Financial Control Techniques Services Company with Fuzzy Mamdani

 
 
 
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  • Abstract


    Service Company is a business activity that provides products in the form of services to customers. Micro business services are in great demand among SMK graduates as it is very easy to live up to their abilities. This includes Micro Service Counter service, tailor, reflection, and others. However, the problem that arises is their lack of practical financial business knowledge. Many businesses they experience an emergency because they do not have proper business financial statements. With the current technological advances, most problems can be solved by technology. One such solution is an accounting information system application with mamdani blur technique. The process of calculating the system is done in 4 stages, namely: the formation of the blur set, the implications of the rules, the rules of composition and Defuzzyfication. Based on the results of trials, there’s an error in determining the price of services. Therefore, the high price of service can reduce the number of service requests and the low price service may incur losses to the company. With this system, one can determine the best service price and the best service for consumers.


  • References


      [1] Adewuyi, P. A. (2012). Performance Evaluation of Mamdani-type and Sugeno-type Fuzzy Inference System Based Controllers for Computer Fan. International Journal of Information Technology and Computer Science, 5(1), 26–36. https://doi.org/10.5815/ijitcs.2013.01.03

      [2] Agus Nursikuwagus. (2017). A Mamdani Fuzzy Model to Choose Eligible Student Entry. Telkomnika, , 15(1), 365–372. https://doi.org/10.12928/TELKOMNIKA.v15i1.4893

      [3] Arcilla, M., Calvo-Manzano, J. A., & San Feliu, T. (2013). Building an IT service catalog in a small company as the main input for the IT financial management. Computer Standards and Interfaces, 36(1), 42–53. https://doi.org/10.1016/j.csi.2013.07.003

      [4] Basu, S. (2012). Realization of Fuzzy Logic Temperature Controller. International Journal of Emerging Technology and Advanced Engineering, 2(6), 151–155.

      [5] Bergmeir, C., & Ben, M. (2015). frbs : Fuzzy Rule-Based Systems for Classification. Journal of Statistical Software, 65(6), 1–30. https://doi.org/10.18637/jss.v069.i12

      [6] Dauderis, H., & Annand, D. (2017). with Open Texts Introduction to Financial Accounting.

      [7] Faith, D. O., & Edwin, A. M. (2014). A Review of The Effect of Pricing Strategies on The Purchase of Consumer Goods. International Journal of Research in Management, Science & Technology, 2(2), 2321–3264.

      [8] Lekhanya, L. M. (2013). Functions and Reliability of International Financial Reporting Systems of Rural Smes in Kwazulu Natal: Knowledge and Understanding of Financial Management. International Journal of Academic Research in Accounting Finance and Management Sciences, 3(3), 125–132. https://doi.org/10.6007/IJARAFMS/v3-i

      [9] Liaquat, H., Irfan, A., & Sami, A. (2017). Technical Efficiency And Its Determinants: A Case Study Of Faisalabad Textile Industry. City University Research Journal, Special Issue, 183-194

      [10] Ogedengbe, M. T., & Agana, M. A. (2017). New Fuzzy Techniques for Real-Time Task Scheduling on Multiprocessor Systems, 47(3), 189–196.

      [11] Patil, S., Mulla, A., & Mudholkar, R. R. (2012). Best Student Award – a Fuzzy Evaluation Approach. International Journal of Computer Science and Communication, 3(1), 9–12.

      [12] Sanchez-Torrubia, G., & Torres-Blanc, C. (2010). A Mamdani-Type Fuzzy Inference System To Automatically Assess Dijkstra ’ S Algorithm Simulation Gloria Sánchez – Torrubia , Carmen Torres – Blanc, 17(1).

      [13] Shaharudin, J., Angely, G. S. N., Anita, J., & Khin, T. M. (2012). Examining the Product Quality Attributes That Influences Customer Satisfaction Most When the Price Was Discounted : A Case Study in Kuching Sarawak Curtin University of Technology Sarawak Campus. International Journal of Business and Social Science, 3(23), 221–237.

      [14] Sigit, H. T., & Kapuji, A. (2014). Mamdani Fuzzy inference system Application Setting For Traffic Lights, 3(10), 56–62.

      [15] Srismrita Basu. (2012). Realization of fuzzy logic temperatur Controller, 2(6), 56–62.


 

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Article ID: 22382
 
DOI: 10.14419/ijet.v7i4.28.22382




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