Data Clustering Mining Applying the K-Means Algorithm, Cervical Cancer Behavior Risk

Authors

  • Ridha Maya Faza Lubis Southern Taiwan University of Science and Technology, Tainan
  • Jen-Peng Huang Southern Taiwan University of Science and Technology, Tainan
  • Pai-Chou Wang Southern Taiwan University of Science and Technology, Tainan
  • Kiki Khoifin Southern Taiwan University of Science and Technology, Tainan
  • Mula Sigiro University of HKBP Nommensen, Medan
  • Joel Panjaitan Academy of Deli Serdang Engineering, Medan

DOI:

https://doi.org/10.30865/mib.v7i2.6088

Keywords:

Data Mining, Clustering, K-Means, Cervical Cancer

Abstract

Nowadays, cancer is often heard as a topic of conversation for both men and women in Indonesia and even in the world, in addition to the symptoms that are not too significant and also the lack of public awareness to carry out periodic health checks, which has a negative impact on health. This lack of care is also caused by several factors, namely the lack of the community's economy, too busy with work (other matters) and even some people are not ready to know and accept the disease they are suffering from. Based on all the factors causing the reluctance of medical examinations, of course, it requires us to carry out examinations so that we can prevent and treat them early if they are diagnosed with certain diseases. There are several cancers with predominant sufferers and even only suffered by women, one of which is cervical cancer. In 2020 it is estimated that cases of cervical cancer will increase by 3.4% from 6.6% in 2018 to 9% and even cervical cancer will also become the third deadly disease in women after breast cancer and lung cancer. From this it can be seen that the percentage of deaths caused by cervical cancer is always increasing. Therefore, to reduce the high mortality rate, a clustering technique was carried out to group the data into their respective clusters based on the similarity of characteristics between one data and another. The algorithm used is K-Means with the rapid miner tester application. The final result obtained is that cluster 1 has more data and it is stated that out of 72 data on Cervical Cancer only 28 are declared as sufferers of Cervical Cancer and 44 other data are not.

References

N. A. Wantini, N. Indrayani, “Deteksi dini kanker serviks dengan inspeksi visual asam asetat (IVA)â€, Jurnal Ners dan Kebidanan (Journal of Ners and Midwifery), vol. 6, no. 1, pp. 27–34, 2019.

M. Marsono, D. Saripurna, M. Zunaidi, “Analisis Data Mining Pada Strategi Penjualan Produk PT Aquasolve Sanaria Dengan Menggunakan Metode K-Means Clusteringâ€, J-SISKO TECH (Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD), vol. 4, no. 1, pp. 127, 2021, doi:10.53513/jsk.v4i1.60.

B. D. Mudzakkir, “Pengelompokan Data Penjualan Produk Pada Pt Advanta Seeds Indonesia Menggunakan Metode K-Meansâ€, Jurnal Mahasiswa Teknik Informatika, vol. 2, no. 2, pp. 34–40, 2018.

R. Muliono, Z. Sembiring, “Data Mining Clustering Menggunakan Algoritma K-Means Untuk Klasterisasi Tingkat Tridarma Pengajaran Dosenâ€, CESS (Journal of Computer Engineering, System and Science), vol. 4, no. 2, pp. 272–279, 2019.

H. Priyatman, F. Sajid, D. Haldivany, “Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswaâ€, Jurnal Edukasi Dan Penelitian Informatika (JEPIN), vol. 5, no. 1, pp. 62, 2019.

S. S. Arifin, A. M. Siregar, T. Al Mudzakir, “Klasifikasi Penyakit Kanker Serviks Menggunakan Algoritma Support Vector Machine (SVM)â€, Conference on Innovation and Application of Science and Technology (CIASTECH), pp. 521–528, 2021.

E. S. Salim et al., “Analisa Metode Random Forest Tree dan K-Nearest Neighbor dalam Mendeteksi Kanker Serviksâ€, Jurnal Ilmu Komputer Dan Sistem Informasi (JIKOMSI), vol. 3, no. 2, pp. 97–101, 2020.

M. Jamaris, “Implementasi Metode Rough Set Untuk Menentukan Kelayakan Bantuan Dana Hibah Fasilitas Rumah Ibadahâ€, INOVTEK Polbeng - Seri Informatika, vol. 2, no. 2, pp. 161, 2017, doi:10.35314/isi.v2i2.203.

S. Al Syahdan, A. Sindar, “Data Mining Penjualan Produk Dengan Metode Apriori Pada Indomaret Galang Kotaâ€, Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 1, no. 2, 2018, doi:10.32672/jnkti.v1i2.771.

H. Juliansa, S. Defit, S. Sumijan, “Identifikaasi Tingkat Kerusakan Peralatan Laboratorium Komputer Menggunakan Metode Rough Setâ€, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 2, no. 1, pp. 410–415, 2018, doi:10.29207/resti.v2i1.274.

E. Buulolo, Data Mining Untuk Perguruan Tinggi, Deepublish, 2020.

Dewi Eka Putri, Eka Praja Wiyata Mandala, “Hybrid Data Mining berdasarkan Klasterisasi Produk untuk Klasifikasi Penjualanâ€, Jurnal KomtekInfo, vol. 9, pp. 68–73, 2022, doi:10.35134/komtekinfo.v9i2.279.

S. M. Dewi, A. P. Windarto, D. Hartama, “Penerapan Datamining Dengan Metode Klasifikasi Untuk Strategi Penjualan Produk Di Ud.Selamat Selularâ€, KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 3, no. 1, pp. 617–621, 2019, doi:10.30865/komik.v3i1.1669.

S. Sindi et al., “Analisis algoritma k-medoids clustering dalam pengelompokan penyebaran covid-19 di indonesiaâ€, (JurTI) Jurnal Teknologi Informasi, vol. 4, no. 1, pp. 166–173, 2020.

D. A. I. C. Dewi, D. A. K. Pramita, “Analisis Perbandingan Metode Elbow dan Silhouette pada Algoritma Clustering K-Medoids dalam Pengelompokan Produksi Kerajinan Baliâ€, Matrix : Jurnal Manajemen Teknologi dan Informatika, vol. 9, no. 3, pp. 102–109, 2019, doi:10.31940/matrix.v9i3.1662.

A. N. Fadhilah, A. Jananto, “KLASTERISASI LITERATUR MAHASISWA MENGGUNAKAN METODE AHC DI DINAS KEARSIPAN DAN PERPUSTAKAAN PROVINSI JAWA TENGAHâ€2021.

Y. Syahra, “Penerapan Data Mining Dalam Pengelompokkan Data Nilai Siswa Untuk Penentuan Jurusan Siswa Pada SMA Tamora Menggunakan Algoritma K-Means Clusteringâ€, Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer), vol. 17, no. 2, pp. 228, 2018, doi:10.53513/jis.v17i2.70.

. F., F. T. Kesuma, S. P. Tamba, “Penerapan Data Mining Untuk Menentukan Penjualan Sparepart Toyota Dengan Metode K-Means Clusteringâ€, Jurnal Sistem Informasi dan Ilmu Komputer Prima(JUSIKOM PRIMA), vol. 2, no. 2, pp. 67–72, 2020, doi:10.34012/jusikom.v2i2.376.

S. A. Rahmah, “KLASTERISASI POLA PENJUALAN PESTISIDA MENGGUNAKAN METODE K-MEANS CLUSTERING ( STUDI KASUS DI TOKO JUANDA TANI KECAMATAN HUTABAYU RAJA )â€vol. 1, no. 1, pp. 1–5, 2020.

I. Nasution, A. P. Windarto, M. Fauzan, “Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsiâ€, Building of Informatics, Technology and Science (BITS), vol. 2, no. 2, pp. 76–83, 2020.

A. Nursia, W. Ramdhan, W. M. Kifti, “Analisis Kelayakan Penerima Bantuan Covid-19 Menggunakan Metode K – Meansâ€, Building of Informatics, Technology and Science (BITS), vol. 3, no. 4, pp. 574–583, 2022, doi:10.47065/bits.v3i4.1399.

Z. K. A. B. Kurnia Drajat Wibowo, “Movie Recommendation System Using Knowledge-Based Filtering and K-Means Clusteringâ€, Building of Informatics, Technology and Science (BITS), vol. 3, no. 4, pp. 460–465, 2022, doi:10.47065/bits.v3i4.1236.

Downloads

Published

2023-04-27

Issue

Vol. 7 No. 2 (2023): April 2023

Section

Articles

License

Creative Commons 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:

  1. 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.
  2. 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.
  3. 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).