Social CRM using Web Mining for Indonesian ...

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Social CRM as a social media extension can gives benefits where direct marketing cannot, for example, marketing team can profile their potential candidate to.
Social CRM using Web Mining for Indonesian Academic Institution Nyoman Karna, Iping Supriana, Nur Maulidevi Sekolah Teknik Elektro dan Informatika Institut Teknologi Bandung Bandung, Indonesia [email protected], [email protected], [email protected] Abstract—Indonesian academic institutions commonly use direct marketing strategy, such as visiting high schools to find potential candidate for students, however, only few institutions use social media as an alternative for marketing channel and as the source of information. Social CRM as a social media extension can gives benefits where direct marketing cannot, for example, marketing team can profile their potential candidate to provide a better proposal. To do this, social CRM must have a feature like web mining where a crawler engine gather information from social media for a specific customer, for example high school students and/or parents, and create better segmentation using clustering algorithm. This research proposes a model and implementation of web mining extension part for social CRM, especially for Indonesian academic institution. This model and web mining part can focus marketing strategy by segmenting potential candidates. Keywords—CRM; customer relationship; customer profile; web mining; knowledge-based system; semantic network; social media; Indonesian academic institution; marketing strategy

REFERENCES [1] [2] [3]

[4]

Direktorat Jenderal Pendidikan Tinggi Indonesia, Pangkalan Data Pendidikan Tinggi, https://forlap.dikti.go.id, 2015 Seleksi Nasional Masuk Perguruan Tinggi Negeri, https://web.snmptn.ac.id/ptn, 2015. Nyoman Karna, Iping Supriana, Nur Maulidevi, “Social CRM using web mining,” International Conference on Information Technology Systems and Innovation (ICITSI), pp. 264 – 268, 2014. Ronald J. Brachman and Hector J. Levesque, Knowledge Representation and Reasoning, San Francisco: Morgan Kaufmann, 2004: 2-11.

[5]

[6]

[7]

[8]

[9]

[10]

[11] [12]

[13]

[14]

[15]

John F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations, Boston: Course Technology, CENGAGE Learning, 2000: 11-29. Nyoman Karna, Iping Supriana, and Ulfa Maulidevi, “Intelligent Interface for a Knowledge-based System,” Journal Telkomnika Telecommunication, Computing, Electronics and Control, Vol. 12, No. 4, TELKOMNIKA, pp. 1096-1104, 2014 Gordon McCalla and Nick Cercone, “Guest Editors' Introduction: Approaches to Knowledge Representation,” IEEE Journal of Computer, Volume: 16, Issue: 10, 1983 Liu Xin, “A New Algorithm for Electronic Customer Relationship Management,” 7th International Conference on Computing and Convergence Technology (ICCCT), pp. 359-362, 2012. Limei Zhang, “Data mining application in customer relationship management,” International Conference on Computer Application and System Modeling (ICCASM), pp. V14-171-V14-174, 2010. Ming Ren, Zuoliang Chen, and Chuanlan Liu, “An Evolving Information System Based in Data Mining Knowledge to Support Customer Relationship Management,” IEEE Symposium on Advanced Management of Information for Globalized Enterprises (AMIGE), pp. 15, 2008. TeleManagement Forum, Application Framework (TAM), Frameworx 15, http://www.tmforum.org, 2015 Direktorat Jenderal Pendidikan Dasar dan Menengah Kementerian Pendidikan dan Kebudayaan Indonesia, Data Pokok Pendidikan Jenjang SMA-SMK, http://dapo.dikmen.kemdikbud.go.id/ Foundation Directory Online, The Foundation Center’s Fields of Interest/Subject Term/Recipient Type Authority List, https://fconline.foundationcenter.org Pusat Kajian Komunikasi Universitas Indonesia, Profil Pengguna Internet Indonesia 2014, Asosiasi Penyelenggara Jasa Internet Indonesia, Maret 2015 Badan Pusat Statistik Indonesia, Klasifikasi Baku Jenis Pekerjaan Indonesia 2002, http://sirusa.bps.go.id/webadmin/doc/KBJI2002.pdf, 2002