Cluster Analysis of Environmental Pollution in Indonesia Using Complete Linkage Method with Elbow Optimization

Authors

  • Adelia Damayanti Department of Mathematic, Islamic State of Sunan Ampel Surabaya University
  • Wika Dianita Utami Department of Mathematic, Islamic State of Sunan Ampel Surabaya University
  • Dian Candra Rini Novitasari Department of Mathematic, Islamic State of Sunan Ampel Surabaya University
  • Putroue Keumala Intan Department of Mathematic, Islamic State of Sunan Ampel Surabaya University
  • Mohammad Lail Kurniawan Statistics of Pasuruan City

DOI:

https://doi.org/10.31764/jtam.v7i2.12961

Keywords:

Cluster Analysis, Complete Linkage, Elbow Method, Environmental Pollution, Silhouette Coefficient.

Abstract

The issue of environmental contamination remains unsolved. The problem continues to have a substantial detrimental impact. This research aimed to identify provinces in Indonesia with high or low levels of environmental pollution so that the government may offer treatment to provinces with high levels of pollution and seek a significant reduction in the incidence of environmental pollution in Indonesia. Clustering is required to identify provinces with high and low pollution levels using the complete linkage method because this method can provide tight clusters and is less impacted by outliers. The analysis of the complete linkage method with Elbow optimization revealed two optimal clusters, namely high and low clusters. The high cluster consists of three provinces: Central Java, West Java, and East Java. The low cluster consists of 31 provinces. This research used a Silhouette Coefficient validity test. The value of the Silhouette Coefficient is 0.75. The value indicates that the data object is in the correct cluster and that the cluster structure is relatively strong.

References

Ais, C., Hamid, A., Candra, D., & Novitasari, R. (2022). Analysis of Livestock Meat Production in Indonesia Using Fuzzy C-Means Clustering. Jurnal Ilmu Komputer Dan Informasi (Journal of Computer Science and Information), 15(1), 1–8.

Cao, R., Li, B., Wang, Z., Peng, Z. R., Tao, S., & Lou, S. (2020). Using a Distributed Air Sensor Network to Investigate the Spatiotemporal Patterns of PM2.5 Concentrations. Environmental Pollution, 264, 114549. https://doi.org/10.1016/j.envpol.2020.114549

Chen, M., Wang, P., Chen, Q., Wu, J., & Chen, X. (2015). A Clustering Algorithm for Sample Data Based on Environmental Pollution Characteristics. Atmospheric Environment, 107, 194–203. https://doi.org/10.1016/j.atmosenv.2015.02.042

Cui, M. (2020). Introduction to the K-Means Clustering Algorithm Based on the Elbow Method. 5–8. https://doi.org/10.23977/accaf.2020.010102

Dinata, E., & Syaputra, H. (2020). Penerapan Metode Agglomerativ Hirarchical Clusturing Untuk Klasifikasi Dokumen Skripsi. Bina Darma Conference on Computer Science (BDCCS), 2(2), 412–422.

Drews, S., Savin, I., & van den Bergh, J. C. J. M. (2019). Opinion Clusters in Academic and Public Debates on Growth-vs-Environment. Ecological Economics, 157(October 2018), 141–155. https://doi.org/10.1016/j.ecolecon.2018.11.012

Govender, P., & Sivakumar, V. (2020). Application of K-Means and Hierarchical Clustering Techniques for Analysis of Air Pollution: A Review (1980–2019). In Atmospheric Pollution Research (Vol. 11, Issue 1). Turkish National Committee for Air Pollution Research and Control. https://doi.org/10.1016/j.apr.2019.09.009

Hendra Perdana, Nur Asiska, N. S. (2019). Pencarian Cluster Optimum Pada Single Linkage, Complete Linkage dan Average Linkage. Bimaster : Buletin Ilmiah Matematika, Statistika Dan Terapannya, 8(3), 393–398.

Hidayat, A. (n.d.). Penjelasan Lengkap tentang Analisis Klaster. Statistikian.

Hidayati, R., Zubair, A., Hidayat Pratama, A., & Indana, L. (2021). Silhouette Coefficient Analysis in 6 Measuring Distances of K-Means Clustering. Techno.Com, 20(2), 186–197.

Liu, F., & Deng, Y. (2021). Determine the Number of Unknown Targets in Open World Based on Elbow Method. IEEE Transactions on Fuzzy Systems, 29(5), 986–995. https://doi.org/10.1109/TFUZZ.2020.2966182

Mu’afa, S. F., & Ulinnuha, N. (2019). Perbandingan Metode Single Linkage, Complete Linkage Dan Average Linkage dalam Pengelompokan Kecamatan Berdasarkan Variabel Jenis Ternak Kabupaten Sidoarjo. Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 4(2).

Muningsih, E., & Kiswati, S. (2018). Sistem Aplikasi Berbasis Optimasi Metode Elbow Untuk Penentuan Clustering Pelanggan. Joutica, 3(1), 117.

Nabiilah Ardini Fauziyyah, & Sholikhah, I. (2021). Introduction to Hierarchical Clustering.

Ogbuabor, G., & F. N, U. (2018). Clustering Algorithm for a Healthcare Dataset Using Silhouette Score Value. International Journal of Computer Science and Information Technology, 10(2), 27–37. https://doi.org/10.5121/ijcsit.2018.10203

Ramadhani, L., Purnamasari, I., & Amijaya, F. D. T. (2018). Penerapan Metode Complete Linkage dan Metode Hierarchical Clustering Multiscale Bootstrap (Studi Kasus: Kemiskinan Di Kalimantan Timur Tahun 2016). Eksponensial, 9(2016), 1–10.

Rizal. (2013). Metode Klasterisasi K-Means.

Sipayung, A. T., Saifullah, & Winanjaya, R. (2020). Penerapan Metode K-Means Dalam Mengelompokkan Banyaknya Desa/ Kelurahan Menurut Jenis Pencemaran Lingkungan Hidup Berdasarkan Provinsi. Kesatria: Jurnal Penerapan Sistem Informasi (Komputer & Manajemen), 1(1), 104–111.

Statistik, B. P. (n.d.). Banyaknya Desa/Kelurahan Menurut Jenis Pencemaran Lingkungan Hidup (Desa), 2014-2021. Badan Pusat Statistik.

Swindiarto, V. T. P., Sarno, R., & Novitasari, D. C. R. (2018). Integration of Fuzzy C-Means Clustering and TOPSIS (FCM-TOPSIS) with Silhouette Analysis for Multi Criteria Parameter Data. Proceedings - 2018 International Seminar on Application for Technology of Information and Communication: Creative Technology for Human Life, ISemantic 2018, 463–468.

Wibisono, Y., & Khodra, M. L. (2018). Pengelompokan Artikel Berita Berbahasa Indonesia dengan Agglomerative Clustering. 2014, 11–13.

Widyawati, W., Saptomo, W. L. Y., & Utami, Y. R. W. (2020). Penerapan Agglomerative Hierarchical Clustering Untuk Segmentasi Pelanggan. Jurnal Ilmiah SINUS, 18(1), 75.

Wijaya, A., Ar, F., & Rusyana, A. (2021). Perbandingan Metode Gerombol Pautan Lengkap dan Pautan Rataan untuk Pengelompokan Kemiskinan Kabupaten/Kota di Indonesia. Journal of Data Analysis, 3(1), 13–25.

Downloads

Published

2023-04-08

Issue

Section

Articles