A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem





Rural, Indexing, Clustering, Classification, Recommendation, Village Assistants


Goals (SDGs) within intricate rural contexts holds paramount significance. Sustainable rural development holds profound significance for both developed and developing nations. This study was conducted to develop a methodological framework for placing experts with strategically relevant competencies to meet the specific needs of villages, thus enabling the practical application of their expertise in generating innovative solutions to problems in a village. This study entails several pivotal phases. Firstly, it constructs a Community Standard of Living Index (CSLI) using  Delphi and Rank Reciprocal (RR). Secondly, it establishes village clustering through a hybrid Fuzzy C-Means (FCM), Self-Organizing Map (SOM), and Xie-Beni (XB). Thirdly, a classification of village development levels is created using Tsukamoto and Smallest of Maximum (SM). Finally, recommendations for placing experts in villages, aligning their skills with identified needs using the Cosine Similarity (CS). The results obtained are compared with factual data of each village to obtain relevant conclusions, where an accuracy value of 0.95 indicates a high success rate in the test results of the proposed technique. This study has the potential to significantly enhance decision-making by introducing opportunities for the development of hybrid methodologies in expert mapping for rural issues.


Download data is not yet available.


Harbiankova, A., & Gertsberg, L. (2022). Information Model for Sustainable Rural Development. Energies, 15(11), 4009. https://doi.org/10.3390/en15114009

Bennich, T., Persson, Å., Beaussart, R., Allen, C., & Malekpour, S. (2023). Recurring patterns of SDG interlinkages and how they can advance the 2030 Agenda. One Earth. https://doi.org/10.1016/j.oneear.2023.10.008

Purba, J. T., Budiono, S., Hariandja, E. S., & Pramono, R. (2023). Sustainability Strategy to Alleviate Poverty Through Education, Energy, GRDP, and Special Funds: Evidence from Indonesia. International Journal of Sustainable Development and Planning, 18(5), 1397–1406. https://doi.org/10.18280/ijsdp.180510

Yang, X., Xu, Z., & Xu, J. (2023). Large-scale group Delphi method with heterogeneous decision information and dynamic weights. Expert Systems with Applications, 213. https://doi.org/10.1016/j.eswa.2022.118782

Chatterjee, S., & Chakraborty, S. (2023). Application of the R method in solving material handling equipment selection problems. Decision Making: Applications in Management and Engineering, 6(2), 74–94. https://doi.org/10.31181/dmame622023391

Xu, N., Zhu, W., Wang, R., Li, Q., Wang, Z., & Finkelman, R. B. (2023). Application of self-organizing maps to coal elemental data. International Journal of Coal Geology, 277, 104358. https://doi.org/10.1016/j.coal.2023.104358

Sahu, P., Kumar Sahoo, B., Kumar Mohapatra, S., & Kumar Sarangi, P. (2023). Segmentation of encephalon tumor by applying soft computing methodologies from magnetic resonance images. Materials Today: Proceedings, 80, 3371–3375. https://doi.org/10.1016/j.matpr.2021.07.255

Mota, V. C., Damasceno, F. A., & Leite, D. F. (2018). Fuzzy clustering and fuzzy validity measures for knowledge discovery and decision making in agricultural engineering. Computers and Electronics in Agriculture, 150, 118–124. https://doi.org/10.1016/j.compag.2018.04.011

Uma G, & S, N. (2023). An Investigative Study on Quick Switching System using Fuzzy and Neutrosophic Poisson Distribution. Neutrosophic Systems with Applications, 7, 61–70. https://doi.org/10.61356/j.nswa.2023.28

Abdelhafeez, A., Mahmoud, H., & Aziz, A. S. (2023). Identify the most Productive Crop to Encourage Sustainable Farming Methods in Smart Farming using Neutrosophic Environment. Neutrosophic Systems with Applications, 6, 17–24. https://doi.org/10.61356/j.nswa.2023.34

Dore, P., Chakkor, S., El Oualkadi, A., & Baghouri, M. (2023). Real-time intelligent system for wind turbine monitoring using fuzzy system. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 3, 100096. https://doi.org/10.1016/j.prime.2022.100096

Kurniawan, S., Ranggadara, I., Suhendra, & Ratnasari, A. (2020). Smallest of Maximum to find α-predicate for DeterminingCattle Health Conditions. International Journal of Advanced Trends in Computer Science and Engineering, 9(5), 8245–8251. https://doi.org/10.30534/ijatcse/2020/192952020

Olajide, A. O., Busayo, A. T., & Olawale, E. T. (2017). Analyzing the Effects of the Different Defuzzification Methods in the Evaluation of Javacomponents’ Customizability for Reusability. International Journal of Advanced Engineering, Management and Science, 3(9), 962–971. https://doi.org/10.24001/ijaems.3.9.10

Morales, R., Marín, L. G., Roje, T., Caquilpan, V., Sáez, D., & Nuñez, A. (2024). Microgrid planning based on computational intelligence methods for rural communities: A case study in the José Painecura Mapuche community, Chile. Expert Systems with Applications, 235, 121179. https://doi.org/10.1016/j.eswa.2023.121179

Mupedziswa, R., Malinga, T., & Nuggert Ntshwarang, P. (2021). Standard of Living, Well-Being and Community Development: The Case of Botswana. In Improving Quality of Life - Exploring Standard of Living, Wellbeing, and Community Development. IntechOpen. https://doi.org/10.5772/intechopen.97680

Lathief, M. F., Soesanti, I., & Permanasari, A. E. (2019). Combination of Fuzzy C-Means, Xie-Beni Index, and Backpropagation Neural Network for Better Forecasting Result. Proceedings of the International Conference on Creative Economics, Tourism and Information Management, Iccetim 2019, 72–77. https://doi.org/10.5220/0009858200720077

Nugraha, L. F., Sulistyowati, L., Setiawan, I., & Noor, T. I. (2022). Alternative Community-Based Village Development Strategies in Indonesia: Using Multicriteria Decision Analysis. Agriculture, 12(11), 1903. https://doi.org/10.3390/agriculture12111903

Nur Fitria, M. C., Debataraja, N. N., & Rizki, S. W. (2022). Classification of Village Status in Landak Regency Using C5.0 Algorithm. Tensor: Pure and Applied Mathematics Journal, 3(1), 33–42. https://doi.org/10.30598/tensorvol3iss1pp33-42

Pattanayek, S. K., Mookherjee, S., & Mondal, D. (2019). An Analysis of Standard of Living Index in West Bengal’s Paschim Medinipur District: An Application of Average Correlation Method. Indian Journal of Human Development, 13(2), 159–182. https://doi.org/10.1177/0973703019851629

Salima, N., & Ilham, A. (2022). Village Status Classification Based Tree Algorithm. Proceedings of the 3rd International Conference of Science Education in Industrial Revolution 4.0, ICONSEIR 2021, December 21st, 2021, Medan, North Sumatra, Indonesia, 2–8. https://doi.org/10.4108/eai.21-12-2021.2317488

Einola, K., & Alvesson, M. (2021). Behind the Numbers: Questioning Questionnaires. Journal of Management Inquiry, 30(1), 102–114. https://doi.org/10.1177/1056492620938139

Friston, K. J., Parr, T., Yufik, Y., Sajid, N., Price, C. J., & Holmes, E. (2020). Generative models, linguistic communication and active inference. Neuroscience & Biobehavioral Reviews, 118(June), 42–64. https://doi.org/10.1016/j.neubiorev.2020.07.005

Taherdoost, H. (2018). Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research. SSRN Electronic Journal, 5(2), 18–27. https://doi.org/10.2139/ssrn.3205035

Watson, L. (2018). Educating for Good Questioning: a Tool for Intellectual Virtues Education. Acta Analytica, 33(3), 353–370. https://doi.org/10.1007/s12136-018-0350-y

Danacı, M., & Yıldırım, U. (2023). Comprehensive analysis of lifeboat accidents using the Fuzzy Delphi method. Ocean Engineering, 278, 114371. https://doi.org/10.1016/j.oceaneng.2023.114371

Mohammed, R. T., Yaakob, R., Sharef, N. M., & Abdullah, R. (2021). Unifying The Evaluation Criteria Of Many Objectives Optimization Using Fuzzy Delphi Method. Baghdad Science Journal, 18(4), 1423. https://doi.org/10.21123/bsj.2021.18.4(Suppl.).1423

Mohammed, R. T., Zaidan, A. A., Yaakob, R., Sharef, N. M., Abdullah, R. H., Zaidan, B. B., Albahri, O. S., & Abdulkareem, K. H. (2022). Determining Importance of Many-Objective Optimisation Competitive Algorithms Evaluation Criteria Based on a Novel Fuzzy-Weighted Zero-Inconsistency Method. International Journal of Information Technology & Decision Making, 21(01), 195–241. https://doi.org/10.1142/S0219622021500140

Faisal, M., & Rahman, T. K. A. (2023). Optimally Enhancement Rural Development Support Using Hybrid Multy Object Optimization (MOO) and Clustering Methodologies: A Case South Sulawesi - Indonesia. International Journal of Sustainable Development and Planning, 18(6), 1659–1669. https://doi.org/10.18280/ijsdp.180602

Ezugwu, A. E.-S., Agbaje, M. B., Aljojo, N., Els, R., Chiroma, H., & Elaziz, M. A. (2020). A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering. IEEE Access, 8, 121089–121118. https://doi.org/10.1109/ACCESS.2020.3006173

Arunachalam, D., & Kumar, N. (2018). Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications, 111, 11–34. https://doi.org/10.1016/j.eswa.2018.03.007

Lee, K. J., Yun, S. T., Yu, S., Kim, K. H., Lee, J. H., & Lee, S. H. (2019). The combined use of self-organizing map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea. Journal of Hydrology, 569, 685–697. https://doi.org/10.1016/j.jhydrol.2018.12.031

Afzal, A., Ansari, Z., Alshahrani, S., Raj, A. K., Kuruniyan, M. S., Saleel, C. A., & Nisar, K. S. (2021). Clustering of COVID-19 data for knowledge discovery using c-means and fuzzy c-means. Results in Physics, 29, 104639. https://doi.org/10.1016/j.rinp.2021.104639

Sitepu, K. A. B., Sitompul, O. S., & Situmorang, Z. (2019). Analysis of Fuzzy C-Means and Analytical Hierarchy Process (AHP) Models Using Xie-Beni Index. 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM), 1–6. https://doi.org/10.1109/ICoSNIKOM48755.2019.9111538

Antczak, T. (2023). On optimality for fuzzy optimization problems with granular differentiable fuzzy objective functions. Expert Systems with Applications, 121891. https://doi.org/10.1016/j.eswa.2023.121891

Sukheja, D., Shah, J. A., Madhu, G., Kautish, K. S., Alghamdi, F. A., Yahia, I. S., M. El-Kenawy, E. S., & Mohamed, A. W. (2022). New Decision-Making Technique Based on Hurwicz Criteria for Fuzzy Ranking. Computers, Materials & Continua, 73(3), 4595–4609. https://doi.org/10.32604/cmc.2022.029122

Lan, L. T. H., Tuan, T. M., Ngan, T. T., Giang, N. L., Ngoc, V. T. N., & Van Hai, P. (2020). A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making. IEEE Access, 8, 164899–164921. https://doi.org/10.1109/ACCESS.2020.3021097

Xie, X., Fu, Y., Jin, H., Zhao, Y., & Cao, W. (2020). A novel text mining approach for scholar information extraction from web content in Chinese. Future Generation Computer Systems, 111, 859–872. https://doi.org/10.1016/j.future.2019.08.033

Sebestyén, V., Domokos, E., & Abonyi, J. (2020). Focal points for sustainable development strategies—Text mining-based comparative analysis of voluntary national reviews. Journal of Environmental Management, 263, 110414. https://doi.org/10.1016/j.jenvman.2020.110414

Song, H., Bei, J., Zhang, H., Wang, J., & Zhang, P. (2024). Hybrid algorithm of differential evolution and flower pollination for global optimization problems. Expert Systems with Applications, 237, 121402. https://doi.org/10.1016/j.eswa.2023.121402

Tan, W. H., & Mohamad-Saleh, J. (2023). A hybrid whale optimization algorithm based on equilibrium concept. Alexandria Engineering Journal, 68, 763–786. https://doi.org/10.1016/j.aej.2022.12.019

Ishtaiwi, A., Alshahwan, F., Jamal, N., Hadi, W., & Abuarqoub, M. (2021). A dynamic clause specific initial weight assignment for solving satisfiability problems using local search. Algorithms, 14(1). https://doi.org/10.3390/a14010012

Yin, C., Zhang, B., Liu, W., Du, M., Luo, N., Zhai, X., & Ba, T. (2022). Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis. ISPRS International Journal of Geo-Information, 11(7), 360. https://doi.org/10.3390/ijgi11070360

Mellor, S. (2022). A Construct Validity Study for the Union Intolerance Scale: Convergent-Discriminant Validity and Concurrent Criterion-Related Validity. Merits, 2(3), 210–223. https://doi.org/10.3390/merits2030015

Wu, Z., Zhao, Y., Wang, W., & Li, C. (2023). Adaptive weighted fuzzy clustering based on intra-cluster data divergence. Neurocomputing, 552, 126550. https://doi.org/10.1016/j.neucom.2023.126550

Deng, X., Yang, Y., & Jiang, W. (2023). Discrete choice models with Atanassov-type intuitionistic fuzzy membership degrees. Information Sciences, 622, 46–67. https://doi.org/10.1016/j.ins.2022.11.127

Saberi, H., Sharbati, R., & Farzanegan, B. (2022). A gradient ascent algorithm based on possibilistic fuzzy C-Means for clustering noisy data. Expert Systems with Applications, 191, 116153. https://doi.org/10.1016/j.eswa.2021.116153

Wiroonsri, N. (2023). Clustering performance analysis using a new correlation-based cluster validity index. Pattern Recognition, 1, 1–14. https://doi.org/10.1016/J.PATCOG.2023.10991

Zhou, B., Lu, B., & Saeidlou, S. (2022). A Hybrid Clustering Method Based on the Several Diverse Basic Clustering and Meta-Clustering Aggregation Technique. Cybernetics and Systems, 1–27. https://doi.org/10.1080/01969722.2022.2110682

Liang, J. (2022). Value Analysis and Realization of Artistic Intervention in Rural Revitalization Based on the Fuzzy Clustering Algorithm. Scientific Programming, 2022, 1–9. https://doi.org/10.1155/2022/3107440

Shen, L., Lu, J., Long, M., & Chen, T. (2019). Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering. Mathematical Problems in Engineering, 2019, 1–12. https://doi.org/10.1155/2019/2151284

Yuan, W., Li, J., Liu, C., & Shang, R. (2022). How to Realize the Integration of Urbanization and Rural Village Renewal Strategies in Rural Areas: The Case Study of Laizhou, China. Land, 11(12), 2161. https://doi.org/10.3390/land11122161

Ilbeigipour, S., Albadvi, A., & Akhondzadeh Noughabi, E. (2022). Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making. Informatics in Medicine Unlocked, 32, 101005. https://doi.org/10.1016/j.imu.2022.101005

Karthik, J., Tamizhazhagan, V., & Narayana, S. (2021). WITHDRAWN: Data leak identification using scattering search K Means in social networks. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.01.200

Jin, Q., Lin, N., & Zhang, Y. (2021). K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony. Algorithms, 14(2), 53. https://doi.org/10.3390/a14020053

Faisal, M., & Rahman, T. K. A. (2023). Optimally Enhancement Rural Development Support Using Hybrid Multy Object Optimization (MOO) and Clustering Methodologies: A Case South Sulawesi - Indonesia. International Journal of Sustainable Development and Planning, 18(6), 1659–1669. https://doi.org/10.18280/ijsdp.180602

Padilla-Rivera, A., do Carmo, B. B. T., Arcese, G., & Merveille, N. (2021). Social circular economy indicators: Selection through fuzzy delphi method. Sustainable Production and Consumption, 26, 101–110. https://doi.org/10.1016/j.spc.2020.09.015

Mendel, J. M., & Bonissone, P. P. (2021). Critical Thinking About Explainable AI (XAI) for Rule-Based Fuzzy Systems. IEEE Transactions on Fuzzy Systems, 29(12), 3579–3593. https://doi.org/10.1109/TFUZZ.2021.3079503

Fakir, M., Hicham, H., Chabi, M., & Sarfraz, M. (2020). Classification of Eyes Based on Fuzzy Logic. International Journal of Cognitive Informatics and Natural Intelligence, 14(4), 101–112. https://doi.org/10.4018/IJCINI.2020100106

Zhu, X., Pedrycz, W., & Li, Z. (2022). A Granular Approach to Interval Output Estimation for Rule-Based Fuzzy Models. IEEE Transactions on Cybernetics, 52(7), 7029–7038. https://doi.org/10.1109/TCYB.2020.3025668

Nowak-Brzezińska, A., & Wakulicz-Deja, A. (2019). Exploration of rule-based knowledge bases: A knowledge engineer’s support. Information Sciences, 485, 301–318. https://doi.org/10.1016/j.ins.2019.02.019

Ranasinghe, K., Sabatini, R., Gardi, A., Bijjahalli, S., Kapoor, R., Fahey, T., & Thangavel, K. (2022). Advances in Integrated System Health Management for mission-essential and safety-critical aerospace applications. Progress in Aerospace Sciences, 128, 100758. https://doi.org/10.1016/j.paerosci.2021.100758

Ibrahim, E. A., Salifu, D., Mwalili, S., Dubois, T., Collins, R., & Tonnang, H. E. Z. (2022). An expert system for insect pest population dynamics prediction. Computers and Electronics in Agriculture, 198, 107124. https://doi.org/10.1016/j.compag.2022.107124

Ji, W. (2021). Fuzzy implications in lattice effect algebras. Fuzzy Sets and Systems, 405, 40–46. https://doi.org/10.1016/j.fss.2020.04.021

Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: A measure driven view. Information Sciences, 507, 772–794. https://doi.org/10.1016/j.ins.2019.06.064

Faisal, M., Chaudhury, S., Sankaran, K. S., Raghavendra, S., Chitra, R. J., Eswaran, M., & Boddu, R. (2022). Faster R-CNN Algorithm for Detection of Plastic Garbage in the Ocean: A Case for Turtle Preservation. Mathematical Problems in Engineering, 2022, 1–11. https://doi.org/10.1155/2022/3639222



How to Cite

Faisal, M., Abd Rahman, T. K., Mulyadi, I., Aryasa , K., Irmawati, ., & Thamrin , M. (2024). A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem. Decision Making: Applications in Management and Engineering, 7(2), 132–171. https://doi.org/10.31181/dmame7220241023