Penerapan Data Mining Menggunakan K-Means Untuk Penentuan Penerima Bantuan Langsung Tunai (BLT) Dana Desa Pada Desa Cempaka Timur

Huswatun Hasanah; Pakarti Riswanto; Sidik Rahmatullah

doi:10.56327/jtksi.v6i3.1508

Penerapan Data Mining Menggunakan K-Means Untuk Penentuan Penerima Bantuan Langsung Tunai (BLT) Dana Desa Pada Desa Cempaka Timur

Huswatun Hasanah⁽¹⁾, Pakarti Riswanto⁽²⁾, Sidik Rahmatullah⁽³⁾,

(1) Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia
(2) Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia
(3) Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Corresponding Author

Abstract

Direct Cash Assistance is the provision of a sum of money to underprivileged people. This assistance is to help underprivileged communities in dealing with the corona virus (Covid19). In the distribution of Direct Cash Assistance, problems often occur as the distribution is not fully on target. BLT recipients were not on target because the determination process still used manual bookkeeping and the computerized process used MS. Excel so that the level of accuracy of data processing is still in doubt. Therefore to predict the determination of BLT recipients using the K-Means method and its implementation using MS. Excel and Python. Given that this method is one of those utilized to group data as a reference in making decisions on clustering sizable amounts of data, it is quite ideal for applying the predictions of BLT-DD Assistance recipients. From the results obtained in the processing of Direct Cash Assistance data using attributes that greatly influence, namely work, status of residence, income and number of dependents by obtaining two clusters, namely feasible or not feasible. In 2020 there were 38 eligible recipients of direct cash assistance and 72 inappropriate categories, in 2021 there were 30 eligible recipients of cash assistance and 70 inappropriate categories, in 2022 there were recipients of direct cash assistance with the category 29 people are eligible and 61 people are not eligible.

Keywords

Data Mining, K-Means, Village Fund Direct Cash Assistance

References

Y. Filki, “Jurnal Informatika Ekonomi Bisnis Algoritma K-Means Clustering dalam Memprediksi Penerima Bantuan Langsung Tunai ( BLT ) Dana Desa,” vol. 4, pp. 6–9, 2022, doi: 10.37034/infeb.v4i4.166.

I. Algoritma, C. Untuk, B. L. T. Di, and D. Tanjung, “178160037 - Sri Juwita - Fulltext,” 2022.

R. A. Farissa, R. Mayasari, and Y. Umaidah, “Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient di Puskesmas Karangsambung,” J. Appl. Informatics Comput., vol. 5, no. 2, pp. 109–116, 2021, doi: 10.30871/jaic.v5i1.3237.

A. Afandi, D. Nurdianah, P. C. Rejo, N. Bayes, and K. A. Dominan, “Naive Bayes Method and C4 . 5 in Classification of Birth Data,” vol. 16, no. 4, pp. 435–446, 2022.

R. Muliono and Z. Sembiring, “Data Mining Clustering Menggunakan Algoritma K-Means Untuk Klasterisasi Tingkat Tridarma Pengajaran Dosen,” CESS (Journal Comput. Eng. Syst. Sci., vol. 4, no. 2, pp. 2502–714, 2019.

E. E. Barito, J. T. Beng, and D. Arisandi, “Penerapan Algoritma C4.5 Untuk Klasifikasi Mahasiswa Penerima Bantuan Sosial Covid-19,” J. Ilmu Komput. dan Sist. Inf., vol. 10, no. 1, 2022, doi: 10.24912/jiksi.v10i1.17819.

E. Dewi et al., “Clustering Wilayah Dan Pelanggaran Berkendaraan Menggunakan Algoritma K-Means Pada Data Satlantas Polres Tasikmalaya Kota,” e-Jurnal JUSITI (Jurnal Sist. Inf. dan Teknol. Informasi), vol. 8–1, no. 1, pp. 1–11, 2019, doi: 10.36774/jusiti.v8i1.595.

N. N. F. R, D. S. Anggraeni, and U. Enri, “Pengelompokkan Data Kemiskinan Provinsi Jawa Barat Menggunakan Algoritma K-Means dengan Silhouette Coefficient,” Temat. J. Teknol. Inf. Komun., vol. 9, no. 1, pp. 29–35, 2022, [Online]. Available: http://jurnal.plb.ac.id/index.php/tematik/article/view/901

A. Muni, “Analisis Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Penjualan Sepeda Motor Studi Kasus PT. Alfa Scorpii,” Juti Unisi, vol. 4, no. 1, pp. 1–8, 2020, doi: 10.32520/juti.v4i1.1087.

I. N. M. Adiputra, “Clustering Penyakit Dbd Pada Rumah Sakit Dharma Kerti Menggunakan Algoritma K-Means,” Inser. Inf. Syst. Emerg. Technol. J., vol. 2, no. 2, p. 99, 2022, doi: 10.23887/insert.v2i2.41673.

O. Kinerja, E. C. U. Study, K. Mobil, and A. Dan, “Implementasi Algoritma K-Means Dan Algoritma Apriori,” vol. 1, no. 2, pp. 81–88, 2021.

C. Algorithm et al., “Classification and Clustering of Internet Quota Sales Data Using,” vol. 9, no. 2, pp. 268–283, 2023, doi: 10.26555/jiteki.v9i2.25970.

Raf Knowledge, MENGENAL MICROSOFT OFFICE 2010. 2010.

I. Algoritma and C. Untuk, “Implementasi algoritma c4.5 untuk memprediksi keterlambatan pembayaran sumbangan pembangunan pendidikan sekolah menggunakan python,” vol. 10, pp. 36–44, 2020.

M. Khandava Mulyadien and U. Enri, “Algoritma K-Means Untuk Pengelompokan Bantuan Langsung Tunai (BLT),” J. Ilm. Wahana Pendidik., vol. 2022, no. 12, pp. 198–210, 2022.

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