Cluster Analysis Based on McKinsey 7s Framework in Improving University Services


  • Deny Jollyta Faculty of Computer Science, Institut Bisnis dan Teknologi Pelita Indonesia
  • Dwi Oktarina Faculty of Computer Science, Institut Bisnis dan Teknologi Pelita Indonesia
  • Gusrianty Faculty of Computer Science, Institut Bisnis dan Teknologi Pelita Indonesia
  • Renita Astri Program of Information System, Universitas Dharma Andalas
  • Lina Arliana Nur Kadim STMIK Kaputama Binjai
  • Ni Gusti Ayu Dasriani Faculty of Engineering and Design, Universitas Bumigora


University services, McKinsey 7s, Manhattan distance, K-Medoids algorithm


The epidemic of Covid-19 has impacted all aspects of human life, including education. Academic and administrative services for academic community are suffering, as a result of the fact that not all universities are able to provide online services to help break the chain of Covid-19 distribution. This is due to a lack of human competencies to use technology and a lack of information technology resources, necessitating the development of new strategies by universities to address these flaws. The goal of this study is to develop a university service strategy based on McKinsey 7s cluster results on the part that is having issues based on questionnaire data. The questionnaire is organized on seven McKinsey elements. The Manhattan distance calculation and the K-Medoids algorithm results demonstrated that the structure, system, skill and staff are all part of elements that clustered in k=2 and has to be addressed in aiding services during the Covid-19 pandemic. The McKinsey 7s showed that universities service enhancements may be achieved by combining clustering techniques and McKinsey framework.


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How to Cite

Jollyta, D. ., Oktarina, D. ., Gusrianty, Astri , R. ., Kadim, L. A. N. ., & Dasriani, N. G. A. . (2021). Cluster Analysis Based on McKinsey 7s Framework in Improving University Services. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 1(1), 1–8. Retrieved from