Application of K-means Clustering Data Mining in Grouping Data of People with Disabilities
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Abstract
Data mining is critical in enabling organizations to derive reliable insights from data. Social welfare remains a significant challenge in Indonesia, particularly for people with disabilities, emphasizing the need for targeted strategies. However, developing research has not used natural characteristics according to disability problems. This study utilizes the K-Means Clustering algorithm to analyze and categorize the population of people with disabilities in East Java. The attributes include the type of disability, population size, and regional distribution. We employs a dataset from the East Java Central Bureau of Statistics, comprising 342 data points across eight attributes, including region, disability type, and year. The analysis involves data preprocessing, transformation, clustering, and evaluation using the Davies-Bouldin Index (DBI). The results identify two optimal clusters, achieving the lowest DBI score of 0.097, indicating high cluster quality. Cluster 0 represents regions with fewer people with disabilities, while Cluster 1 highlights areas with higher populations. These findings provide a foundation for developing more focused and inclusive welfare programs tailored to regional needs, enhancing the quality of life for people with disabilities.
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References
L. N. Indahsari, I. Yunita, and Z. Fatah, “Information System Design Using Cutomer Relationship Management (CRM) Method at Paglak Petung Cafe and Art in Banyuwangi District,” Jurnal Teknik Informatika (Jutif), vol. 5, no. 4, pp. 617–622, 2024, doi: https://doi.org/10.52436/1.jutif.2024.5.4.2263. DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2263
A. Rahman, “Persepsi Masyarakat Terhadap Penyandang Disabilitas Di Kelurahan Bongki Kecamatan Sinjai Utara Kabupaten Sinjai,” vol. 13, pp. 93–99, 2021.
I. A. Darmawan, M. F. Randy, I. Yunianto, M. M. Mutoffar, and M. T. P. Salis, “Penerapan Data Mining Menggunakan Algoritma Apriori Untuk Menentukan Pola Golongan Penyandang Masalah Kesejahteraan Sosial,” Sebatik, vol. 26, no. 1, pp. 223–230, 2022, doi: 10.46984/sebatik.v26i1.1622. DOI: https://doi.org/10.46984/sebatik.v26i1.1622
A. I. Sari, H. S. Tambunan, W. Saputra, I. S. Damanik, and I. S. Saragih, “Implementasi Algoritma K-Means Dalam Pengelompokan Penyandang Disabilitas Menurut Kecamatan Kabupaten Simalungun,” in Prosiding Seminar Nasional Riset Dan Information Science (SENARIS), 2020, vol. 2, pp. 54–61.
H. Hidayaturrahman, “Pelaksanaan Hak Penyandang Disabilitas Di Kota Pekanbaru Berdasarkan Undang–Undang Nomor 8 Tahun 2016,” Jurnal Gagasan Hukum, vol. 4, no. 01, pp. 14–28, 2022, doi: 10.31849/jgh.v4i01.10439. DOI: https://doi.org/10.31849/jgh.v4i01.10439
P. Lestari, N. Suarna, and W. Prihartono, “Implementasi Data Mining Clustering Dalam Mengelompokkan Penduduk Penyandang Disabilitas Menggunakan Algoritma K-Means,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 958–966, 2024, doi: 10.36040/jati.v8i1.8445. DOI: https://doi.org/10.36040/jati.v8i1.8445
S. Hanafi, Y. Djabbar, M. Fahri, S. P. Jasmin, and M. Zulhidayat, “Tantangan dalam Implementasi Kebijakan Perlindungan Hak Asasi Manusia bagi Penyandang Disabilitas di Provinsi DKI Jakarta,” Jurnal Hukum dan HAM Wara Sains, vol. 2, no. 6, pp. 509–516, 2023, doi: 10.58812/jhhws.v2i6.446. DOI: https://doi.org/10.58812/jhhws.v2i6.446
A. Rahmawati, R. Roekhudin, and A. Prastiwi, “Effect of good corporate governance and corporate social responsibility on firm value moderate by profitability,” International Journal of Research in Business and Social Science, vol. 10, no. 4, pp. 59–66, 2021, doi: 10.20525/ijrbs.v10i4.1194. DOI: https://doi.org/10.20525/ijrbs.v10i4.1194
S. Arrohman and Z. Fatah, “Prediksi Diabetes Menggunakan Algoritma Klasifikasi K-Nearest Neighbors (K-NN) pada Perempuan Indian Pima,” Gudang Jurnal Multidisiplin Ilmu, vol. 2, no. 10, pp. 220–226, 2024, doi: https://doi.org/10.59435/gjmi.v2i10.986.
A. Sujjada, G. P. Insany, and S. Noer, “Analisis Clustering Data Penyandang Disabilitas Menggunakan Metode Agglomerative Hierarchical Clustering dan K-means,” Jurnal Teknologi dan Manajemen Informatika, vol. 10, no. 1, pp. 1–12, 2024, doi: 10.26905/jtmi.v10i1.10654. DOI: https://doi.org/10.26905/jtmi.v10i1.10654
P. W. Rahayu et al., Buku Ajar Data Mining. PT. Sonpedia Publishing Indonesia, 2024.
S. Trihandaru, Clustering Untuk Data Suara Studi Kasus Mel-Frequency Cepstral Coefficients (MFCC) dan Long Short Term Memory (LSTM) dengan Internet of Things (IoT) untuk Klasifikasi Suara. Uwais Inspirasi Indonesia, 2024.
R. M. Sari, A. Rizka, N. A. Putri, and A. Efriana, Perhitungan Metode Clustering. Payakumbuh: Serasi Media Teknologi, 2024.
I. A. Firdaus et al., Strategi Pengembangan Kota Ramah Disabilitas. Surabaya: Cipta Media Nusantara, 2022.
P. A. Gatto and R. M. Awangga, Pengelompokan Kedisiplinan Pegawai Berdasarkan Absensi Menggunakan Algoritma K-Means. Penerbit Buku Pedia, 2023.
Nurhayati Nurhayati, Pemodelan K- Means Algoritma Dan Big Data Analysis (Pemetaan Data Mustahiq), Cetakan I. Pascal Books, 2022.
Kuntoro, Data Science dalam Perspektif Statistika. Airlangga University Press, 2024, 2024.
I. F. Fauzi, M. G. Resmi, and T. I. Hermanto, “Penentuan Jumlah Cluster Optimal Menggunakan Davies Bouldin Index pada Algoritma K-Means untuk Menentukan Kelompok Penyakit,” Jurnal Masyarakat Informatika Unjani, vol. 7, no. 2, pp. 1–15, 2023.