Penerapan Algoritma K-Means Clustering Untuk Pengelompokan Data Pasien Rehabilitasi Narkoba
DOI:
https://doi.org/10.30865/klik.v4i1.1107Keywords:
Clustering; Data Mining; Drugs; Rehabilitation; Patients; Python; Jupyter NotebooksAbstract
Drug abuse’s a serious problem leading to addiction and harmful effects. Rehabilitation aims to save drug addicts and help them lead a normal, physically, and mentally healthy life while improving skills and social relationships. The North Sumatra Province National Narcotics Agency’s responsible for drug prevention, eradication, and rehabilitation. There’re different rehabilitation programs for adolescents and adults, with a "parenting" program applied for adolescents. However, the manual and inefficient process of determining programs poses challenges. This research utilizes data mining with the K-Means clustering algorithm to efficiently categorize drug rehabilitation patient data. The clustering results in three patient clusters based on their characteristics, providing essential information for North Sumatra Province National Narcotics Agency to tailor rehabilitation programs to each group's needs. Through the data clustering process, drug user patterns can be identified based on their shared attributes. Consequently, The North Sumatra Province National Narcotics Agency can determine more effective and suitable programs for each cluster. The findings show that the parenting program is appropriate for Cluster two. The study concludes that using the K-Means clustering algorithm with Python and Jupyter Notebook tools yields optimal clustering results. This research serves as a foundation for application development, further investigations, and comparisons with other clustering algorithms in drug rehabilitation patient data grouping.
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B. L. Hasibuan, Sofiah, and E. Yolanda, “Pengklasifikasian Data Pasien Tes Urine Dengan Metode Clustering Pada Kantor Badan Narkotika Nasional Provinsi Sumut ( BNNP SUMUT ),” JUKI J. Komput. dan Inform., vol. 4, no. 2, pp. 176–187, 2022.
A. Winarta and W. J. Kurniawan, “Optimasi cluster k-means menggunakan metode elbow pada data pengguna narkoba dengan pemrograman python,” J. Tek. Inform. Kaputama, vol. 5, no. 1, pp. 113–119, 2021.
S. Hidayatun and Y. Widowaty, “Konsep Rehabilitasi Bagi Pengguna Narkotika yang Berkeadilan,” J. PENEGAKAN Huk. DAN KEADILAN, vol. 1, no. 2, pp. 166–181, 2020, doi: 10.18196/jphk.1209.
P. U. F. Aulia and S. Saepudin, “PENERAPAN DATA MINING K-MEANS CLUSTERING UNTUK,” SISMATIK(Seminar NasionalSistemInformasidan ManajemenInformatika), pp. 209–2017, 2021.
S. Handoko, Fauziah, and endah tri esti Handayani, “Implementasi Data Mining Untuk Menentukan Tingkat Penjualan Paket Data Telkomsel Menggunakan Metode K-Means Clustering.,” J. Ilm. Teknol. dan Rekayasa, vol. 25, no. 1, pp. 76–88, 2020.
N. Mirantika, A. Tsamratul’ain, and futry diviana Agnia, “Penerapan Algoritma K-Means Clustering Untuk Mengelompokan Penyebaran Covid-19 Di Provinsi Jawa Barat.,” J. NUANSA Inform., vol. 15, no. 2, pp. 92–98, 2021.
B. A. Pangestu, N. A. Kristiawan, and N. Sulistiyowati, “Clustering Obat Untuk Menentukan Pola Pemasaran Efektif di apotek Amar Sehat,” J. Ilm. Wahana Pendidik., vol. 8, no. 16, pp. 115–126, 2022.
A. Rohmah, F. Sembiring, and A. Erfina, “IMPLEMENTASI ALGORITMA K-MEANS CLUSTERING ANALYSIS UNTUK MENENTUKAN HAMBATAN PEMBELAJARAN DARING ( STUDI KASUS?: SMK YASPIM GEGERBITUNG ),” SISMATIK(Seminar Nas. Sist. Inf. dan Manaj. Inform., pp. 290–298, 2021.
A. Sulistiyawati and E. Supriyanto, “Implementasi Algoritma K-means Clustring dalam Penetuan Siswa Kelas Unggulan,” J. TEKNO KOMPAK, vol. 15, no. 2, pp. 25–36, 2020.
A. Asroni, H. Fitri, and E. Prasetyo, “Penerapan Metode Clustering dengan Algoritma K-Means pada Pengelompokkan Data Calon Mahasiswa Baru di Universitas Muhammadiyah Yogyakarta (Studi Kasus: Fakultas Kedokteran dan Ilmu Kesehatan, dan Fakultas Ilmu Sosial dan Ilmu Politik),” Semesta Tek., vol. 21, no. 1, pp. 60–64, 2018, doi: 10.18196/st.211211.
N. W. Utami and A. A. I. I. Paramitha, “PENERAPAN DATA MINING UNTUK MENGETAHUI POLA PEMILIHAN PROGRAM STUDI DI STMIK PRIMAKARA MENGGUNAKAN ALGORITMA K-MEANS,” J. Teknol. Inf. dan Komput., vol. 7, no. 4, pp. 456–463, 2021.
D. A. Manalu and G. Gunadi, “IMPLEMENTASI METODE DATA MINING K-MEANS CLUSTERING TERHADAP DATA PEMBAYARAN TRANSAKSI MENGGUNAKAN BAHASA PEMROGRAMAN PYTHON PADA CV DIGITAL DIMENSI,” INFOTECH J. Technol. Inf., vol. 8, no. 1, pp. 45–54, 2022.
N. A. Pramono, M. H. Nurdiansyah, and D. Z. Hidayatullifa, “Rancang Bangun Pembuatan Sistem Pengiriman Sensor Secara Real Time Menggunakan Python dan Raspberry Pi,” Risal. Fis., vol. 3, no. 2, pp. 43–46, 2019, doi: 10.35895/rf.v3i2.154.
D. F. Sengkey, F. D. Kambey, S. P. Lengkong, S. R. Joshua, H. Valentino, and F. Kainde, “Pemanfaatan Platform Pemrograman Daring dalam Pembelajaran Probabilitas dan Statistika di Masa Pandemi CoVID-19,” J. Inform., vol. 15, no. 4, pp. 217–224, 2020, [Online]. Available: https://ejournal.unsrat.ac.id/index.php/informatika/article/view/31685
S. R. Pratama and A. H. Mirza, “PENERAPAN DATA MINING UNTUK MEMPREDIKSI TINGKAT INFLASI MENGGUNAKAN METODE REGRESI LINIER BERGANDA PADA BPS,” Bina Darma Conf. Comput. Sci., pp. 245–255, 2021.
N. Purba, Poningsih, and H. S. Tambunan, “Penerapan Algoritma K-Means Clustering Pada Penyebaran Penyakit Infeksi Saluran Pernapasan Akut ( ISPA ) di Provinsi Riau,” J. Inf. Syst. Res., vol. 2, no. 3, pp. 220–226, 2021.
R. T. Wahyuni, A. M. H. Pardede, and T. Pasaribu, “DATA MINING PENGELOMPOKAN PECANDU NARKOBA DI MASYARAKAT BERDASARKAN FAKTOR PENYEBAB PENGGUNAANYA MENGGUNAKAN METODE K-MEANS CLUSTERING,” J. Inform. Kaputama, vol. 6, no. 3, pp. 836–844, 2022.
I. D. M. Suyoto, T. Rachmadi, and L. T. Parulian, “Menentukan Cluster Yang Tepat Dengan K-Means Dalam Rangka Mengukur Efektvitas Pelaksanaan Anggaran Pada Kementrerian Agraria Dan Tata Ruang/Badan Pertahanan Nasional,” INFOTECH J. Technol. Inf., vol. 8, no. 1, pp. 13–22, 2022, doi: https://doi.org/10.37365/jti.v8i1.126.
F. Sembiring, Octaviana, and S. Saepudin, “Implementasi Metode K-Means Dalam Pengklasteran Daerah Pungutan Liar di Kabupaten Sukabumi (Studi Kasus?: Dinas Kependudukan Dan Pencatatan Sipil),” J. tekno insentif, vol. 14, no. 1, pp. 40–47, 2020.
F. N. Dhewayani, D. Amelia, Alifah, B. N. Sari, and M. Jajuli, “Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM,” J. Teknol. dan Inf., vol. 12, no. 1, pp. 64–77, Mar. 2022, doi: 10.34010/jati.v12i1.6674.
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