Sistem Aplikasi Cerdas Klasterisasi Penerima Bantuan Covid-19
Abstract
Wabah Covid-19 berakibat pada krisis ekonomi masyarakat dan menciptakan kemiskinan dan pengangguran. Hal ini menyebabkan pemerintah Indonesia turun tangan memberikan bantuan Covid-19 bagi masyarakat yang paling terdampak buruk. Namun yang menjadi kesulitan adalah dalam menentukan dengan tepat serta benar kandidat yang layak dan yang tidak layak sebagai penerima bantuan yang masih dilakukan secara manual. Karenanya dibutuhkan solusi untuk mengatasinya. Itulah sebabnya penelitian ini bertujuan membangun sistem dan aplikasi cerdas yang bisa melakukan pengklasterkan kandidat penerima bantuan Covid-19 yang layak, kurang layak dan tidak layak sebagai penerima bantuan Covid-19. Metode yang digunakan dalam penelitian ini untuk klasterisasi adalah metode data mining k-means. Hasil penelitian ini adalah pengklasteran kelayakan penerima bantuan Covid-19 terbagi dalam klaster C0 (penerima bantuan yang layak) sebanyak 53, klaster C1 (cukup layak menerima bantuan) sebanyak 71, dan klaster yang tidak layak sebagai penerima bantuan (C2) sebanyak 76 dari 200 data pengujian. Aplikasi cerdas ang dibangun juga menunjukkan hasil yang sama dengan pengklasteran yang di lakukan dengan menerapakan metode k-means, sehingga aplikasi cerdas yang dibangun berguna untuk komputerisasi klasterisasi yang layak, kurang layak dan tidak layak sebagai penerima bantuan Covid-19.
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References
[2] C. Wang et al., “The impact of COVID-19 pandemic on physical and mental health of Asians: A study of seven middle-income countries in Asia,” PLoS ONE, vol. 16, no. 2 Febuary, pp. 1–20, 2021, doi: 10.1371/journal.pone.0246824.
[3] S. Brown, “The impact of COVID-19 on development assistance,” International Journal, vol. 76, no. 1, pp. 42–54, 2021, doi: 10.1177/0020702020986888.
[4] M. A. Jibrin, M. N. Musa, and S. Tahir, “Development of E-Scholarship System,” International Journal of Computer and Information Technology, vol. 7, no. 2, pp. 523–530, 2016.
[5] R. Sparrow, T. Dartanto, and R. Hartwig, “Indonesia Under the New Normal: Challenges and the Way Ahead,” Bulletin of Indonesian Economic Studies, vol. 56, no. 3, pp. 269–299, 2020, doi: 10.1080/00074918.2020.1854079.
[6] D. Laureiro-Martínez and S. Brusoni, “Cognitive Flexibility and Adaptive Decision-Making: Evidence from a laboratory study of expert decision-makers,” Strategic Management Journal, vol. 39, no. 4, pp. 1031–1058, 2018.
[7] P. H. Dos Santos, S. M. Neves, D. O. Sant’Anna, C. H. de Oliveira, and H. D. Carvalho, “The analytic hierarchy process supporting decision making for sustainable development: An overview of applications,” Journal of Cleaner Production, vol. 212, pp. 119–138, 2019, doi: 10.1016/j.jclepro.2018.11.270.
[8] T. D. Puspitasari, E. O. Sari, P. Destarianto, and H. Y. Riskiawan, “Decision Support System for Determining Scholarship Selection using an Analytical Hierarchy Process,” in The 2nd International Joint Conference on Science and Technology (IJCST) 2017, 2018, no. 11, pp. 61–67.
[9] C. Yuan and H. Yang, “Research on K-Value Selection Method of K-Means Clustering Algorithm,” Multidisciplinary Scientific Journal, vol. 2, no. 2, pp. 226–235, 2019, doi: 10.3390/j2020016.
[10] C. Satria and A. Anggrawan, “Aplikasi K-Means berbasis Web untuk Klasifikasi Kelas Unggulan,” Matrik : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 1, pp. 111–124, 2021, doi: 10.30812/matrik.v21i1.1473.
[11] C. Wu et al., “k-Means Clustering Algorithm and Its Simulation Based on Distributed Computing Platform,” Journal of Wiley, vol. 2021, pp. 1–10, 2021, doi: 10.1155/2021/9446653.
[12] A. Anggrawan, N. Ibrahim, S. Muslim, and C. Satria, “Interaction between Learning Style and Gender in Mixed Learning with 40 % Face-to-face Learning and 60 % Online Learning,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 5, pp. 407–413, 2019.
[13] A. Anggrawan, C. Satria, N. Gusti, and A. Dasriani, “Reciprocity Effect between Cognitive Style and Mixed Learning Method on Computer Programming Skill,” Journal of computer Science, vol. 17, no. 9, pp. 814–824, 2021, doi: https://doi.org/10.3844/jcssp.2021.814.824.
[14] A. Anggrawan, C. Satria, C. K. Nuraini, Lusiana, N. G. A. Dasriani, and Mayadi, “Machine Learning for Diagnosing Drug Users and Types of Drugs Used,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 11, pp. 111–118, 2021.
[15] R. D. F. S. M. Russo and R. Camanho, “Criteria in AHP: A systematic review of literature,” Procedia Computer Science, vol. 55, no. Itqm, pp. 1123–1132, 2015, doi: 10.1016/j.procs.2015.07.081.
[16] Z. Zhang, “Introduction to machine learning: K-nearest neighbors,” Annals of Translational Medicine, vol. 4, no. 11, pp. 1–7, 2016, doi: 10.21037/atm.2016.03.37.
[17] Z. Karaca, “The Cluster Analysis in the Manufacturing Industry With K-Means Method: an Application for Turkey,” Eurasian Journal of Economics and Finance, vol. 6, no. 3, pp. 1–12, 2018, doi: 10.15604/ejef.2018.06.03.001.
[18] Zaitun, Mustakim, I. Kamila, and S. S. Helma, “Implementation of MOORA Method for Determining Prospective Smart Indonesia Program Funds Recipients,” International Journal of Engineering and Advanced Technology, vol. 9, no. 2, pp. 1922–1925, 2019, doi: 10.35940/ijeat.b2860.129219.
[19] H. J. de P. Alves, F. A. F. Henrique José de Paula Alves, K. P. de Lima, B. D. de O. Batista, and T. J. Fernandes, “The COVID-19 pandemic in Brazil an application of the k-means clustering method,” Research, Society and Developmen, vol. 9, no. 10, pp. 1–21, 2020.
[20] L. Ganda, R. Putra, and A. Anggrawan, “Pengelompokan Penerima Bantuan Sosial Masyarakat dengan Metode K-Means,” Matrik: Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer, vol. 21, no. 1, pp. 205–214, 2021, doi: 10.30812/matrik.v21i1.1554.
[21] O. Marban, G. Mariscal, and J. Segovia, “A Data Mining & Knowledge Discovery Process Model,” no. January, Vienna, Austria: Data Mining and Knowledge Discovery in Real Life Applications, 2009, p. 438.
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