Application of Fuzzy C-Means and Weighted Scoring Methods for Mapping Blankspot Villages in Pemalang Regency

Authors

  • Imam Adiyana Department of Statistics, IPB University, Indonesia
  • I Made Sumertajaya Department of Statistics, IPB University, Indonesia
  • Farit M Afendi Department of Statistics, IPB University, Indonesia

DOI:

https://doi.org/10.29244/ijsa.v6i1p77-89

Keywords:

blankspot, data validation, fuzzy c-means, weighted scoring

Abstract

Covid-19 pandemic affects habits people around the world. The education sector in Indonesia is also undergoing policy changes, namely policy of transitioning face-to-face teaching and learning process to distance learning process (PJJ/online learning). Several studies have been conducted to examine the constraints PJJ process, resulting in finding that quality of internet network is majority obstacle in PJJ process. Conditions where there is no internet network in an area is commonly called a blankspot. In order to minimize the problem of blankspots, President and Ministry of Communication and Informatics of Indonesia realized the program "Indonesia is free signals to the corners of the country". This program involves all districts in Indonesia to conduct network quality surveys in the smallest areas of the village.  Basically, network quality survey activities require relatively no small resources and costs. So as to conduct the efficiency of field survey activities, early detection of village blankspot status is required based on the characteristics blankspot village in general. While the commonly used method of grouping village based on village characteristics is the fuzzy c-means and weighted scoring method. These two methods were chosen because they have good cluster convergence rate and easily interpreted display results of the group by user in the form diagrams and scores. This study aims to prove that fuzzy c-means and weighted scoring method are good for grouping cases of blankspot villages according to previous studies with different cases. The result comparison goodness value of clustering, it is known that fuzzy c-means method more suitable for clustering characteristics blankspot village than the k-means method. Meanwhile, weighted scoring method cannot be said better method for village classification than the decision tree method.

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References

Abdou, H., & Pointon, J. (2011). Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of The Literature. Intelligent Systems in Accounting Finance and Management, 18(2):59-88. Doi: 10.1002/isaf.325.

Agusta, Y. (2007). K means-Penerapan, Permasalahan dan Metode Terkait. Jurnal Sistem dan Informatika, 3:47-60.

Bezdek, J. (1981). Pattern Recognition with Fuzzy Objective Function Algorithm. New York: Plenum Press.

Coman, C., Tiru, L.G., Schmitz, L.M., Stanciu, C., & Bularca, M.C. (2020). Online Teaching and Learning in Higher Education During the Coronavirus Pandemic: Students Prespective. Sustainability Journal, 12:1-24. doi: 10.3390/su122410367.

Dillon, W.R., & Goldstein, M. (1984). Multivariate Analysis Method and Applications. Canada : John Wiley & Sons.

Fikri, M., Ananda, M.Z., Faizah, N., Rahmani, R., Elian, S.A., & Suryanda, A. (2021). Kendala dalam Pembelajaran Jarak Jauh di Masa Pandemi Covid-19: Sebuah Kajian Kritis. Jurnal Education and Development Institut Pendidikan Tapanuli Selatan, 9(1):145-148.

Kondadadi, R., & Kozma, R. (2002). A Modified Fuzzy Art for Soft Document Clustering. Memphis: Division of Computer Science, University of Memphis.

Mattjik, A.A., & Made, I.S. (2011). Analisis Peubah Ganda dengan Menggunakan SAS Edisi Pertama. Departemen Statistika FMIPA-IPB.

Morpus, N. (2021). Step by Step Guide for Using A Weighted Scoring Model. [accessed 2021 February 04]. https://blueprint.fool.com/.

Rajkumar, K., Yesubabu, A., & Subrahmanyam, K. (2019). Fuzzy Clustering and Fuzzy Cmeans Partition Cluster Analysis and Validation Studies on A Subset of Citescore Dataset. IJECE, 9(4):2760-2770. doi:10.11591/ijece.v9i4.pp2760-2770.

Sarika, S. (2012). Server Selection by Using Weighted Sum and Revised Weighted Sum Decision Models. International Journal ICT, 2(6) : 499-511.

Sivarathri, S., & Govardhan, A. (2014). Experiments on Hypothesis “Fuzzy K-Means is Better than K-Means for Clusteringâ€. IJDKP, 4(5): 21-34.

Surkhali, B., & Garbuja, C.K. (2020). Virtual Learning During Covid-19 Pandemic : Pros and Cons. JLMC, 8(1). doi:10.22502/jlmc.v8i1.345.

Sutrisno, E. (2020). Merdeka Sinyal Hingga Pelosok Negeri. [accessed 2021 June 20].https://www.jawapos.com/nasional/05/08/2020/ketersediaan-internet-jadi-kendala-pembelajaran-jarak-jauh/

Wang, F., Franco-Penya, H.H., Kelleher, J., Pugh, J., & Rose, R. (2017). An Analysis of the Application of Simplified Silhouette to the Evaluation of K-Means Clustering Validity. Lecture Notes in Computer Science. doi: 10.1007/978-3-319-62416-7_21.

Zhang, H., Zeng, R., Chen, L., & Zhang, S. (2020). Research on Personal Credit Scoring Model Based on Multi-Source Data. Journal of Physics, 1-15. Doi:10. 1088/1742-6596/ 1437/ 1/012053.

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Published

2022-05-31

How to Cite

Adiyana, I. ., Sumertajaya, I. M., & Afendi, F. M. . (2022). Application of Fuzzy C-Means and Weighted Scoring Methods for Mapping Blankspot Villages in Pemalang Regency. Indonesian Journal of Statistics and Its Applications, 6(1), 77–89. https://doi.org/10.29244/ijsa.v6i1p77-89

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