The Comparison of the Social Welfare Data Classification Algorithm for Bantul Regency

  • Erfin Nur Rohma Khakim Magister Teknologi Informasi Universitas Teknologi Yogyakarta

Abstract

Today, the main problem in the economy sector in Indonesia is related to poverty alleviation. In Bantul Regency, poverty alleviation relies on the poverty data. The Social Welfare Integrated Data set by the Ministry of Social Affairs of the Republic of Indonesia is currently can not describe the classification of the poverty. It is either the causes of the delay in handling the poverty problem in Bantul Regency. Mapping of the poverty data has a major impact to the accuracy of targets for poverty reduction programs. One of the mappings that can be do for the problems of poverty data is by using the classification method. With this classification, the poverty data can be classified into several groups according to their circumstances. These groups include the very poor, the poor, the vulnerable and the near poor. In this study, two classification methods will be tried, called Naive Bayes and K-nearest neighboor (KNN) to compare the best results based on a measurement method.

References

F. Fajriwati, “Dampak Perekonomian Terhadap Masyarakat Miskin Di Lingkungan Kampung Nelayan Kecamatan Medan Labuhan,” Ekon. J. Ilmu Ekon. dan Stud. Pembang., vol. 16, no. 2, pp. 145–154, 2016, doi: 10.30596/ekonomikawan.v16i2.942.
[2] K. DKB Ditjen Dukcapil, “Statistik Penduduk DIY,” Biro Tata Pemerintahan Setda DIY, 2020. https://kependudukan.jogjaprov.go.id/statistik/penduduk/jumlahpenduduk/14/0/12/04/.clear
[3] B. BPS, “Tabel Kemiskinan,” BPS Kab Bantul, 2021. https://bantulkab.bps.go.id/subject/23/kemiskinan.html#subjekViewTab3
[4] R. Kemensos, “Peraturan Menteri Sosial tentang Pengelolaan Data Terpadu Kesejahteraan Sosial,” BN. 2021 No. 578, jdih.kemensos.go.id, vol. 4, no. 1, pp. 1–2, 2021, [Online]. Available: https://peraturan.bpk.go.id/Home/Details/171535/permensos-no-3-tahun-2021
[5] Kusrini and E. T. Luthfi, Algoritma Data Mining. Andi Offset, 2009.
[6] A. Jananto, “Algoritma Naive Bayes untuk Mencari Perkiraan Waktu Studi Mahasiswa,” OJS Unisbank J., 2013.
[7] A. Wanto et al., Data Mining : Algoritma dan Implementasi. Yayasam Kita Menulis, 2020.
[8] H. Annur, “Klasifikasi Masyarakat Miskin Menggunakan Metode Naive Bayes,” Ilk. J. Ilm., vol. 10, no. 2, pp. 160–165, 2018, doi: 10.33096/ilkom.v10i2.303.160-165.
[9] A. Gravita, “Mengenal Algoritma Naive Bayes dan Kegunaannya,” PT Semua Mahir Teknologi (SMART), 2022. https://codingstudio.id/algoritma-naive-bayes/
[10] A. maulana Ismail, “Cara Kerja Algoritma k-Nearest Neighboor,” Bee Solution Partners, 2018. https://medium.com/bee-solution-partners/cara-kerja-algoritma-k-nearest-neighbor-k-nn-389297de543e
[11] A. Khairi, A. F. Ghozali, and A. D. N. Hidayah, “Implementasi K-Nearest Neighbor (KNN) untuk Mengklasifikasi Masyarakat Pra-Sejahtera Desa Sapikerep Kecamatan Sukapura,” TRILOGI J. Ilmu Teknol. Kesehatan, dan Hum., vol. 2, no. 3, pp. 319–323, 2021, doi: 10.33650/trilogi.v2i3.2878.
[12] A. Umar, “Rapidminer, Definisi dan Fitur-fiturnya,” 2021. https://www.abdumar.com/2021/03/rapidminer-definisi-dan-fitur-fiturnya.html?m=1
[13] Alfarisi, “Data Preprocessing - Konsep Pembelajaran Data Mining,” Steemit, 2017. https://steemit.com/education/@alfarisi/data-preprocessing-konsep-pembelajaran-data-mining
[14] T. & I. Hutapea, “Penerapan Algoritma Modified K-Nearest Neighbour Pada Pengklasifikasian Penyakit Kejiwaan Skizofrenia,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, pp. 3957–3961, 2018.
Published
2022-10-27
How to Cite
KHAKIM, Erfin Nur Rohma. The Comparison of the Social Welfare Data Classification Algorithm for Bantul Regency. Jurnal Processor, [S.l.], v. 17, n. 2, p. 91 - 100, oct. 2022. ISSN 2528-0082. Available at: <http://ejournal.stikom-db.ac.id/index.php/processor/article/view/1222>. Date accessed: 26 jan. 2023. doi: https://doi.org/10.33998/processor.2022.17.2.1222.
Section
Articles