K-MEANS CLUSTERING WITH COMPARISON OF ELBOW AND SILHOUETTE METHODS FOR MEDICINES CLUSTERING BASED ON USER REVIEWS

Safitri Juanita; Raynaldi Dwi Cahyono

doi:10.52436/1.jutif.2024.5.1.1349

Safitri Juanita Department of Information Systems, Universitas Budi Luhur, Indonesia
Raynaldi Dwi Cahyono Department of Information Systems, Universitas Budi Luhur, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.1.1349

Keywords: clustering, elbow, k-means, medicine, silhouette

Abstract

The dissemination of medicine information allows users or customers to make massive assessments of medicines, containing positive and negative reviews—one of a site that provides medicine information online, named drugs.com, contains reviews and ratings of each medicine by variant disease. This site has extensive data collection that has not been processed to produce helpful information for medicine information seekers or the medicine industry. Therefore, research is needed to cluster medicines based on data review from drugs.com. The contribution of this study proposes the best model to cluster user reviews for medicines using K-Means by comparing 2 (two) techniques to determine the optimal number of clustering, Silhouette and Elbow. This study aims to recommend the best K-Means clustering method for processing extensive data reviews and ratings, and cluster results help medical experts, medicine information seekers, or pharmaceutical businesses determine the market share of medicines. The results show that the K-Means model performs best when clustering using the Silhouette method with a DBI value of 0.261 and producing 2 clusters. Meanwhile, the Elbow model has the best performance value of 0.460 and produces 3 clusters. This study also shows that clustering results with both methods produce three medicine cluster groups based on reviews: moderate, unpopular and famous.

Downloads

Download data is not yet available.

References

L. M. Ganiem, “Efek Telemedicine Pada Masyarakat (Kajian Hukum Media McLuhan: Tetrad),” Interak. J. Ilmu Komun., vol. 9, no. 2, pp. 87–97, 2021, doi: 10.14710/interaksi.9.2.87-97.

I. Maskanah, “Fenomena Self-Diagnosis di Era Pandemi COVID-19 dan Dampaknya terhadap Kesehatan Mental,” JoPS J. Psychol. Students, vol. 1, no. 1, pp. 1–10, 2022, doi: 10.15575/jops.v1i1.17467.

M. Ahmed, R. Seraj, and S. M. S. Islam, “The k-means algorithm: A comprehensive survey and performance evaluation,” Electronics, vol. 9, pp. 1–12, 2020, doi: 10.3390/electronics9081295.

Sekar Setyaningtyas, B. Indarmawan Nugroho, and Z. Arif, “Tinjauan Pustaka Sistematis: Penerapan Data Mining Teknik Clustering Algoritma K-Means,” J. Teknoif Tek. Inform. Inst. Teknol. Padang, vol. 10, no. 2, pp. 52–61, 2022, doi: 10.21063/jtif.2022.v10.2.52-61.

M. Guntara and N. Lutfi, “Cacah Klaster pada Klasterisasi dengan Algoritma K-Means Menggunakan Silhouette Coeficient dan Elbow Method,” Juti “Jurnal Teknol. Informasi,” vol. 2, no. 1, pp. 43–52, 2023, doi: 10.26798/juti.v2i1.944.

M. Sholeh and K. Aeni, “Perbandingan Evaluasi Metode Davies Bouldin, Elbow Dan Silhouette Pada Model Clustering Dengan Menggunakan Algoritma K Means,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 8, no. 1, pp. 56–65, 2023.

H. Haviluddin, Y. Sukmono, D. Suprihanto, A. Harjanto, and O. A. Murtioso, “Model Clustering Pemakaian Obat Menggunakan Algoritma K-Means,” CESS (Journal Comput. Eng. Syst. Sci., vol. 7, no. 2, pp. 537–546, 2022, doi: 10.24114/cess.v7i2.36104.

L. Izzah and A. Jananto, “Penerapan Algoritma K-Means Clustering Untuk Perencanaan Kebutuhan Obat Di Klinik Citra Medika,” Progresif J. Ilm. Komput., vol. 18, no. 1, pp. 69–76, 2022, doi: 10.35889/progresif.v18i1.769.

M. R. Nugroho, I. E. Hendrawan, and P. Purwantoro, “Penerapan Algoritma K-Means Untuk Klasterisasi Data Obat Pada Rumah Sakit ASRI,” Nuansa Inform., vol. 16, no. 1, pp. 125–133, 2022, doi: 10.25134/nuansa.v16i1.5294.

S. Paembonan and H. Abduh, “Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat,” PENA Tek. J. Ilm. Ilmu-Ilmu Tek., vol. 6, no. 2, pp. 48–54, 2021, doi: 10.51557/pt_jiit.v6i2.659.

B. A. Pangestu, N. A. Kristiawan, and N. Sulistiyowati, “Clustering Obat untuk Menentukan Pola Pemasaran Efektif di Apotek Amarta Sehat,” J. Ilm. Wahana Pendidik., vol. 8, no. 16, pp. 115–126, 2022, [Online]. Available: https://doi.org/10.5281/zenodo.7058995.

G. Gustientiedina, M. H. Adiya, and Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan pada RSUD Pekanbaru,” J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 17–24, 2019, doi: 10.25077/teknosi.v5i1.2019.17-24.

R. A. Febrianty, W. Witanti, and P. N. Sabrina, “Segmentasi Penjualan Obat Di Apotek Menggunakan Metode K-Means,” in Prosiding SISFOTEK, 2020, pp. 200–206, [Online]. Available: http://www.seminar.iaii.or.id/index.php/SISFOTEK/article/view/213.

M. Cui, “Introduction to the K-Means Clustering Algorithm Based on the Elbow Method,” Accounting, Audit. Financ., vol. 1, pp. 5–8, 2020, doi: 10.23977/accaf.2020.010102.

C. Li, “Preprocessing Methods and Pipelines of Data Mining: An Overview,” in Seminar Data Mining, 2019, pp. 1–7, [Online]. Available: http://arxiv.org/abs/1906.08510.