Pengelompokan Data Kriminal Pada Poldasu Menentukan Pola Daerah Rawan Tindak Kriminal Menggunakan Data Mining Algoritma K-Means Clustering
DOI:
https://doi.org/10.30865/json.v1i2.1955Keywords:
Criminal, Data Mining, K-Means Clustering, RapidminerAbstract
Crime is all forms of actions and actions that are economically and psychologically harmful that violate the applicable laws in the Indonesian state and social and religious norms. Can be interpreted that, crime is anything that violates the law and violates social norms, so that the public opposes it. This study aims to facilitate and assist law enforcement authorities in anticipating criminal acts in vulnerable areas. The method used in this research is the k-means algorithm method using rapidminer 7.3 software. Where the grouping is done to determine the level of vulnerable areas. The establishment of this system is expected to assist the police in determining areas prone to crime. And from the results of the study stated groups of areas prone to criminal acts, namely MEDAN POLRESTA and LABUHAN BATU POLRES.
References
R. I. Ndaumanu and M. R. Arief, Kusrini, “Analisis Prediksi Tingkat Pengunduran Diri Mahasiswa dengan Metode K-Nearest Neighbor,†Jatisi, vol. 1, no. 1, pp. 1–15, 2014.
F. Nasari and S. Darma, “Penerapan K-Means Clustering Pada Data Penerimaan Mahasiswa Baru,†Semin. Nas. Teknol. Inf. dan Multimed. 2015, pp. 73–78, 2015.
Sunjana, “Aplikasi Mining Data Mahasiswa dengan Metode Klasifikasi Decision Tree,†Seminar Nasional Aplikasi Tknologi Informasi. pp. 1–6, 2010.
K. Dumai et al., “IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING UNTUK MENENTUKAN ARKETIPE PEMBELIAN SUKU CADANG DAN ASESORIS KOMPUTER ( STUDI KASUS DI TOKO LAKSAMANA Penelitian ini dilakukan untuk mempelajari Data Mining K-Means Clustering dalam pencarian knowledge ( pengetahu,†vol. 9, no. 1, pp. 26–35, 2017.
A. Jananto, “Penggunaan Market Basket Analysis untuk Menentukan Pola Kompetensi Mahasiswa,†vol. 17, no. 2, pp. 82–89, 2012.
T. Kristanto, R. Arief, D. Jurusan, and T. Informatika, “Analisa Data Mining Metode Fuzzy Untuk Customer Relationship Management Pada Perusahaan Tour &,†vol. d, no. 2009, pp. 2–4, 2013.
K. Tampubolon, H. Saragih, B. Reza, K. Epicentrum, A. Asosiasi, and A. Apriori, “Implementasi Data Mining Algoritma Apriori Pada Sistem Persediaan Alat-Alat Kesehatan,†Inf. dan Teknol. Ilm., pp. 93–106, 2013.
D. Dwinavinta, C. Nugraha, M. Fahmi, Z. Naimah, and N. Setiani, “Klasterisasi Judul Buku dengan Menggunakan Metode K-Means,†Semin. Nas. Apl. Teknol. Inf. Yogyakarta, vol. 21, no. 1, pp. 1907–5022, 2014.
A. Dahlan, E. Utami, and E. T. Luthfi, “Perancangan Data Warehouse Perpustakaan Perguruan Tinggi Xyz Menggunakan Metode Snowflake Schema,†VIII NomorJurnal Teknol. Inf., vol. 24, no. November, pp. 1907–2430, 2013.
J. O. Ong, “Implementasi Algotritma K-means clustering untuk menentukan strategi marketing president university,†J. Ilm. Tek. Ind., vol. vol.12, no, no. juni, pp. 10–20, 2013.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
