Penerapan Data Mining Untuk Klasifikasi Penerima Dana Bantuan Sosial Dengan Menggunakan Algoritma K-Nearest Neighbor
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Agung Triayudi
*
Universitas Nasional, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
The Social Assistance Fund (Bansos) is a government program carried out to assist in eradicating community poverty in Indonesia and improving the welfare of families in Indonesia. Social Assistance Funds (Bansos) are distributed from the central ministry, then forwarded to local social services and then distributed to the community through each sub-district office. After data collection is carried out, the process of determining and selecting the families who receive Social Assistance Funds (Bansos) is carried out. However, in the implementation process there were several obstacles, one of which was that the provision of Social Assistance Funds (Bansos) was still not on target for families who deserved to receive Social Assistance Funds (Bansos). This problem is an important matter that must be resolved, this is because the main aim of the Social Assistance Fund (Bansos) program is to help eradicate poverty in Indonesia. Reviewing and processing data again based on previous data can be completed using one of the computer techniques. Data mining is a technique used to reprocess data. Data processing returns to data mining based on data previously stored in a data collection or data warehouse. Classification is part of data mining which aims to find out certain models of data so that they can be divided into several classes or groups. The K-Nearest Neighbor (K-NN) algorithm is part of a data mining technique which aims to divide data into certain groups. The results obtained in the research are the K value used in the research, namely K=7, the result of the family data grouping process which has just determined that the family received Social Assistance Funds (Bansos).
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References
H. Mubarok, S. Murni, and M. M. Santoni, “Penerapan Algoritma K-Nearest Neighbor untuk Klasifikasi Tingkat Kematangan Buah Tomat Berdasarkan Fitur Warna,” Semin. Nas. Mhs. Ilmu Komput. dan Apl. Jakarta-Indonesia, no. April, pp. 773–782, 2021.
A. Salsabila, R. Yunita, and C. Rozikin, “Identifikasi Citra Jenis Bunga menggunakan Algoritma KNN dengan Ekstrasi Warna HSV dan Tekstur GLCM,” Technomedia J., vol. 6, no. 1, pp. 124–137, 2021, doi: 10.33050/tmj.v6i1.1667.
A. Pratama, F. A. Ma’ruf, Iin, A. Rizki Rinaldi, and Faturrhohman, “Klasifikasi Penerima Beasiswa Dengan Menggunakan Algoritma K Nearest Neighbor,” J. Data Sci. Inform., vol. 1, no. 1, pp. 11–15, 2021.
A. Christopher and T. M. S. Mulyana, “Klasifikasi Tumbuhan Angiospermae Menggunakan Algoritma K-Nearest Neighbor Berdasarkan Pada Bentuk Daun,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 7, no. 4, pp. 1233–1243, 2022, doi: 10.29100/jipi.v7i4.3211.
S. Z. H. Rukmana, A. Aziz, and W. Harianto, “Optimasi Algoritma K-Nearest Neighbor (Knn) Dengan Normalisasi Dan Seleksi Fitur Untuk Klasifikasi Penyakit Liver,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 439–445, 2022.
A. E. Gumanti, Taslim, S. Handayani, and D. Toresa, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Topik Skripsi Mahasiswa di Fakultas Ilmu Komputer,” JITaCS J. Inf. Technol. Comput. Sci., vol. 1, no. 2, pp. 44–50, 2022.
LOUIS MADAERDO SOTARJUA and DIAN BUDHI SANTOSO, “Perbandingan Algoritma KNN, Decision Tree Dan Random Forest Pada Data Imbalanced Class Untuk Klasifikasi Promosi Karyawan,” J. INSTEK (Informatika Sains dan Teknol., vol. 7, no. 2, pp. 192–200, 2022, doi: 10.24252/instek.v7i2.31385.
M. D. Hendriyanto and B. N. Sari, “Penerapan Algoritma K-Nearest Neighbor Dalam Klasifikasi Judul Berita Hoax,” J. Ilm. Inform., vol. 10, no. 02, pp. 80–84, 2022, doi: 10.33884/jif.v10i02.5477.
R. Nuraini, “Klasifikasi Citra Jenis Kapasitor Menggunakan Kombinasi Algoritma K-Nearest Neighbor dan Principal Component Analysis,” J. Comput. Syst. Informatics, vol. 3, no. 3, pp. 133–140, 2022, doi: 10.47065/josyc.v3i3.1694.
R. S. Amardita, A. Adiwijaya, and M. D. Purbolaksono, “Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 1, p. 62, 2022, doi: 10.30865/jurikom.v9i1.3793.
W. Ramdhani, D. Bona, R. B. Musyaffa, and C. Rozikin, “Klasifikasi Penyakit Kangker Payudara Menggunakan Algoritma K-Nearest Neighbor,” J. Ilm. Wahana Pendidik., vol. 8, no. 12, pp. 445–452, 2022.
H. A. Dwi Fasnuari, H. Yuana, and M. T. Chulkamdi, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Penyakit Diabetes Melitus,” Antivirus J. Ilm. Tek. Inform., vol. 16, no. 2, pp. 133–142, 2022, doi: 10.35457/antivirus.v16i2.2445.
M. Y. Putra and D. I. Putri, “Pemanfaatan Algoritma Naïve Bayes dan K-Nearest Neighbor Untuk Klasifikasi Jurusan Siswa Kelas XI,” J. Tekno Kompak, vol. 16, no. 2, pp. 176–187, 2022.
A. H. Nasrullah, “Implementasi Algoritma Decision Tree Untuk Klasifikasi Data Peserta Didik,” J. Pilar Nusa Mandiri, vol. 7, no. 2, p. 217, 2021.
A. S. Fikri, F. Setiawan, W. A. Violando, A. D. Muttaqin, and F. Rahmawan, “Analisis Penutupan Lahan menggunakan Google Earth Engine (GEE) dengan Metode Klasifikasi Terbimbing (Studi kasus: WIilayah Pesisir Kabupaten Lamongan, Provinsi Jawa Timur),” in Prosiding FIT ISI Vol, 2021, vol. 1, pp. 1–102, [Online]. Available: http://eprints.itn.ac.id/1418/.
A. Damuri, U. Riyanto, H. Rusdianto, and M. Aminudin, “Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako,” J. Ris. Komput., vol. 8, no. 6, pp. 219–225, 2021, doi: 10.30865/jurikom.v8i6.3655.
Jeprianto and R. A. Aziz, “Implementasi Algoritma Rough Set Dan Naive Bayes Untuk Mendapatkan Rule Dalam Menyeleksi Pemohon Bantuan Fasilitas Rumah Ibadah (Studi Kasus : Pemerintah Kabupaten Pringsewu),” JTKSI, vol. 03, no. 93, pp. 74–83, 2020.
B. N. Azmi, A. Hermawan, and D. Avianto, “Analisis Pengaruh PCA Pada Klasifikasi Kualitas Air Menggunakan Algoritma K-Nearest Neighbor dan Logistic Regression,” J. Sist. dan Teknol. Inf., vol. 7, no. 2, pp. 94–103, 2022, [Online]. Available: http://jurnal.unmuhjember.ac.id/index.php/JUSTINDO/article/view/8190%0Ahttp://jurnal.unmuhjember.ac.id/index.php/JUSTINDO/article/download/8190/4143.
K. F. Margolang, M. M. Siregar, S. Riyadi, and Z. Situmorang, “Analisa Distance Metric Algoritma K-Nearest Neighbor Pada Klasifikasi Kredit Macet,” J. Inf. Syst. Res., vol. 3, no. 2, pp. 118–124, 2022, doi: 10.47065/josh.v3i2.1262.
S. T. Rizaldi and M. Mustakim, “Perbandingan Teknik Pembagian Data untuk Klasifikasi Sarana Akses Air pada Algoritma K- Nearest Neighbor dan Naïve Bayes Classifier,” Semin. Nas. Teknol. Informasi, Komun. dan Ind. 12, pp. 130–137, 2020.
S. Nurjanah, A. M. Siregar, and D. S. Kusumaningrum, “Penerapan Algoritma K – Nearest Neighbor (KNN) Untuk Klasifikasi Pencemaran Udara Di Kota Jakarta,” Sci. Student J. Information, Technol. Sci., vol. 1, no. 2, pp. 71–76, 2020, [Online]. Available: http://journal.ubpkarawang.ac.id/mahasiswa/index.php/ssj/article/view/14.
M. N. Maskuri, Harliana, K. Sukerti, and R. M. H. Bhakti, “Penerapan Algoritma K-Nearest Neighbor (KNN) untuk Memprediksi Penyakit Stroke,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 4, no. 1, pp. 130–140, 2022.
I. A. Nikmatun and I. Waspada, “Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor,” J. SIMETRIS, vol. 10, no. 2, pp. 421–432, 2019.
M. S. Mustafa and I. W. Simpen, “Implementasi Algoritma K-Nearest Neighbor ( KNN ) Untuk Memprediksi Pasien Terkena Penyakit Diabetes Pada Puskesmas Manyampa Kabupaten Bulukumba,” Semin. Ilm. Sist. Inf. Dan Teknol. Inf., vol. VIII, no. 1, pp. 1–10, 2019, [Online]. Available: https://ejurnal.dipanegara.ac.id/index.php/sisiti/article/view/1 -10/68.
A. N. Kasanah, Muladi, and U. Pujianto, “Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 196–201, 2019.
N. M. Putry and B. N. Sari, “KOMPARASI ALGORITMA KNN DAN NAÏVE BAYES UNTUK KLASIFIKASI DIAGNOSIS PENYAKIT DIABETES MELITUS,” Evolusi J. Sains dan Manaj., vol. 10, no. 1, 2022.
N. Ajijah, A. Kurniawan, and Susilawati, “Klasifikasi Teks Mining Terhadap Analisa Isu Kegiatan Tenaga Lapangan Menggunakan Algoritma K-Nearest Neighbor (KNN),” J. Sains Komput. Inform. (J-SAKTI, vol. 7, no. 1, pp. 254–262, 2023.
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