Abstract
This study aims to map the use of access points based on the log of usage in several locations in the UIN Sunan Kalijaga. The analyzed data are access point records taken three times a day for one week working hours. The data obtained was processed and clustered using the K-Means algorithm. There are five clusters obtained from the processed data, each of which divides the number of access loads from an access point. The results of clustering provide a recommendation on which locations need to be added to access points that it can improve institutional userservices.
References
Arsih, Kansul. (2012). Perancangan dan Implementasi Aplikasi Analisis Log Menggunakan Metode JST Adaptive Resonance Theory 2 dalam Memprediksi Tingkah Laku Pengguna Internet. Bandung : Universitas Telkom
Witten, et al. (2012). Data Mining Practical Machine Learning Tools and Technique, 2nd Edition. San Fransisco : Morgan Kaufmann
Micro, Andi. (2012). Dasar-dasar Jaringan Komputer. clearOS Indonesia
Aini, Fithratul. (2011). Web Usage Mining Menggunakan Algoritma Adaptive Web Access Pattern Tree (AWAPT). Bandung: Universitas Telkom
Han, Jiawei. et al. (2011). Data Mining: Concepts and Tecniques, 3rd ed. San Francisco: Morgan Kauffman
Prasetyo, Eko. (2012). Data Mining : Konsep dan Aplikasi Menggunakan MATLAB. Yogyakarta : Andi
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