Classification of COVID-19 Aid Recipients in Kasomalang District Using the K-Nearest Neighbor Method
Abstract
The impact of the Coronavirus, also known as COVID-19, which emerged in 2019, has not only threatened public health but also affected the global economy, including Indonesia. The government has initiated various aid programs to assist the community during the COVID-19 pandemic. These aids are expected to alleviate the economic burden on the affected population. One such aid program is the Direct Cash Assistance (Bantuan Langsung Tunai/BLT) from the Village Fund, which has been distributed since the onset of COVID-19 in Indonesia. However, the distribution of BLT has encountered several issues, including misidentification of recipients and double or excessive distribution beyond the established criteria. To address these issues, data mining for the classification of aid recipients can be employed. This study uses the K-Nearest Neighbor (KNN) method for data mining classification to classify residents' data with new patterns, ensuring aid distribution aligns with the criteria and eliminating double recipients. The application of K-Nearest Neighbor to the population data in Kasomalang District yields optimal performance, with evaluation results showing an accuracy of 96%, precision of 0.98, recall of 0.96, and F1 score of 0.97 using the confusion matrix method.
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References
DJP, “Direktorat Jenderal Pajak Tanggap COVID-19,” Direktorat Jenderal Pajak. Accessed: Jun. 26, 2024. [Online]. Available: http://www.pajak.go.id/id/covid19
B. P. S. Indonesia, “Ekonomi Indonesia Triwulan II 2020 Turun 5,32 Persen.” Accessed: Jun. 26, 2024. [Online]. Available: https://www.bps.go.id/id/pressrelease/2020/08/05/1737/ekonomi-indonesia-triwulan-ii-2020-turun-5-32-persen.html
F. Y. D. Marta and R. Nurlitasari, “Implementasi Penyaluran Bantuan Langsung Tunai Dana Desa di Era Pandemi Covid-19 di Kabupaten Sigi 2020,” Jurnal Terapan Pemerintahan Minangkabau, vol. 1, no. 1, Art. no. 1, Jun. 2021, doi: 10.33701/jtpm.v1i1.1870.
W. Rahmansyah, R. A. Qadri, R. R. A. Sakti, and S. Ikhsan, “Pemetaan Permasalahan Penyaluran Bantuan Sosial Untuk Penanganan Covid-19 Di Indonesia,” Jurnal Pajak dan Keuangan Negara (PKN), vol. 2, no. 1, Art. no. 1, Sep. 2020, doi: 10.31092/jpkn.v2i1.995.
C. E. F. Maun, “Efektivitas Bantuan Langsung Tunai Dana Desa Bagi Masyarakat Miskin Terkena Dampak Covid-19 Di Desa Talaitad Kecamatan Suluun Tareran Kabupaten Minahasa Selatan,” POLITICO: Jurnal Ilmu Politik, vol. 9, no. 2, Art. no. 2, Jul. 2020, Accessed: Jun. 26, 2024. [Online]. Available: https://ejournal.unsrat.ac.id/v3/index.php/politico/article/view/30702
N. Widiawati, B. N. Sari, and T. N. Padilah, “Clustering Data Penduduk Miskin Dampak Covid-19 Menggunakan Algoritma K-Medoids,” Journal of Applied Informatics and Computing, vol. 6, no. 1, Art. no. 1, May 2022, doi: 10.30871/jaic.v6i1.3266.
A. Mustofa, O. Okfalisa, E. P. Cynthia, Y. Yelfi, and S. K. Gusti, “Klasifikasi Penerima Bantuan Covid-19 Menggunakan Metode Weighted K-Nearest Neighbour,” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 5, no. 3, Art. no. 3, Jun. 2022, doi: 10.32672/jnkti.v5i3.4399.
N. Nafisah, R. I. Adam, and C. Carudin, “Klasifikasi K-NN dalam Identifikasi Penyakit COVID-19 Menggunakan Ekstraksi Fitur GLCM,” Journal of Applied Informatics and Computing, vol. 5, no. 2, Art. no. 2, Oct. 2021, doi: 10.30871/jaic.v5i2.3258.
D. E. Kurniawan and A. Fatulloh, “Clustering of Social Conditions in Batam, Indonesia Using K-Means Algorithm and Geographic Information System,” International Journal of Earth Sciences and Engineering (IJEE), vol. 10, no. 5, pp. 1076–1080, 2017
C. J. Costa and J. T. Aparicio, “A Methodology to Boost Data Science in the Context of COVID-19,” in Advances in Parallel & Distributed Processing, and Applications, H. R. Arabnia, L. Deligiannidis, M. R. Grimaila, D. D. Hodson, K. Joe, M. Sekijima, and F. G. Tinetti, Eds., Cham: Springer International Publishing, 2021, pp. 65–75. doi: 10.1007/978-3-030-69984-0_7
C. Schröer, F. Kruse, and J. M. Gómez, “A Systematic Literature Review on Applying CRISP-DM Process Model,” Procedia Computer Science, vol. 181, pp. 526–534, Jan. 2021, doi: 10.1016/j.procs.2021.01.199.
N. Hotz, “What is CRISP DM?,” Data Science Process Alliance. Accessed: Jun. 26, 2024. [Online]. Available: https://www.datascience-pm.com/crisp-dm-2/
F. Binsar and T. Mauritsius, “Mining of Social Media on Covid-19 Big Data Infodemic in Indonesia,” J. Comput. Sci, vol. 16, no. 11, pp. 1598–1609, 2020.
W. Y. Ayele, “Adapting CRISP-DM for idea mining: a data mining process for generating ideas using a textual dataset,” International Journal of Advanced Computer Sciences and Applications, vol. 11, no. 6, pp. 20–32, 2020.
J. S. Saltz, “CRISP-DM for data science: strengths, weaknesses and potential next steps,” in 2021 IEEE International Conference on Big Data (Big Data), IEEE, 2021, pp. 2337–2344. Accessed: Jun. 26, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9671634/
N. Wang, R. Liang, X. Zhao, and Y. Gao, “Cost-sensitive hypergraph learning with f-measure optimization,” IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 2767–2778, 2021.
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