Implementasi Algoritma Knuth-Morris-Pratt pada Sistem Tracer Study Teknologi Informasi Universitas Sumatera Utara

Abstract

Tracer study is an essential activity for tracking graduate information, including employment status and job-seeking duration, which is highly valuable for program evaluation and accreditation processes. However, the low participation rate of alumni in manually filling out tracer forms results in incomplete and unrepresentative data. This research aims to develop a web-based tracer study system that can automatically collect and display alumni information from the LinkedIn platform using web scraping techniques. The collected data is stored in a database, and users can perform keyword based searches. In the search process, the Knuth-Morris-Pratt (KMP) algorithm is implemented to efficiently match keywords with alumni data. The system is developed using Python, Selenium, and BeautifulSoup, with data stored in MySQL. Testing results show that the system can accurately display alumni data with an average search time of under 0.1 seconds, and a 100% match accuracy in black-box testing. Thus, this system can serve as an alternative tool to support tracer study data collection in a more efficient, fast, and structured manner.