Yazid

Work place: Magister of Informatics Engineering of Universitas AMIKOM Yogyakarta, Ring Road Utara ST, Condong Catur, Depok Sleman Yogyakarta Indonesia

E-mail: ntyazid@gmail.com

Website:

Research Interests: Computer systems and computational processes, Artificial Intelligence, Data Mining, Data Structures and Algorithms

Biography

Yazid, M.Kom, was born in Purbalingga, on December 18, 1990. He earned his S.Kom. from STMIK Amikom Purwokerto in 2015, and M.Kom. from Universitas Amikom Yogyakarta in 2017, Indonesia. He worked as Android Software Developer at PT. Woolu Aksaramaya located in Yogyakarta, Indonesia, in the year 2016-2017. He also became one of the founders of Nairotechno engaged in software development and IT Consultant located in Purwokerto, Central Java Indonesia. His research is interested in the field of data mining, artificial intelligence, data warehouse, and big data. He has already participated in national and international conferences.

Author Articles
Decision Support System to Determine Promotional Methods and Targets with K-Means Clustering

By Yazid Ema Utami

DOI: https://doi.org/10.5815/ijieeb.2018.02.02, Pub. Date: 8 Mar. 2018

Promotion becomes one of the important aspects of institutions of college. The number of competitors demanding the marketing must be fast and accurate in formulating strategies and decision making. Data warehouse and data mining become one of the means to build a decision support system that can provide knowledge and wisdom quickly to be taken into consideration in promotion strategy planning. Development of this system then does the process of testing with the number of data 6171 rows of student enrollment taken directly from a transactional database. The data is done ETL process and clustering with the k-means clustering algorithm, then the data in each cluster is done grouping and summarization to get weighting. After that just done ranking to produce wisdom, one of them determine the list of schools that will be the target roadshow. The analysis also produces several patterns of student enrollment, namely the registrant pattern from the wave of registration and favorite or non-favorite school categories. In addition, the results of system design in this study can be developed easily if requires added external data. Such as data of SMK/SMK school graduates in the area or data of students enrolling in other universities. This is one of the superiority of semantic-based data warehouses.

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