[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119651-en":3,"doc-seo-119651-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119651,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Integration of Machine Learning and GAP Analysis for a Data Driven Lecturer Performance Evaluation System","Research designs and implements a lecturer performance evaluation system that integrates Machine Learning with the GAP analysis method to quantify differences between expected competencies and actual performance. The model uses eight primary criteria—cooperation, communication, initiative, alertness, discipline, leadership, problem solving, and time usage—each with sub-criteria. The study applies a web-based decision support system with trained machine learning models to classify performance levels and reveal patterns. Evaluation with 18 lecturers assesses usability across ease of use, completeness, accuracy, and interface composition, achieving an overall 88% rating labeled “Very Worthy,” improving decision-making and efficiency through predictive analytics.","International Journal of Advances in Data and Information Systems  \nVol. 7, No. 1, April 2026, pp. 52~61  \nISSN: 2721-3056, DOI: 10.25008/ijadis.v7i1 .1481 r 52  \n\n| Integration of Machine Learning and GAP Analysis for a Data Driven Lecturer Performance Evaluation System\u003Cbr>Ramen Antonov Purba1, Tori Andika Bukit2\u003Cbr>1STMIK Methodist, Binjai, Indonesia\u003Cbr>2Hankuk University of Foreign Studies, South Korea |  |\n| --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Nov 28, 2025 Revised Dec 29, 2025 Accepted Jan 06, 2026\u003Cbr>Keywords:\u003Cbr>Machine Learning; GAP Analysis;\u003Cbr>Performance Evaluation; Data Driven Assessment; DSS.\u003Cbr>Corresponding Author: | ABSTRACT\u003Cbr>The objective of this research is to design and implement a performance evaluation system that combines Machine Learning for data processing, predictive modeling, and pattern recognition with the GAP method to measure discrepancies between expected competencies and actual performance. Eight primary criteria were cooperation, communication, initiative, alertness, discipline, leadership, problem solving, time usage each consisting of several sub-criteria. The study involved 18 lecturers, and the evaluation was conducted using a web-based decision support system equipped with machine learning models trained to classify performance levels and identify underlying patterns within the assessment data. System usability was examined through four categories: ease of use, completeness, accuracy, and interface composition. The results show that the integrated system successfully identified the highest-performing lecturer (Lecturer 7) with a score of 6.1801, followed by Lecturer 12 with 4.9314 and Lecturer 4 with 4.1157. Usability testing also yielded positive outcomes, with scores of 89% for ease of use, 87% for completeness, 90% for accuracy enhanced through machine learning validation and 88% for interface composition. These results produced an overall average of 88%, classifying the system as Very Worthy. In conclusion, integrating Machine Learning and GAP Analysis in a web-based DSS significantly improves the effectiveness and efficiency of lecturer performance evaluation. The system accelerates data processing, enhances assessment quality, and strengthens decision-making through predictive analytics and automated classification. This framework offers a valuable reference for future performance evaluations in higher education institutions seeking accountability, transparency, and data-driven decision-making.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| Ramen Antonov Purba,\u003Cbr>Informatics Engineering Study Program, STMIK Methodist, Binjai, Indonesia, Email: [ramenantonovpurba@gmail.com](ramenantonovpurba@gmail.com). |  |\n\n1. INTRODUCTION  \nThe rapid development of digital technology requires higher education institutions to adopt more serious, systematic, and data-driven approaches in evaluating lecturer performance. Lecturer performance plays a crucial role in influencing the quality of education, institutional accreditation, and long-term academic development, making it essential for universities to implement objective and transparent assessment strategies [1] . STMIK Methodist Binjai, as an institution committed to academic improvement, needs a reliable mechanism to analyze and monitor lecturer performance across key competency areas such as teaching quality, research productivity, community service, communication, discipline, innovation, teamwork, and administrative responsibilities. In this context, the integration of Machine Learning and the GAP Analysis Method offers an effective solution by combining advanced data processing, pattern recognition, and automated classification capabilities with a structured framework for measuring discrepancies between expected standards and actual performance [2], [3] . The implementation of a web-based, data-driven performance evaluation system enables institutional leaders to obtain more accurate insights, m","cbCaieIsFMm0wZaI","https://ap.wps.com/l/cbCaieIsFMm0wZaI","pdf",1848512,1,10,"English","en",105,"# Introduction\n## Data-driven lecturer performance evaluation\n## Integration of Machine Learning and GAP analysis\n# System and evaluation approach\n## Web-based DSS with predictive modeling\n## Criteria and sub-criteria for assessment\n# Usability and results\n## Usability dimensions\n## Identified top-performing lecturers\n## Overall effectiveness","[{\"question\":\"What is the main goal of the proposed lecturer performance evaluation system?\",\"answer\":\"The research aims to design and implement a performance evaluation system that combines Machine Learning with GAP analysis to measure discrepancies between expected competencies and actual lecturer performance.\"},{\"question\":\"How does the system evaluate lecturer performance?\",\"answer\":\"It uses eight primary criteria—cooperation, communication, initiative, alertness, discipline, leadership, problem solving, and time usage—each supported by sub-criteria, and applies machine learning models to classify performance levels and identify patterns in the assessment data.\"},{\"question\":\"How were usability and system effectiveness assessed?\",\"answer\":\"Usability was tested in four categories: ease of use, completeness, accuracy, and interface composition. 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