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Journal : Bulletin of Computer Science Research

Penerapan Algoritma BM25 dalam Pencarian Lowongan Pekerjaan pada Website Job Portal Kheng, Tek; Asri, Jefry Sunupurwa; Wahyu, Sawali; Yulhendri, Yulhendri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.760

Abstract

The development of the digital era has grown rapidly all the time which has significantly changed the job search process for job applicants, making Online Job Portals one of the main places in human resource recruitment activities, however, the effectiveness of Job Portals Job search still has fundamental weaknesses such as the job search technology used still uses simple string matching which can cause less relevant search results and reduce the quality of user experience in applying for jobs. This study was conducted to improve the quality of job vacancy search results on Job Portal A Career by applying the Okapi BM25 algorithm. This research method uses a Rapid Application Development (RAD) development approach, such as designing a client server architecture with Next.js as the frontend, ASP.NET Core as the backend and PostgreSQL as the main database. The BM25 algorithm is integrated directly into the database using the VectorChord BM25 extension to calculate the search relevance score with the user inputted query. In testing with the query “accelist the quality support career IT need”, the system displays 800 of 1,011 documents (79.13%) with a non-zero relevance score. Furthermore, evaluation through User Acceptance Testing (UAT) showed a user satisfaction rate of 91.2%, confirming that BM25 is capable of displaying the most relevant results at the top of the rankings and supporting the effectiveness of the search system. The results of this study can be concluded that the BM25 algorithm is a more effective and efficient search solution with high scalability potential for application to other web-based job search systems.
Implementasi Metode Cosine Similarity Dalam Sistem Profiling Dosen Berbasis Data Bibliometrik Untuk Pemetaan Kompetensi Akademik Jefry Sunupurwa Asri; Firnanda Amalia; Muhammad Thifaal Dzaki; Muhammad Fikri; Ardra Rianisa
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.811

Abstract

Lecturer profiling based on scientific publications is a strategic component in managing human resources in higher education institutions. The manual process of identifying lecturer competencies often requires considerable time and may lead to inaccuracies. This study aims to develop an automated application for lecturer profiling and competency mapping to relevant courses using an unsupervised text similarity approach based on the Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity methods. The application was developed using the Streamlit framework with integrated data from Google Scholar, SINTA, and Scopus. The evaluation involved 50 lecturers and 120 lecturer–course pairs, measured using accuracy, precision, recall, F1-score, response time, and usability metrics. The results show an accuracy of 85.3%, an F1-score of 0.853, an average response time of 2.3 seconds, and a usability score of 86.4, which falls into the excellent category. The system is capable of displaying interactive lecturer profiles, performing competency mapping to relevant courses, and generating automatic reports in PDF format. Therefore, this application effectively supports data-driven academic decision-making processes for assigning lecturers according to their areas of expertise.