [13]. Integrating data mining techniques, particularly algorithms like Apriori and advanced algorithms such as K-Means Deep, offers a promising approach for healthcare facilities to optimize drug inventory management.…”