Christ S, Merry (2016) Analisa Perbandingan Algoritma K-Means Dan Fuzzy C-Means(Studi Kasus:Topik Skripsi Sistem Informasi). Other thesis, Universitas Katolik Musi Charitas.
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Abstract
The performance of each algorithm is very important, as well as the selection of a thesis topic for students final year. Clustering is a grouping of data without specific data based on the class. Clustering can be used to label the data class is not yet known. The method used is the CRISP-DM which through understanding of business processes, understanding the data, the data preparation, modeling, evaluation and deployment. The algorithm used for the formation of clusters is a K-Means algorithm and Fuzzy C-Means. K-Means and Fuzzy C-Means is one of data method non-hierarchical clustering.RapidMiner 7.0 is using the research to aid clustering of attributes used are the academic year, sex and thesis topic. The result this research is efficiency based on time. The result are used as a feedback in the use of an algorithm to study the case further.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | Skripsi Lengkap dapat dibaca di Ruang Referensi Perpustakaan UKMC Kampus Bangau. |
| Uncontrolled Keywords: | Clustering, CRISP-DM, academic year, sex, thesis topic, K-Means, Fuzzy C-Means, RapidMiner 7.0 |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Theses - S1 > Information Systems Study Program |
| Depositing User: | Perpustakaan Unika Musi Charitas |
| Date Deposited: | 13 Nov 2017 01:56 |
| Last Modified: | 03 Apr 2018 05:00 |
| URI: | http://eprints.ukmc.ac.id/id/eprint/562 |
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