Implementation of K-Means Clustering to Group Research Productivity Data of Lecturers at Una'im Yapis Wamena
DOI:
https://doi.org/10.71302/jbidai.v8i2.90Keywords:
Centroid, Data Mining, K-Means Clustering, Research, Lecturer ProductivityAbstract
Una'im Yapis Wamena, as one of the higher education institutions in Papua, has a number of lecturers with various research activities. However, the challenge faced is the difficulty of comprehensively identifying the patterns and characteristics of faculty research. Without proper analysis, it is difficult for the institution to determine the research productivity profile of faculty members, identify those who require mentoring, or determine the focus of research capacity development. This can result in suboptimal allocation of resources, research support, and research development planning within the institution. This study aims to help determine the level of productivity of lecturers in research with high, medium, and low categories. With this grouping, it is hoped that the Una'im Yapis Wamena Campus can more easily make decisions about the appropriate coaching methods to improve the research results of lecturers. The method used in this study is K-means Clustering. The data analyzed included 45 lecturers with variables such as number of publications, number of citations, H-index, funded research, and research collaboration. The results of this study show that the level of faculty research productivity can be grouped into three categories, namely: Cluster 1 (High Productivity) consisting of 8 faculty members, Cluster 2 (Medium Productivity) consisting of 25 faculty members, and Cluster 3 (Low Productivity) consisting of 12 faculty members. The clustering process reached convergence in the 6th iteration with stable results. These clustering results can serve as a guide for Una'im Yapis Wamena in determining policies to increase lecturer research productivity in the future.
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