Segmentasi Penilaian Kompetensi Alumni Stt-Pln Menggunakan Model Klaster Fuzzy Clustering Means (Fcm)

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Dine Tiara Kusuma
Iriansyah BM Sangadji

Abstract

Manpower needs of the company will be reliable and quality from year to year is always increasing. Where the requirements in the workforce is not only in the field of academic, non-academic ability also is of no less importance after the alumni entering the workforce. Therefore, STT-PLN is required to produce a qualified workforce and have high competitiveness in their field. In order to obtain maximum results is then, in need of harmony between education and field of work of the graduates as well as the necessary harmony between the academic ladder STT-PLN graduates with non-academic level is the main requirement in obtaining employment. To obtain this information then dibutukan further research on the level of competence of alumni with the working world markets. This research will use Fuzzy Clustering Means clustering method (FCM) to obtain information about the competencies needed in the world of work. The number of clusters formed is as much as three clusters in which, of the three clusters that have been produced will find the best cluster. The best member of the cluster will be a priority competencies that will be applied in the field of learning in STT-PLN. From the calculation of the index XB it can be concluded that the cluster 3 is the best cluster. Where members of the cluster 3 may be a priority concern for decision makers in determining policy on the application of learning methods to the lecturer / lecturer / educator in applying methods of learning to the learning process of students not only gain competency in the academic field but also in the field non-academic indirectly. Because based on survey results that competency is a point which is not less important in addition to competency in the academic field.

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How to Cite
Kusuma, D. T., & BM Sangadji, I. (2019). Segmentasi Penilaian Kompetensi Alumni Stt-Pln Menggunakan Model Klaster Fuzzy Clustering Means (Fcm). KILAT, 5(2), 88–96. https://doi.org/10.33322/kilat.v5i2.685
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Articles

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