COMPARISON OF DATA MINING ALGORITHM FOR CLUSTERING PATIENT DATA HUMAN INFECTIOUS DISEASES

Nadia Nurfadilla; M.  Afdal; Inggih  Permana; Zarnelly Zarnelly

doi:10.52436/1.jutif.2023.4.5.983

Nadia Nurfadilla Information Systems, Faculty of Science and Technology, UIN Sultan Syarif Kasim Riau, Indonesia
M. Afdal Information Systems, Faculty of Science and Technology, UIN Sultan Syarif Kasim Riau, Indonesia
Inggih Permana Information Systems, Faculty of Science and Technology, UIN Sultan Syarif Kasim Riau, Indonesia
Zarnelly Information Systems, Faculty of Science and Technology, UIN Sultan Syarif Kasim Riau, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.5.983

Keywords: Clustering, Davies Bouldin Index, Tuberculosis

Abstract

Tuberculosis is known as an infectious disease whose transmission through air intermediaries is caused by the germ Mycobacterium Tuberculosis. This disease has become a case that has almost spread throughout the pelalawan Regency with the number continuing to increase every year so that it is possible to be able to group the areas where this disease spreads. Grouping of tuberculosis data distribution areas using data mining methods in the form of clustering with the data used coming from the Pelalawan Regency Health Office from 2020 to 2022. The data obtained earlier will then be processed using k-medoids, k-means, and x-means algorithms. The beginning of this research was by processing data from each year using these three algorithms. Determination of the most optimal algorithm using DBI or known as the Davies Bouldin Index. The results of the processing of existing indicators are grouped into three sections, namely areas with a high, medium, and low number of cases. From the results of the study, the optimal algorithm in 2020 data is the k-medoids algorithms with a DBI value of 0,553 and in 2021 data, the most optimal algorithm is the k-means and x-means algorithm with similar DBI values of 0,582. Furthermore, the data in 2022 the most optimal algorithms are the k-means and x-means algorithms because they have the same DBI value, which is 0,510.

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References

V. T. Hulu et al., Epidemiologi Penyakit Menular: Riwayat, Penularan dan Pencegahan. Yayasan Kita Menulis, 2020.

Adt/Ning, “Melalui Kegiatan INA – TIME 2022 Ke-4, Menkes Budi Minta 90% Penderita TBC Dapat Terdeteksi di Tahun 2024,” p2p.kemkes.go.id, 2022. http://p2p.kemkes.go.id/melalui-ina-time-2022-ke-4-menkes-budi-minta-90-penderita-tbc-dapat-terdeteksi-di-tahun-2024/.

WHO/Indonesia, “Hari Tuberkulosis Sedunia 2022,” 2022. https://www.who.int/indonesia/news/campaign/tb-day-2022.

D. N. Batubara, A. P. Windarto, D. Hartama, and H. Satria, “Analisis Metode K-MEANS Pada Pengelompokan Keberadaan Area Resapan Air Menurut Provinsi,” Semin. Nas. Sains Teknol. Inf., no. x, pp. 345–349, 2019, [Online]. Available: https://prosiding.seminar-id.com/index.php/sensasi/article/view/324 (05 Juni 2020).

W. A. Wahyuni and S. Saepudin, “Penerapan Data Mining Clustering Untuk Mengelompokkan Berbagai Jenis Merk Mesin Cuci,” Semin. Nas. Sist. …, pp. 306–313, 2021, [Online]. Available: https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/35%0Ahttps://sismatik.nusaputra.ac.id/index.php/sismatik/article/download/35/31.

F. Harahap, “Perbandingan Algoritma K Means dan K Medoids Untuk Clustering Kelas Siswa Tunagrahita,” TIN Terap. Inform. Nusant., vol. 2, no. 4, pp. 191–197, 2021.

S. Ramadhani, D. Azzahra, U. I. Negeri, and S. S. Kasim, “Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student ’s Thesis,” vol. x, no. x, pp. 24–33, 2022.

B. Bustami, R. Mahara, H. Ahmadian, S. Wahyuni, and K. AR, “Analisis Clustering Penduduk Miskin Di Provinsi Aceh Menggunakan Algoritma K-Means Dan X-Means,” J. Nas. Komputasi dan Teknol. Inf., vol. 5, no. 1, pp. 26–35, 2022, doi: 10.32672/jnkti.v5i1.3961.

I. N. Sari et al., Metode penelitian kualitatif. UNISMA PRESS, 2022.

A. Szent-Gyorgyi, “IDENTIFIKASI PENELITIAN DAN PERUMUSAN MASALAH PENELITIAN,” Pengantar Metodol. Penelit. Pendekatan Multidisipliner, p. 19, 2022.

F. Hasyim and M. Muafi, “Implementasi Data Mining Dalam Menentukan Strategi Promosi Program KB Menggunakan Algoritma K-Means Clustering,” COREAI J. Kecerdasan Buatan …, vol. 3, no. 1, pp. 110–119, 2022, [Online]. Available: https://ejournal.unuja.ac.id/index.php/core/article/view/4292.

E. Muningsih, I. Maryani, and V. R. Handayani, “Penerapan Metode K-Means dan Optimasi Jumlah Cluster dengan Index Davies Bouldin untuk Clustering Propinsi Berdasarkan Potensi Desa,” J. Sains dan Manaj., vol. 9, no. 1, pp. 95–100, 2021, [Online]. Available: www.bps.go.id.

A. Rohmah, F. Sembiring, and ..., “Implementasi Algoritma K-Means Clustering Analysis Untuk Menentukan Hambatan Pembelajaran Daring (Studi Kasus: Smk Yaspim …,” … Sist. Inf. dan …, pp. 290–298, 2021, [Online]. Available: https://sismatik.nusaputra.ac.id/index.php/sismatik/article/view/32.

H. D. Tampubolon, S. Suhada, M. Safii, S. Solikhun, and D. Suhendro, “Penerapan Algoritma K-Means dan K-Medoids Clustering untuk Mengelompokkan Tindak Kriminalitas Berdasarkan Provinsi,” J. Ilmu Komput. dan Teknol., vol. 2, no. 2, pp. 6–12, 2021, doi: 10.35960/ikomti.v2i2.703.

A. A. Riza and D. R. S. Saputro, “Clustering Data Numerik Menggunakan Algoritme X-Means,” UNEJ e-Proceeding, 2022, [Online]. Available: https://jurnal.unej.ac.id/index.php/prosiding/article/view/33491%0Ahttps://jurnal.unej.ac.id/index.php/prosiding/article/download/33491/11655.

A. F. Solikin, Kusrini, and F. W. Wibowo, “Evaluasi Cluster Data Interkomparasi Anak Timbangan Dengan Algoritma Self Organizing Maps Cluster Evaluation Weighing Intercomparison Data with Self Organizing Maps Algorithm,” vol. 11, no. 2, pp. 208–219, 2021.

D. A. Saidah, R. Santoso, and T. Widiharih, “Pengelompokan Provinsi Di Indonesia Berdasarkan Indikator Kesehatan Lingkungan Menggunakan Metode Partitioning Around Medoids Dengan Validasi Indeks Internal,” J. Gaussian, vol. 11, no. 2, pp. 302–312, 2022, doi: 10.14710/j.gauss.v11i2.35478.

T. A. Munandar, “Penerapan Algoritma Clustering Untuk Pengelompokan Tingkat Kemiskinan Provinsi Banten,” JSiI (Jurnal Sist. Informasi), vol. 9, no. 2, pp. 109–114, 2022, doi: 10.30656/jsii.v9i2.5099.

G. T. Enggiel, H. D. Purnomo, and D. K. Salatiga, “APPLICATION OF K-MEANS METHOD IN THE SPREAD OF POSITIVE CASES OF COVID-19 IN SALATIGA CITY Penerapan Metode K-Means Pada Penyebaran Kasus Positif Covid-19,” vol. 3, no. 5, pp. 1323–1328, 2022.

J. Ilmiah and W. Pendidikan, “Penerapan K-Means Clustering dalam Pengelompokan Kasus Tuberkulosis di Provinsi Jawa Barat,” J. Ilm. Wahana Pendidik., vol. 8, no. September, pp. 219–227, 2022.

A. J. Wahidin and D. I. Sensuse, “Perbandingan Algoritma K-Means, X-Means Dan K-Medoids Untuk Klasterisasi Awak Kabin Lion Air,” J. ICT Inf. Commun. Technol., vol. 20, no. 2, pp. 298–302, 2021, doi: 10.36054/jict-ikmi.v20i2.387.