Klasterisasi Data Penerima Bantuan Langsung Tunai Menggunakan Algoritma K-Means
DOI:
https://doi.org/10.30865/jurikom.v10i2.5807Keywords:
Clusterization, K-Means, Davies Bouldin, Social Assistance, BLTDAbstract
Increasing population and unequal distribution of population even with conditions of varying poverty levels need to be the center of attention and proper handling. In Pelangsian Village, there were 202 residents who received BLTD in 2021. The existence of a quota of beneficiaries and the number of recipients' conditions that were not suitable often became an obstacle in determining beneficiaries. So that from the data obtained in this study it is necessary to do clustering. Clustering results can be used to find out if the population receiving BLTD meets predetermined criteria. so that it can further assist the government in seeing the categories of people who are really entitled to get this assistance. Data clustering can be done using algorithms in data mining. The algorithm used in the data clustering of Pelangsian villagers in this study is the K-Means algorithm. The research methodology was carried out in several stages, such as problem selection, data collection, data preprocessing, data mining algorithm selection, results evaluation, and results interpretation. Clustering is done by forming 2 data clusters. Before the data is clustered, 202 records need to be preprocessed so that it is found that there are 196 valid data records that can be processed according to research needs. The results of data processing are done by clustering the data into 2 groups. Clustering uses the K-Means algorithm by determining the value of K = 2 so that it is obtained that cluster0 has 115 residents and cluster1 has 81 residents. Algorithm performance testing shows that the K-Means Algorithm obtains a Devies-Bouldin value of -0.794. With a Davies-Bouldin-0.794 value, it can be said that the performance of the clustering algorithm is quite good.
References
İ. M. Eligüzel and E. Özceylan, “A Comparative Study of Classification Methods on the States of the USA Based on COVID-19 Indicators,†in IFIP Advances in Information and Communication Technology, 2021, vol. 632 IFIP. doi: 10.1007/978-3-030-85906-0_63.
A. L. Fairuz, R. D. Ramadhani, and N. A. F. Tanjung, “Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter,†Journal of Dinda : Data Science, Information Technology, and Data Analytics, vol. 1, no. 1, 2021, doi: 10.20895/dinda.v1i1.180.
E. Esyudha Pratama, H. Sastypratiwi, and Yulianti, “Analisis Kecenderungan Informasi Terkait Covid-10 Berdasarkan Big Data Sosial Media dengan Menggunakan Metode Data Mining,†JIP (Jurnal Informatika Polinema), vol. 7, no. 2, pp. 1–6, Feb. 2021.
Y. F. S. Y. Damanik, S. Sumarno, I. Gunawan, D. Hartama, and I. O. Kirana, “Penerapan Data Mining Untuk Pengelompokan Penyebaran Covid-19 Di Sumatera Utara Menggunakan Algoritma K-Means,†Jurnal Ilmu Komputer dan Informatika, vol. 1, no. 2, 2021, doi: 10.54082/jiki.13.
B. Santoso, “Analisa Pengaruh Pandemi Covid-19 terhadap Kinerja Keuangan Sektoral Perusahaan Emiten di Bursa Efek Indonesia,†Journal of Management and Business Review, vol. 18, no. 2, 2021, doi: 10.34149/jmbr.v18i2.268.
T. M. Permata Aulia, N. Arifin, and R. Mayasari, “PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19,†SINTECH (Science and Information Technology) Journal, vol. 4, no. 2, 2021, doi: 10.31598/sintechjournal.v4i2.762.
W. Wiyanto and Z. Setyaningsih, “Sentiment Analysis Pemutusan Hubungan Kerja Akibat Pandemi Covid-19 Menggunakan Algoritma NaïveBayes Dan PSO,†Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 10, no. 3, 2021, doi: 10.32736/sisfokom.v10i3.1299.
N. Nurahman and D. Dwi Aulia, “Algoritma K-Means Untuk Melihat Penularan Tertinggi Virus Covid-19 Diseluruh Provinsi Indonesia,†2021. doi: https://doi.org/10.36050/betrik.v12i2.331.
N. Nurahman and S.- Aminah, “Klasifikasi Penerima Bantuan Sosial Di Desa Batuah Menggunakan Metode Algoritma C4.5,†Jurnal Teknik Informasi dan Komputer (Tekinkom), vol. 5, no. 2, p. 271, Dec. 2022, doi: 10.37600/tekinkom.v5i2.516.
A. Imam, B. S. Rintyarna, and D. Arifianto, “Pengelompokan Golongan Ekonomi Masyarakat di Desa Kartonegoro Kecamatan Jenggawah dengan menggunakan Algoritma K-Means,†Sekripsi Universitas Muhammadiyah Jember Fakultas Teknik, 2016.
Y. Kusnadi and M. S. Putri, “Clustering Menggunakan Metode K-Means Untuk Menentukan Prioritas Penerima Bantuan Bedah Rumah (Studi Kasus : Desa Ciomas Bogor),†Jurnal Teknologi Informatika dan Komputer, vol. 7, no. 1, 2021, doi: 10.37012/jtik.v7i1.498.
A. Ikhwan and N. Aslami, “Implementasi Data Mining untuk Manajemen Bantuan Sosial Menggunakan Algoritma K-Means,†Jurnal Teknologi Informasi, vol. 4, no. 2, 2020, doi: 10.36294/jurti.v4i2.2103.
M. Rizki, D. Devrika, I. H. Umam, and F. S. Lubis, “Aplikasi Data Mining dalam Penentuan Layout Swalayan dengan Menggunakan Metode MBA,†Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri, vol. 5, no. 2, 2020, doi: 10.24014/jti.v5i2.8958.
F. Elfaladonna and A. Rahmadani, “ANALISA METODE CLASSIFICATION-DECISSION TREE DAN ALGORITMA C.45 UNTUK MEMPREDIKSI PENYAKIT DIABETES DENGAN MENGGUNAKAN APLIKASI RAPID MINER,†SINTECH (Science and Information Technology) Journal, vol. 2, no. 1, 2019, doi: 10.31598/sintechjournal.v2i1.293.
D. Galih Pradana, M. L. Alghifari, M. Farhan Juna, and S. Dwisiwi Palaguna, “Klasifikasi Penyakit Jantung Menggunakan Metode Artificial Neural Network,†Indonesian Journal of Data and Science (IJODAS), vol. 3, no. 2, pp. 55–60, 2022.
N. Dwitri, J. A. Tampubolon, S. Prayoga, F. I. R.H Zer, and D. Hartama, “PENERAPAN ALGORITMA K-MEANS DALAM MENENTUKAN TINGKAT PENYEBARAN PANDEMI COVID-19 DI INDONESIA,†Jurnal Teknologi Informasi, vol. 4, no. 1, 2020, doi: 10.36294/jurti.v4i1.1266.
M. Mursalim, P. Purwanto, and M. A. Soeleman, “Penentuan Centroid Awal Pada Algoritma K-Means Dengan Dynamic Artificial Chromosomes Genetic Algorithm Untuk Tuberculosis Dataset,†Techno.Com, vol. 20, no. 1, 2021, doi: 10.33633/tc.v20i1.4230.
N. Nurahman, A. Purwanto, and S. Mulyanto, “Klasterisasi Sekolah Menggunakan Algoritma K-Means berdasarkan Fasilitas, Pendidik, dan Tenaga Pendidik,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 21, no. 2, pp. 337–350, Mar. 2022, doi: 10.30812/matrik.v21i2.1411.
M. R. Muttaqin and M. Defriani, “Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa,†ILKOM Jurnal Ilmiah, vol. 12, no. 2, 2020, doi: 10.33096/ilkom.v12i2.542.121-129.
O. Mar, atun Sholihah, N. Suarna, and G. Dwilestari, “Implementasi Metode K-means Clustering Untuk Menganalisa Penerima Bantuan di Desa Palasah,†Jurnal Informatika dan Teknologi Informasi, vol. 1, no. 2, 2023, doi: 10.56854/jt.v1i2.121.
Sri Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4.5, Naive Bayes, KNN, Dan SVM,†Jurnal Tekno Insentif, vol. 13, no. 1, 2019.
Nurahman, M. M. Alfitri, and E. Mashamy, “Klasifikasi Data Penduduk Untuk Menerima Bantuan Pangan Non Tunai Menggunakan Algoritma Naïve Bayes,†JURIKOM (Jurnal Riset Komputer), vol. 9, no. 4, pp. 1035–1043, 2022, doi: 10.30865/jurikom.v9i4.4678.
N. Nurahman and D. Tjahjo Seabtian, “Classification of Poverty Reduction Program Recipients with Neural Network Algorithm in East Kotawaringin Communities,†E-Komtek, vol. 5, no. 2, pp. 190–202, 2021, doi: 10.37339/e-komtek.v5i2.751.
r gupitha, “Penentuan Strategi Marketing Sekolah Menengah Kejuruan Terpadu Lampang Subang Menggunakan Metode K-Means Clustering,†Global, 2018.
