Fuzzy Based Evaluation of Digital Connectivity Across Countries Using Mamdani Fuzzy Inference System

Asido Saragih; Jaya Santoso; Ana Muliyana

doi:10.33541/edumatsains.v10i4.8096

Authors

Asido Saragih Institut Teknologi Del
Jaya Santoso
Ana Muliyana

DOI:

https://doi.org/10.33541/edumatsains.v10i4.8096

Keywords:

Mamdani Fuzzy Inference System, Digital Connectivity, Internet Users, Fixed Broadband, Mobile Subscriptions

Abstract

Digital connectivity plays an important role in supporting economic growth and social inclusion. However, measuring it across countries is not straightforward because it involves several indicators and a certain level of uncertainty. In this study, we applied a Mamdani Fuzzy Inference System (FIS) to assess digital connectivity in 137 countries. The data were taken from the ITU and World Bank (2024), including Internet Users, Fixed Broadband Subscriptions, and Mobile Cellular Subscriptions. We built a rule-based system using five linguistic categories and adjusted the membership functions based on the data distribution. The centroid method was then used to generate a Digital Connectivity Index (DCI) on a scale of 0 to 100. The results indicate a clear gap in global connectivity. A total of 23 countries achieved a Very High DCI (≥ 80), mostly located in Western Europe and East Asia. In contrast, 17 countries fell into the Very Low category (< 20), mainly from Sub-Saharan Africa. The global average DCI was 53.47. Overall, the proposed approach provides a clear and easy-to-understand classification that can support policy analysis and evaluation of digital development.

References

Aker, J. C., & Mbiti, I. M. (2010). Mobile Phones and Economic Development in Africa. Journal of Economic Perspectives, 24(3), 207–232. https://doi.org/10.1257/jep.24.3.207

Andayani, S., & Fauziah, A. (2024). Multilevel Fuzzy Mamdani Method for Assessing the Health of Sharia Cooperation. Jurnal Sains Dasar, 13(2).

Bala, P. (2024). The Impact of Mobile Broadband and Internet Bandwidth on Human Development:A Comparative Analysis of Developing and Developed Countries. Journal of the Knowledge Economy, 15(4), 16419–16453. https://doi.org/10.1007/s13132-023-01711-0

Cariolle, J. (2021). International connectivity and the digital divide in Sub-Saharan Africa. Information Economics and Policy, 55, 100901. https://doi.org/10.1016/j.infoecopol.2020.100901

Dutta, S., Lanvin, B., León, L. R., & Vincent, W. S. (2022). The Global Innovation Index 2022. In World Intellectual Property Organization.

Fatima, A., Abbas, S., Asif, M., Khan, M., & Khan, M. (2018). Optimization of Governance Factors for Smart City Through Hierarchical Mamdani Type-1 Fuzzy Expert System Empowered with Intelligent Data Ingestion Techniques. ICST Transactions on Scalable Information Systems, 0(0), 159975. https://doi.org/10.4108/eai.13-7-2018.159975

Istiadi, I., Emma Budi Sulistiarini, Rudy Joegijantoro, Anik Vega Vitianingsih, & Affi Nizar Suksmawati. (2022). Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious Diseases. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(6), 1047–1056. https://doi.org/10.29207/resti.v6i6.4656

ITU. (2024). Measuring digital development: ICT Development Index 2024. In International Telecommunication Union.

Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy Multicriteria Decision-Making: A Literature Review. International Journal of Computational Intelligence Systems, 8(4), 637. https://doi.org/10.1080/18756891.2015.1046325

Katz, R., & Callorda, F. (2018). The economic contribution of broadband, digitization and ICT regulation. In ITU Telecommunication Development Sector.

Klir, G. J., & Yuan, B. (1995). Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall.

Kozielski, M., Prokopowicz, P., & Mikołajewski, D. (2024). Aggregators Used in Fuzzy Control—A Review. Electronics, 13(16), 3251. https://doi.org/10.3390/electronics13163251

Li, D., & He, M. (2024). MP and MT properties of fuzzy inference with aggregation function. Engineering Applications of Artificial Intelligence, 128, 107495. https://doi.org/10.1016/j.engappai.2023.107495

Liu, H., & Zhang, L. (2018). Fuzzy rule-based systems for recognition-intensive classification in granular computing context. Granular Computing, 3(4), 355–365. https://doi.org/10.1007/s41066-018-0076-7

Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2

Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications – Two decades review from 1994 to 2014. Expert Systems with Applications, 42(8), 4126–4148. https://doi.org/10.1016/j.eswa.2015.01.003

Pradhan, R. P., Arvin, M. B., Hall, J. H., & Nair, M. (2016). Innovation, financial development and economic growth in Eurozone countries. Applied Economics Letters, 23(16), 1141–1144. https://doi.org/10.1080/13504851.2016.1139668

Pratama, Y., Pasaribu, M., Nababan, J., Sihombing, D., & Gultom, D. (2021). Selection of Scholarship Recipient by Implementing Genetic Algorithm and Fuzzy Logic. Journal of Physics: Conference Series, 1933(1), 012069. https://doi.org/10.1088/1742-6596/1933/1/012069

Ross, T. J. (2017). Fuzzy logic with engineering applications (4th ed.).

Rustum, R., Kurichiyanil, A. M. J., Forrest, S., Sommariva, C., Adeloye, A. J., Zounemat-Kermani, M., & Scholz, M. (2020). Sustainability Ranking of Desalination Plants Using Mamdani Fuzzy Logic Inference Systems. Sustainability, 12(2), 631. https://doi.org/10.3390/su12020631

Sarkheil, H., Rahbari, S., & Azimi, Y. (2021). Fuzzy-Mamdani environmental quality assessment of gas refinery chemical wastewater in the Pars special economic and energy zone. Environmental Challenges, 3, 100065. https://doi.org/10.1016/j.envc.2021.100065

Triwinanto, M. A., Nugroho, B. I., & Gunawan, G. (2023). Penerapan fuzzy mamdani untuk sistem pendukung keputusan pemilihan telepon seluler. E-Link: Jurnal Teknik Elektro Dan Informatika, 18(2), 67. https://doi.org/10.30587/e-link.v18i2.5893

United Nations. (2023). The sustainable development goals report 2023. In UN Department of Economic and Social Affairs.

World Bank. (2022). Digital economy for Africa: Achieving the digital transformation ambition. In World Bank Group.

World Development Report. (2016). Digital Dividends. In DC: World Bank.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X