Green Computing Innovation Index Measurement Using Fuzzy Inference System for Young Inventors

Authors

  • Thinesswaran Muniandy Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia.
  • Ahmad Nurzid Rosli Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia
  • Suhazlan Suhaimi Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia
  • Asma Hanee Ariffin Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia
  • Suliana Sulaiman Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia
  • Mohd Helmy Abd Wahab Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn, 86400 Batu Pahat, Johor, Malaysia

DOI:

https://doi.org/10.37934/araset.62.1.187197

Keywords:

Innovation, Green Computing, Fuzzy Inference Systems, Young Inventors

Abstract

Green Computing Innovation (GCI) aims to address the negative effects of traditional computing on the environment and promote sustainable practices in computing. Subsequently, the Green Computing Innovation Index (GCII) was used to measure young inventors' inventiveness to evaluate from green computing perspectives. Nonetheless, one of the most significant challenges evaluators face in the measurement process includes uncertainty and imprecise data in measuring the young inventors' green computing innovation. Thus, this paper measures the Green Computing Innovation Index (GCII) with a novel approach utilizing Fuzzy Inference System (FIS) with the Mamdani method. The study measures the top 10 young inventors' innovations from the Faculty of Computing and Meta-Technology, UPSI. Fuzzy Inference System will assist in providing improved decision-making solutions for evaluators to prevent any uncertainty and subjectivity during the evaluation process. The development of FIS is based on four key innovation elements. It uses a triangular membership function with Centroid, Middle of Maximum (MOM), Smallest of Maximum (SOM), Bisector and Largest of Maximum (LOM) methods for defuzzification. The centroid defuzzification method is observed to perform better than other defuzzification methods. It is due to the fact that the adopted defuzzification approach is equally sensitive to all key innovation criteria inputs and can truly represent the minor change in the values of any parameters across all products. The centroid method was found to be the most efficient for decision-making, providing precise results with the ability to detect discrepancies as small as 0.001. Thus, the centroid method can improve decision-making efficiency, leading to fairness in selecting the best green computing innovation product. Ultimately, GCII rating contribute mainly on encouraging young inventors to innovate and promote sustainable practices in innovations. 

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Author Biographies

Thinesswaran Muniandy, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia.

thinesswaran@gmail.com

Ahmad Nurzid Rosli, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia

nurzid@meta.upsi.edu.my

Suhazlan Suhaimi, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia

suhazlan@meta.upsi.edu.my

Asma Hanee Ariffin, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia

asma@meta.upsi.edu.my

Suliana Sulaiman, Faculty of Computing and Meta-Technology, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia

suliana@meta.upsi.edu.my

Mohd Helmy Abd Wahab, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn, 86400 Batu Pahat, Johor, Malaysia

helmy@uthm.edu.my

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Published

2024-10-14

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