SYSTEMATIC LITERATURE REVIEW: PEMETAAN KARAKTERISTIK DENGAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS
DOI:
https://doi.org/10.23969/jp.v10i04.33310Keywords:
data mining, k-means, governmentAbstract
K-Means is a non-hierarchical data clustering method based on data similarity, capable of grouping data into several clusters. In other words, data with similar characteristics are grouped into the same cluster, while data with differing characteristics are placed in separate clusters. The K-Means method can be applied to various types of data, including those in the governmental sector. Although the K-Means algorithm has been widely utilized, its application in government-related activities remains limited, often restricted to selection or recruitment processes. Moreover, the use of attributes in such studies needs to be expanded to achieve more optimal results. This study reviews several articles that implement the K-Means method in research related to public administration. Based on the findings, journals discussing the use of the K-Means algorithm for clustering in government contexts are proven to be relevant and beneficial for future research. It can be concluded that the K-Means method is a validated approach and can be effectively employed for clustering in the public sector. This method also offers advantages across various aspects of governance, benefiting stakeholders, the general public, and other administrative domains.
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References
[1] M. K. M. Nasution, “Memahami Data: Suatu Pengantar,” Sains Data, no. August, 2021, doi: 10.13140/RG.2.2.20754.79048/1.
[2] S. A. Rahmah, “Review Terbaru Tentang Klasterisasi Data Mining Menggunakan Metode K-Means: Tantangan Dan Aplikasi,” Djtechno J. Teknol. Inf., vol. 5, no. 2, pp. 297–303, 2024, doi: 10.46576/djtechno.v5i2.4723.
[3] K. Ananda Mustari, P. Assiroj, B. Hartati, and F. Samuel, “Implementasi Data Mining Pada Instansi Pemerintahan (Systematic Literature Review),” J. Mhs. Tek. Inform., vol. 8, no. 3, pp. 3137–3142, 2024.
[4] A. F. Zabidi, “Penerapan Algoritma K-Means untuk Pengelompokan Koleksi Perpustakaan dengan Data Mining,” vol. 16, no. 2, 2024.
[5] G. Merin Sukarhat and A. Prima Kurniati, “ANALISIS DAN IMPLEMENTASI ALGORITMA K-MEANS++ PADA KLUSTERING Tugas Akhir-2011 Fakultas Teknik Informatika Program Studi S1 Teknik Informatika,” 2011, [Online]. Available: www.tcpdf.org
[6] Fitriah, Imam Riadi, and Herman, “Analisis Data Mining Sistem Inventory Menggunakan Algoritma Apriori,” Decod. J. Pendidik. Teknol. Inf., vol. 3, no. 1, pp. 118–129, 2023, doi: 10.51454/decode.v3i1.132.
[7] Yuda Irawan, “Penerapan Data Mining Untuk Evaluasi Data Penjualan Menggunakan Metode Clustering Dan Algoritma Hirarki Divisive Di Perusahaan Media World Pekanbaru,” J. Teknol. Inf. Univ. Lambung Mangkurat, vol. 4, no. 1, pp. 13–20, 2019, doi: 10.20527/jtiulm.v4i1.34.
[8] F. Nuraeni, D. Kurniadi, and G. Fauzian Dermawan, “Pemetaan Karakteristik Mahasiswa Penerima Kartu Indonesia Pintar Kuliah (KIP-K) menggunakan Algoritma K-Means++,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 11, no. 3, pp. 437–443, 2023, doi: 10.32736/sisfokom.v11i3.1439.
[9] N. Nugroho and F. D. Adhinata, “Penggunaan Metode K-Means dan K-Means++ Sebagai Clustering Data Covid-19 di Pulau Jawa,” Teknika, vol. 11, no. 3, pp. 170–179, 2022, doi: 10.34148/teknika.v11i3.502.
[10] N. Butarbutar, A. P. Windarto, D. Hartama, and S. Solikhun, “Komparasi Kinerja Algoritma Fuzzy C-Means Dan K-Means Dalam Pengelompokan Data Siswa Berdasarkan Prestasi Nilai Akademik Siswa,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 1, no. 1, p. 46, 2017, doi: 10.30645/jurasik.v1i1.8.
[11] R. Helilintar and I. N. Farida, “Penerapan Algoritma K-Means Clustering Untuk Prediksi Prestasi Nilai Akademik Mahasiwa,” J. Sains dan Inform., vol. 4, no. 2, pp. 80–87, 2018, doi: 10.34128/jsi.v4i2.140.
[12] Z. Lubis, B. Andika, S. Saniman, and ..., “Implementasi Data Mining Menganalisa Pemetaan Tingkat Ekonomi Pada Masyarakat Desa Talapeta,” J. SAINTIKOM …, vol. 21, pp. 115–122, 2022, [Online]. Available: http://ojs.trigunadharma.ac.id/index.php/jis/article/view/8739%0Ahttp://ojs.trigunadharma.ac.id/index.php/jis/article/download/8739/2194
[13] J. Hutagalung, “Pemetaan Siswa Kelas Unggulan Menggunakan Algoritma K-Means Clustering,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 1, pp. 606–620, 2022, doi: 10.35957/jatisi.v9i1.1516.
[14] H. K. Saputra, “Analisis Data Mining Untuk Pemetaan Mahasiswa Yang Membutuhkan Bimbingan Dan Konseling Menggunakan Algoritma Naïve Bayes Classifier,” J. Teknol. Inf. dan Pendidik., vol. 11, no. 1, pp. 14–26, 2018, doi: 10.24036/tip.v11i1.104.
[15] L. Iswari and E. G. Ayu, “Pemanfaatan Algoritma K-Means Untuk Pemetaan Hasil Klasterisasi Data Kecelakaan Lalu Lintas,” Teknoin, vol. 21, no. 1, pp. 1–13, 2015, doi: 10.20885/teknoin.vol21.iss1.art7.
[16] R. Watrianthos, R. Handayani, A. F. P. Akhir, A. Ambiyar, and U. Verawardina, “Penerapan Algoritma K-Means Pada Pemetaan Kemampuan Penggunaan Teknologi Informasi Remaja dan Dewasa di Indonesia,” J. Comput. Syst. Informatics, vol. 4, no. 1, pp. 45–50, 2022, doi: 10.47065/josyc.v4i1.2264.
[17] O. Musa and S. Suhartono, “Sistem Informasi Pemetaan Pendidikan Menggunakan Algoritma Data Mining,” J. Sist. Inf. Bisnis, vol. 5, no. 1, pp. 26–32, 2015, doi: 10.21456/vol5iss1pp26-32.
[18] R. N. Fahmi, M. Jajuli, and N. Sulistiyowati, “Analisis Pemetaan Tingkat Kriminalitas di Kabupaten Karawang menggunakan Algoritma K-Means,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 4, no. 1, pp. 67–79, 2021, doi: 10.31539/intecoms.v4i1.2413.
[19] A. Karim et al., “Pemetaan untuk Strategi Dakwah di Kota Semarang Menggunakan Pendekatan Data Mining (Mapping for Da’wah Strategy in Semarang City Using Data Mining Approach),” J. Dakwah Risal., vol. 32, no. 1, p. 40, 2021, doi: 10.24014/jdr.v32i1.12549.
[20] W. Setya and A. Nugraha, “Clustering Pemetaan Tingkat Kemiskinan di Provinsi Jawa Barat Menggunakan Algoritma K-Means,” J. Ilm. Wahana Pendidikan, Januari, vol. 2023, no. 2, pp. 234–244, 2023, [Online]. Available: https://doi.org/10.5281/zenodo.7567622.
[21] P. Marpaung and R. F. Siahaan, “Penerapan Algoritma K-Means Clustering Untuk Pemetaan Kepadatan Penduduk BerdasarkanJumlah Penduduk Kota Medan,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 1, pp. 503–521, 2021.
[22] R. Mbanimara and W. Saputro, “Klasifikasi Pemetaan Penduduk Penerima Bantuan Renovasi Rumah Menggunakan Algoritma K- Means Ronaldi,” J. Pendidik. dan Konseling, vol. 4, no. 5, pp. 637–646, 2022.
[23] S. Syaifuddin, R. Ramlah, I. Hakim, Y. Berliana, and N. Nurhayati, “Pemetaan Produksi Tanaman Tomat di Indonesia Berdasarkan Provinsi Menggunakan Algoritma K-Means Clustering,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 222–228, 2022, doi: 10.47065/josyc.v3i4.2206.
[24] V. Purwayoga, A. A. Mikail, S. D. N. Faridah, and V. R. A’izzah, “Penerapan Data Mining Untuk Pemetaan Daerah Rawan Bencana Sebagai Upaya Kesiapsiagaan Terhadap Bencana,” J. Teknoinfo, vol. 17, no. 1, p. 319, 2023, doi: 10.33365/jti.v17i1.2381.
[25] A. Darmawan, N. Kustian, and W. Rahayu, “Implementasi Data Mining Menggunakan Model SVM untuk Prediksi Kepuasan Pengunjung Taman Tabebuya,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 2, no. 3, p. 299, 2018, doi: 10.30998/string.v2i3.2439.
[26] R. O. Mardiyanto, F. Fitriani, R. J. Purnomo, K. Kusrini, and D. Maulina, “Pemetaan Lokasi Kebakaran Hutan Dan Lahan Di Ntb Dengan Menggunakan Algoritma Naive Bayes,” Tek. Teknol. Inf. dan Multimed., vol. 2, no. 2, pp. 69–75, 2021, doi: 10.46764/teknimedia.v2i2.44.
[27] S. Dewi, S. Defit, and Y. Yuhandri, “Akurasi Pemetaan Kelompok Belajar Siswa Menuju Prestasi Menggunakan Metode K-Means,” J. Sistim Inf. dan Teknol., vol. 3, pp. 28–33, 2021, doi: 10.37034/jsisfotek.v3i1.40.
[28] G. Yao, Y. Wu, X. Huang, Q. Ma, and J. Du, “Clustering of Typical Wind Power Scenarios Based on K-Means Clustering Algorithm and Improved Artificial Bee Colony Algorithm,” IEEE Access, vol. 10, no. September, pp. 98752–98760, 2022, doi: 10.1109/ACCESS.2022.3203695.
[29] R. Monteagudo, E. D. Castronuovo, and R. Barber, “Optimal EVs Charge Station Allocation Considering Residents Dispersion Using a Genetic Algorithm and Weighted K-means,” IEEE Access, no. December, pp. 191071–191085, 2024, doi: 10.1109/ACCESS.2024.3516941.
[30] Z. Yuan, X. Wang, F. Chen, and X. Ma, “Improved K-means Algorithm for Nearby Target Localization,” IEEE Access, vol. 13, no. September 2024, pp. 14872–14880, 2024, doi: 10.1109/ACCESS.2024.3479091.
[31] F. Mahdi Elsiddig Haroun, S. N. M. Deros, and N. M. Din, “Detection and Monitoring of Power Line Corridor From Satellite Imagery Using RetinaNet and K-Mean Clustering,” IEEE Access, vol. 9, no. Vi, pp. 116720–116730, 2021, doi: 10.1109/ACCESS.2021.3106550.
[32] X. Zhou, M. Su, Z. Liu, and D. Zhang, “Smart tour route planning algorithm based on clustering center motive iteration search,” IEEE Access, vol. 7, pp. 185607–185633, 2019, doi: 10.1109/ACCESS.2019.2960761.
[33] S. A. Abbas, A. Aslam, A. U. Rehman, W. A. Abbasi, S. Arif, and S. Z. H. Kazmi, “K-Means and K-Medoids: Cluster Analysis on Birth Data Collected in City Muzaffarabad, Kashmir,” IEEE Access, vol. 8, pp. 151847–151855, 2020, doi: 10.1109/ACCESS.2020.3014021.
[34] J. Ma, Y. A. Muad, and J. Chen, “Visualization of medical volume data based on improved K-means clustering and segmentation rules,” IEEE Access, vol. 9, pp. 100498–100512, 2021, doi: 10.1109/ACCESS.2021.3096790.
[35] M. A. Syakur, B. K. Khotimah, E. M. S. Rochman, and B. D. Satoto, “Integration K-Means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluster,” IOP Conf. Ser. Mater. Sci. Eng., vol. 336, no. 1, 2018, doi: 10.1088/1757-899X/336/1/012017.
[36] Parveen and A. Singh, “Detection of brain tumor in MRI images, using combination of fuzzy c-means and SVM,” 2nd Int. Conf. Signal Process. Integr. Networks, SPIN 2015, pp. 98–102, 2015, doi: 10.1109/SPIN.2015.7095308.
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