Implementation of Local Mean Distance Weighting k-Nearest Neighbor in Determining Vocational High School Majors in Pekanbaru

Abstract

Vocational High School is a formal education at the secondary education level. SMK X Pekanbaru is one of the private vocational schools in Pekanbaru that provides IT-based education. There are 5 (five) majors in SMK X Pekanbaru, namely Computer and Network Engineering (TKJ), Software Engineering (RPL), Accounting and Financial Institution (AKL), Office Automation and Governance (OTKP), and Online Business and Marketing (BDP). During the registration period, prospective SMK students will enter their score data and will also choose the majors they want to take. In the absence of a system that can determine majors for prospective SMK students, obstacles will arise including errors in determining majors and requiring a long time to process prospective student data. Based on the above problems, a system will be built that can speed up and simplify the determination of new student majors by using Supervised Learning algorithms in Machine Learning, namely Local Mean Distance Weight k-Nearest Neighbor (LMDWkNN). Based on the results of the confusion matrix testing carried out, the accuracy results were 88.89%, the precision, recall and F1 score were 89%, which states that the model is good enough to determine majors