Open Access
Issue
E3S Web Conf.
Volume 499, 2024
The 1st Trunojoyo Madura International Conference (1st TMIC 2023)
Article Number 01006
Number of page(s) 7
Section Dense Matter
DOI https://doi.org/10.1051/e3sconf/202449901006
Published online 06 March 2024
  1. Y. Kustiyahningsih, E. Rahmanita, and J. Purnama, “Fuzzy Anp Method And Internal Business Perspective For Performance Measurement In,” Proceeding 1st IBSC Towar. Ext. Use Basic Sci. Enhancing Heal. Environ. Energy Biotechnol., pp. 289–294, 2017. [Google Scholar]
  2. Y. Kustiyahningsih, D. R. Anamisa, and F. A. Mufarroha, “The SME performance recommendation system facing the 4.0 industrial revolution uses the Fuzzy ANP method,” J. Phys. Conf. Ser., vol. 1836, no. 1, pp. 0–7, 2021, doi: 10.1088/1742-6596/1836/1/012036. [Google Scholar]
  3. A. Hidayah, “Implementing Data Clustering to Identify Capital Allocation for Small and Medium Sized Enterprises (SMEs),” ASEAN Mark. J., vol. 10, no. 1, 2021, doi: 10.21002/amj.v10i1.10627. [Google Scholar]
  4. S. O. Caballero-Morales, “Innovation as recovery strategy for SMEs in emerging economies during the COVID-19 pandemic,” Res. Int. Bus. Financ., vol. 57, no. May 2020, p. 101396, 2021, doi: 10.1016/j.ribaf.2021.101396. [CrossRef] [Google Scholar]
  5. Y. Kustiyahningsih and J. Purnama, “An integrated approach to determine mapping of SMEs during Covid-19 pandemic,” 1945. [Google Scholar]
  6. S. A. Mustaniroh, U. Effendi, R. L. R. Silalahi, T. Sari, and M. Ala, “Developing cluster strategy of apples dodol SMEs by integration K-means clustering and analytical hierarchy process method,” IOP Conf. Ser. Earth Environ. Sci., vol. 131, no. 1, pp. 1–6, 2018, doi: 10.1088/1755- 1315/131/1/012033. [CrossRef] [Google Scholar]
  7. F. Marisa, S. S. S. Ahmad, Z. I. M. Yusof, Fachrudin, and T. M. A. Aziz, “Segmentation model of customer lifetime value in Small and Medium Enterprise (SMEs) using K-Means Clustering and LRFM model,” Int. J. Integr. Eng., vol. 11, no. 3, pp. 169–180, 2019, doi: 10.30880/ijie.2019.11.03.018. [CrossRef] [Google Scholar]
  8. A. Hanafi, H. Riniwati, and A. Afandhi, “Fishing Gears Assessment Based on Code of Conduct for Responsible Fisheries ( CCRF ) at Probolinggo,” J- Pal, vol. 10, no. 2, pp. 107–114, 2019, doi: 10.21776/ub.jpal.2019.010.02.05. [Google Scholar]
  9. R. Hidayati, A. Zubair, A. Hidayat Pratama, L. Indana, P. Studi Sistem Informasi, and F. Teknologi Informasi, “Silhouette Coefficient Analysis in 6 Measuring Distances of K-Means Clustering,” Techno.Com, vol. 20, no. 2, pp. 186–197, 2021. [CrossRef] [Google Scholar]
  10. B. K. Khotimah, F. Irhamni, and T. Sundarwati, “A genetic algorithm for optimized initial centers K- means clustering in SMEs,” J. Theor. Appl. Inf. Technol., vol. 90, no. 1, pp. 23–30, 2016. [Google Scholar]
  11. S. B. H. Sakur, M. Silangen, and D. Tuwohingide, “Penerapan Algoritma K-Means Cluster dan Metode TOPSIS pada Pemilihan Mahasiswa kunjungan Industri,” J. Ilm. Tek. Inform. dan Sist. Inf., vol. 11, no. 3, pp. 851–860, 2022, [Online]. Available: http://ojs.stmik-banjarbaru.ac.id/index.php/jutisi/article/view/1045. [Google Scholar]
  12. J. Zhu, Z. Jiang, G. D. Evangelidis, C. Zhang, S. Pang, and Z. Li, “Efficient registration of multi- view point sets by K-means clustering,” Inf. Sci. (Ny)., vol. 488, pp. 205–218, 2019, doi: 10.1016/j.ins.2019.03.024. [CrossRef] [Google Scholar]
  13. E. Daniati and H. Utama, “Clustering K means for criteria weighting with improvement result of alternative decisions using SAW and TOPSIS,” 2019 4th Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2019, pp. 73–78, 2019, doi: 10.1109/ICITISEE48480.2019.9003858. [Google Scholar]
  14. Y. Kustiyahningish, E. Rahmanita, J. Purnama, and A. Info, “Fuzzy type-2 trapezoid methods for decision making salt farmer mapping,” vol. 4, no. 3, pp. 277–288, 2022. [Google Scholar]
  15. M. Elveny and Rahmadsyah, “Analisis Metode Fuzzy Analytic Hierarchy Process ( Fahp ) Dalam Menentukan Posisi Jabatan,” TECHSI - J. Penelit. Tek. Inform., vol. 4, no. 1, pp. 111–126, 2014. [Google Scholar]
  16. M. A. Elleuch, M. Anane, J. Euchi, and A. Frikha, “Hybrid fuzzy multi-criteria decision making to solve the irrigation water allocation problem in the Tunisian case,” Agric. Syst., vol. 176, no. January, p. 102644, 2019, doi: 10.1016/j.agsy.2019.102644. [CrossRef] [Google Scholar]
  17. M. Eghtesadifard, P. Afkhami, and A. Bazyar, “An integrated approach to the selection of municipal solid waste landfills through GIS, K-Means and multi-criteria decision analysis,” Environ. Res., vol. 185, no. March, p. 109348, 2020, doi: 10.1016/j.envres.2020.109348. [CrossRef] [Google Scholar]
  18. Y. Ida, E. Fujita, and T. Hirose, “Classification of volcano-seismic events using waveforms in the method of k-means clustering and dynamic time warping,” J. Volcanol. Geotherm. Res., vol. 429, no. June, p. 107616, 2022, doi: 10.1016/j.jvolgeores.2022.107616. [CrossRef] [Google Scholar]
  19. Y. Kustiyahningsih, “Integration interval type-2 fahp-ftopsis group decision- making problems for salt farmer recommendation,” pp. 1–25, 2021. [Google Scholar]
  20. Y. Kustiyahningsih, E. Rahmanita, Purbandini, and N. Kholifah, “Salt Farmer measurement performance system facing Covid-19 pandemic used interval type-2 FAHP Method,” J. Phys. Conf. Ser., vol. 2193, no. 1, 2022, doi: 10.1088/1742- 6596/2193/1/012012. [Google Scholar]
  21. R. Cerqueti and V. Ficcadenti, “Combining rank- size and k-means for clustering countries over the COVID-19 new deaths per million,” Chaos, Solitons and Fractals, vol. 158, p. 111975, 2022, doi: 10.1016/j.chaos.2022.111975. [CrossRef] [Google Scholar]
  22. S. Kayapinar Kaya and E. Aycin, “An integrated interval type 2 fuzzy AHP and COPRAS-G methodologies for supplier selection in the era of Industry 4.0,” Neural Comput. Appl., no. March, 2021, doi: 10.1007/s00521-021-05809-x. [Google Scholar]
  23. Y. Kustiyahningsih, J. Purnama, E. Rahmanita, and D. R. Anamisa, “Selection SMEs of Batik Bangkalan Using Fuzzy Interval Type-2 Method based on Group Support System,” ICRACOS 2021 - 2021 3rd Int. Conf. Res. Acad. Community Serv. Sustain. Innov. Res. Community Serv. Better Qual. Life Towar. Soc. 5, pp. 191–196, 2021, doi: 10.1109/ICRACOS53680.2021.9702091. [Google Scholar]
  24. Y. Kustiyahningsih, K. Sophan, N. R. Ummah, and J. Purnama, “MCGDM for selection of OSN participants using integration AHP and MOORA methods,” J. Phys. Conf. Ser., vol. 1836, no. 1, 2021, doi: 10.1088/1742-6596/1836/1/012037. [Google Scholar]
  25. U. Awan, L. Hannola, A. Tandon, R. K. Goyal, and A. Dhir, “Quantum computing challenges in the software industry. A fuzzy AHP-based approach,” Inf. Softw. Technol., vol. 147, p. 106896, 2022, doi: 10.1016/j.infsof.2022.106896. [CrossRef] [Google Scholar]
  26. K. Kiracı and E. Akan, “Aircraft selection by applying AHP and TOPSIS in interval type-2 fuzzy sets,” J. Air Transp. Manag., vol. 89, no. September 2020, 2020, doi: 10.1016/j.jairtraman.2020.101924. [Google Scholar]
  27. A. R. Ismail, N. Z. Abidin, and M. K. Maen, “Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare,” vol. 3, no. 2, pp. 143–152, 2022, doi: 10.18196/jrc.v3i2.13133. [Google Scholar]
  28. S. I. Khan, A. Sayed, and L. Hoque, “SICE: an improved missing data imputation technique,” J. Big Data, 2020, doi: 10.1186/s40537-020-00313-w. [Google Scholar]
  29. M. A. Kasri and H. Jati, “Combination of K-Means and Simple Additive Weighting in Deciding Locations and Strategies of University Marketing,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 6, no. 2, pp. 132–141, 2020, doi: 10.23917/khif.v6i2.11281. [Google Scholar]
  30. Z. L. Jiang et al., “Efficient two-party privacy- preserving collaborative k-means clustering protocol supporting both storage and computation outsourcing,” Inf. Sci. (Ny)., vol. 518, pp. 168–180, 2020, doi: 10.1016/j.ins.2019.12.051. [CrossRef] [Google Scholar]
  31. Y. Peng, Y. Zhang, G. Kou, and Y. Shi, “A multicriteria decision making approach for estimating the number of clusters in a data set,” PLoS One, vol. 7, no. 7, 2012, doi: 10.1371/journal.pone.0041713. [CrossRef] [Google Scholar]
  32. S. Sen, L. Sahoo, K. Tiwary, V. Simic, and T. Senapati, “Wireless Sensor Network Lifetime Extension via K-Medoids and MCDM Techniques in Uncertain Environment,” Appl. Sci., vol. 13, no. 5, 2023, doi: 10.3390/app13053196. [Google Scholar]
  33. S. Butdee and P. Phuangsalee, “Uncertain risk assessment modelling for bus body manufacturing supply chain using AHP and fuzzy AHP,” Procedia Manuf., vol. 30, pp. 663–670, 2019, doi: 10.1016/j.promfg.2019.02.094. [CrossRef] [Google Scholar]

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