Penerapan Data Mining Korelasi Penjualan Spare Part Mobil Menggunakan Metode Algoritma Apriori (Studi Kasus: CV. Citra Kencana Mobil)

By utilizing customer data that has been stored in the database, the management can find out how the current sales system is less efficient, therefore a system is needed to process information data more quickly and accurately in increasing sales of car spare parts using the Data Mining application. The Apriori Algorithm method that works by searching for and finding associated patterns among the products being marketed, so that later it can help companies improve the associated items. And with the sales transaction data, the company can know better how they should increase the spare part stock in the company. From the results of testing the sale of car spare parts with 589 data, it was found that 81 rules were formed and the highest Best Rule was obtained and a minimum support value of 1% and a confidence value of 11% If the type of car is Avanza / Xenia and the brand is Toyota, the spare parts used are filters. Air. With supporting spare parts in the database of 1% and certainty of spare parts of 11.

Rekomendasi Paket Menu Angkringan Waru Tanjung Bias Dengan Algoritma Frequent Pattern Growth Berbasis Web

Culinary business using carts selling various kinds of heavy food, light and drinks, is favored by many people to just fill their stomachs, gather with friends and even family. Culinary businesses or culinary destinations like this are known as Angkringan which are increasingly mushrooming in the millennial generation. Angkringan Waru, located in Tanjung Bias, is a gathering destination for all people to enjoy a relaxed atmosphere on the beach. Angkringan Waru provides 85 types of menus for its customers, the many menus often confuse customers in choosing snacks while enjoying the beachside atmosphere. Starting from these problems, data mining techniques are used with the Frequent Pattern Growth (Fp-Growth) algorithm to recommend items in producing a menu package consisting of 1 snack item and 1 drink item. The dataset used is transaction data from Angkringan Waru as many as 870 transactions, the resulting output is a menu package recommendation rule and implemented in a web for Angkringan Waru. The Fp-Growth Data Mining Application by providing a minimum support value of 20% and Confident 50% with a lift ratio > 1 produces 57 rules or menu package recommendations that will be offered to Angkringan Waru customers. The results of the application in the form of 57 menu package recommendations are then used as recommendations for Angkringan Waru customers, where these menus are the favorite menus of customers at Angkringan Waru.