Klasifikasi Penjualan Makanan Cepat Saji Menggunakan Metode Algoritma C4.5 (Studi Kasus : Ayam Penyet Nabila)

Abstract

The availability of sales data at the Ayam Penyet Nabila outlet is not used as much as possible, so that the sales data is not utilized maximally to find out what foods are most preferred by consumers or best-selling. The purpose of this research is to apply the C4.5 Algorithm method at the Penyet Nabila Chicken outlet and hopefully it can produce a knowledge in the form of a classification of food sales which is most preferred by many customers from existing datasets (in demand and in demand). The method used to classify food sales is C4.5 Algorithm, the process uses the five-step rule in KDD (Knowledge Discovery in Databases), which includes several activities namely selection, preprocessing, transformation, data mining, interpretation and evaluation. Besides making calculations in the form of a manual, I also tested this research using the RapidMiner tool. From the research output looking for decision tree results using the C4.5 Algorithm method the entrophy value is generated and for the highest gain value is 0.463363648 on the Sold Amount attribute in manual calculations. Whereas using the RapidMiner tool generated a decision tree as shown in Figure 4.3 Amount Sold - Price - Menu Name. The results of this study are the best-selling sales of Penyet Chicken, Grilled Chicken, Serundeng Chicken. Research using the C4.5 Algorithm method with the RapidMiner tool has an accuracy value of 86.40%, precision 86.29%, recall of 90.53%.

Keywords: C4.5 Algorithm, Data Mining, RapidMiner, Classification.