Proceedings of The 2nd International Conference On Advance And Scientific Innovation, ICASI 2019, 18 July, Banda Aceh, Indonesia

Research Article

Decision Support System In Determining Class On Accupuncture Clinic

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  • @INPROCEEDINGS{10.4108/eai.18-7-2019.2288591,
        author={Basiroh  Basiroh and Mohammad  Nur Hilal},
        title={Decision Support System In Determining Class On Accupuncture Clinic},
        proceedings={Proceedings of The 2nd International Conference On Advance And Scientific Innovation, ICASI 2019, 18 July, Banda Aceh,  Indonesia},
        publisher={EAI},
        proceedings_a={ICASI},
        year={2019},
        month={11},
        keywords={dss promotion clustering k-means algoritm},
        doi={10.4108/eai.18-7-2019.2288591}
    }
    
  • Basiroh Basiroh
    Mohammad Nur Hilal
    Year: 2019
    Decision Support System In Determining Class On Accupuncture Clinic
    ICASI
    EAI
    DOI: 10.4108/eai.18-7-2019.2288591
Basiroh Basiroh1,*, Mohammad Nur Hilal2
  • 1: Departemen Information Technology, Politechnic State of Cilacap, Cilacap, Indonesia
  • 2: Department of Mechanical enginering Politechnic state of Cilacap, Indonesia
*Contact email: basiroh@politeknikcilacap.ac.id

Abstract

The process of determining promotional class in acupuncture clinics is very common. Clinics also have data i.e form of primary data and customer secondary data. This happens repeatedly and generates a build up of customer data that affects information retrieval of the data. This study aims to grouping the clinic customer data in which those who are a greater contribution value get a valuable promotion as well. The Acupuncture Clinic uses decision support system by utilizing data mining process by using Clustering technique. K-Means is one of the non-hierarchical clustering data methods that can group customer data into multiple clusters based on the similarity of the data, so the customer data with similar contribution values are grouped in one cluster and those with different contribution values are grouped into other clusters. Implementation using PHP is used to find accurate values. Attributes used are customer earnings, total clinical outcomes, repeat visits, product purchases, needle types and therapists. The customer cluster formed is four clusters, with the first cluster 5 customers, the second cluster 9 customers and the third cluster a total of 6 customers and the fourth cluster there are 5 customers. The results of this study are used as one of the basic decision-making to determine promotion based on the clusters formed by the administration of acupuncture clinics.