Implementation of Data Mining for Interpretation of KSE Scholarship Applicant Number Data using Naive Bayes Algorithm - Purnama, Riyan Hidayah; Ikhwan, Ali (Forum Kerjasama Pendidikan Tinggi, 2024)
The purpose of this study is to interpret the large number of KSE scholarship applicant data, which is expected to provide a positive contribution in developing the KSE scholarship branding strategy, optimizing resource allocation and increasing the attractiveness of companies to allocate their CSR funds to the Karya Salemba Empat Foundation using data analysis techniques. The problem currently experienced is that the Karya Salemba Empat Foundation has been selecting KSE scholarship recipients manually, which results in the decision-making process not being able to be carried out quickly, accurately and efficiently. As one way to improve data accuracy, a method or computational model is needed in the form of a machine learning algorithm using the Naive Bayes method. With this Naive Bayes method, it is very appropriate to use to produce Knowledge. This study shows how the Karya Salemba Empat Foundation can utilize data to increase its value. From the results of the test carried out using 4,492 rows of data and 6 data variables and the pattern accuracy of 92% with an error margin of 8%, it shows that the naïve bayes method is almost perfect in processing its data. The results of this study are expected to provide in-depth insight into how the application of data science can help the Karya Salemba Empat Foundation increase its appeal and strategy