Sistem Aplikasi Cerdas Klasterisasi Penerima Bantuan Covid-19
DOI:
https://doi.org/10.30812/matrik.v21i2.1716Keywords:
Aplikasi cerdas, Bantuan, Klasterisasi, K-Means, Wabah Covid-19Abstract
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
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[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-MartÃnez 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.
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[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.
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[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.
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[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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2022-03-31
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[1]
A. Anggrawan, D. Kurnianingsih, and C. Satria, “Sistem Aplikasi Cerdas Klasterisasi Penerima Bantuan Covid-19”, MATRIK, vol. 21, no. 2, pp. 367–378, Mar. 2022, doi: 10.30812/matrik.v21i2.1716.
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