Sinkron : Jurnal dan Penelitian Teknik Informatika Volume 8, Number 2, April 2023 DOI : https://doi.org/10.33395/sinkron.v8i2.12224 e-ISSN : 2541-2019 p-ISSN : 2541-044X *Eko Bambang Wijayes This is a Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. 741 Comparison of the K-Means Algorithm and C4.5 Against Sales Data Abdi Dharma 1)* , Eko Bambang Wijaya 2) , Daniel Heyneker 3) , Jeff Vanness 4) 1,2,3,4) Universitas Prima Indonesia, Indonesia abdidharma@unprimdn.ac.id, 1)* ekowijaya0904@gmail.com, 2) danielheynekertalk@gmail.com, 3) jeff.vanness999@gmail.com 4) Submitted : Feb 27, 2023 | Accepted : Mar 22, 2023 | Published : Apr 1, 2023 Abstract: In general, the process of collecting and grouping data requires a long process. And if it has to be grouped manually it takes a very long time. Therefore, data mining is a solution for clustering data - a lot of data to classify it. In this research conducted at CV.Togu - Togu On Medan Branch, data mining is applied using the K-Means process model and the C4.5 algorithm which provides a standard process for using data mining in various fields used in classification because the results of this method easy to understand and easy to interpret. . The K-means method is a non-herarical method which is an algorithmic technique for grouping items into k clusters by minimizing the distance of the SS (sum of square) to the cluster centroid. In the K-means method, the number of clusters can be determined by the researcher himself. And the testing methods used to measure cluster quality are the Silhouette Coefficient and the Elbow Method. Based on the research conducted, there are significant differences before and after using the two methods. The results of the K-Means algorithm will be compared with the results of the C4.5 algorithm in the form of rules (decision trees). This research produces data on goods that have the highest level of sales/behavior. Keywords:Data Mining; K-Means; Elbow Method; Silhouette Coefficients; C4.5 algorithm. INTRODUCTION Research (Fani Mulyana Nasution, 2019) applies the K-Means algorithm to rank food security with the aim of increasing food crop production in urban and local communities in North Sumatra province, technically. This is used to identify areas with food security potential to support food demand using the method K-Means examined based on harvested area, yield and planted area, the use of the K-Means method aims to classify areas with high, medium and low yields of food crops. This study leads to the grouping of several food crops with a total of 3 clusters, where the first cluster is a group with high food security potential, the second cluster is a group with moderate food security potential and the third cluster is a group with moderate food security potential. low food security potential. security potential. potential for food security [12].This study (Gabriella Amelia Prasetyo, R. Gunawan Santosa 2019) was conducted to compare the prediction accuracy of the C4.5 and k-Means algorithms in predicting the first semester GPA of UKDW FTI students. The data used is the 2008-2016 UKDW FTI Student Dataset as training data and the 2017 Batch as test data. The attributes used will be distinguished according to the paths achieved and not achieved. Passing paths use category, state, location and ICE level attributes, while non-passing paths use type, state, location, ICE level, number, speech attributes, space, similar. Accuracy will be calculated by cross table. The C4.5 algorithm achieves the best result of 77.45 n the k-Means algorithm achieves the best result of 60.78%. Scenarios with successful routes get an average accuracy score of 55.27n. Conditions with incomplete paths have an average accuracy score of 38.95%. [13]