Design and Development of Job Recommendation System Based On Two Dominants On Psychotest Results Using KNN Algorithm - Joshua Suharyadi; Adhi Kusnadi (Universitas Multimedia Nusantara, 2018)
Employees are an important factor in the progress of a company. Employees with good performance will certainly provide positive results for the company. One that can determine employee performance is the right placement in the job. To find out the right placement in a job, one way can be done psychologically. Psikotes can help to know the nature of an employee and suitable work based on their nature. The construction of a job recommendation application system was created to help prospective employees know their true identity and suitable work so that they can apply according to their expertise. This system is built with the programming language PHP, Javascript, HTML for web-based platforms and the KNN algorithm as the method. The KNN algorithm is used to measure the closest distance between training data and test data to produce job recommendations. Training data is taken from expert, and book references. System trials are given to users by filling in psychological tests and questionnaires regarding the satisfaction of system use. After getting feedback from users, the value of system satisfaction reached 85%. This states that the system can provide job recommendations that are in accordance with the psychological test results of the user.