Implementasi Metode Fuzzy K-Means untuk Cluster Judul Skripsi Mahasiswa

Ahmad Rifa'i, Galet Guntoro Setiadji

Abstract


Clusters are a way of classifying data which can later be used as information and processed using data mining methods with certain algorithms. From here the author wants to try to use data in the form of thesis titles or final assignments from students, which later can be grouped from these results. So that the existing data can be processed using a data mining method, namely Fuzzy K-Means (FKM).

The data used in this study uses thesis data of students majoring in information technology. As a comparison, the data is also processed using K-Means, from the K-Means calculation, the average cluster is 1.014 and the DBI validity is 0.725. Meanwhile, for the calculation of Fuzzy K-Means, the cluster average is 0.069 and the DBI validity is 0.304

Keywords


Data mining, K-Means, Fuzzy K-Means, DBI

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References


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DOI: http://dx.doi.org/10.26623/jprt.v16i2.2637

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