ANALYZING THE EFFECT OF SIMILARITY FUNCTIONS ON PARTITIONING AROUND MEDOIDS ALGORITHM FOR MAPPING DHF DISEASE IN NORTH SUMATRA
Abstract
Dengue hemorrhagic fever (DHF) is an acute febrile illness caused by a virus through the Aedes mosquito. North Sumatra is among the three provinces with the highest incidence and mortality rates in Indonesia. Mapping of DHF cases is very important in efforts to control and prevent the disease. The Partitioning Around Medoid (PAM) algorithm is commonly used to cluster DHF cases. The idea of PAM is a clustering algorithm with a similarity-based approach to grouping objects in one cluster. There are two main focuses in the research: mapping regencies/cities based on dengue case information and analyzing the performance of several similarity functions. The dataset includes variables of incidence rate (IR), case fatality rate (CFR), larva-free rate (ABJ), and population, obtained from the North Sumatra Provincial Health Office and the Central Statistics Agency (BPS). The analysis showed that three clusters were formed in North Sumatra Province. The first cluster includes regencies/cities such as Langkat, Deli Serdang, Karo, Simalungun, Dairi, Samosir, Humbahas, North Labuhan Batu, North Padang Lawas, South Labuhan Batu, Padang Sidempuan, Nias, South Nias, North Nias, and Sibolga. The second cluster consists of regencies/cities such as Medan, Binjai, Sedang Berdagai, Tebing Tinggi, Batubara, Asahan, Tanjung Balai, Labuhan Batu, Toba, North Tapanuli, Central Tapanuli, Gunungsitoli, and West Nias. The third cluster includes the regencies of South Tapanuli and Mandailing Natal. In addition, an evaluation was conducted using the Silhouette Index to measure the quality of the clustering. Based on the comparison using distance methods (Euclidean distance, Manhattan distance, Minkowski distance, and Chebyshev distance), the highest Silhouette Index value was obtained using Chebyshev distance, which amounted to 0.527554. This value indicates reasonable cluster quality. Thus, this study contributes to the mapping of DHF cases in North Sumatra Province and can be the basis for decision-making in overcoming the disease.
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References
N. Y. Lindawati, L. Murtisiwi, T. A. Rahmania, P. N. Damayanti, and F. M. Widyasari, “UPAYA PENINGKATAN PENGETAHUAN MASYARAKAT DALAM RANGKA PENCEGAHAN DAN PENANGGULANGAN DBD DI DESA DLINGO, MOJOSONGO, BOYOLALI,” SELAPARANG Jurnal Pengabdian Masyarakat Berkemajuan, vol. 4, no. 2, 2021, doi: 10.31764/jpmb.v4i2.4305.
R. Deni and A. Kurnianto, “Data Mapping System Of Riau Province Fire Potential Using K-Means Clustering Method,” JAIA - Journal of Artificial Intelligence and Applications, vol. 1, no. 1, 2020, doi: 10.33372/jaia.v1i1.640.
W. N. Fadillah, Y. M. Rangkuti, I. Muslim, and K. Karo, “Implementasi Partitioning Around Medoids Pada Visualisasi Penyebaran Penyakit DBD di Sumatera Utara,” Journal of Mathematics, vol. 6, no. 2, pp. 128–137, 2023, doi: https://doi.org/10.35580/jmathcos.v6i2.52350.
D. R. Agustian and B. A. Darmawan, “ANALISIS CLUSTERING DEMAM BERDARAH DENGUE DENGAN ALGORITMA K-MEDOIDS (STUDI KASUS KABUPATEN KARAWANG),” JIKO (Jurnal Informatika dan Komputer), vol. 6, no. 1, 2022, doi: 10.26798/jiko.v6i1.504.
S. Suprihatin, Y. R. W. Utami, and D. Nugroho, “K-MEANS CLUSTERING UNTUK PEMETAAN DAERAH RAWAN DEMAM BERDARAH,” Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN), vol. 7, no. 1, 2019, doi: 10.30646/tikomsin.v7i1.408.
S. Wulandari and N. Dwitiyanti, “Implementasi Algoritma Clustering Partitioning Around Medoid (PAM) dalam Clustering Virus MERS-Cov,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 5, no. 1, 2020, doi: 10.30998/string.v5i1.6469.
T. Akbar, G. M. Tinungki, and S. Siswanto, “PERFORMANCE COMPARISON OF K-MEDOIDS AND DENSITY BASED SPATIAL CLUSTERING OF APPLICATION WITH NOISE USING SILHOUETTE COEFFICIENT TEST,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 3, pp. 1605–1616, Sep. 2023, doi: 10.30598/barekengvol17iss3pp1605-1616.
S. Wulandari and N. Dwitiyanti, “IMPLEMENTASI ALGORITMA CLUSTERING PARTITIONING AROUND MEDOID (PAM) DALAM CLUSTERING VIRUS MERS-CoV,” 2020. [Online]. Available: www.ncbi.nlm.nih.gov.
C. Cindy, C. Cynthia, V. Vito, D. Sarwinda, B. D. Handari, and G. F. Hertono, “Cluster Analysis on Dengue Incidence and Weather Data Using K-Medoids and Fuzzy C-Means Clustering Algorithms (Case Study: Spread of Dengue in the DKI Jakarta Province),” Journal of Mathematical and Fundamental Sciences, vol. 53, no. 3, 2022, doi: 10.5614/j.math.fund.sci.2021.53.3.9.
I. M. K. Karo and A. F. Huda, “Spatial clustering for determining rescue shelter of flood disaster in South Bandung using CLARANS Algorithm with Polygon Dissimilarity Function,” in Proceedings - 2016 12th International Conference on Mathematics, Statistics, and Their Applications, ICMSA 2016: In Conjunction with the 6th Annual International Conference of Syiah Kuala University, 2017. doi: 10.1109/ICMSA.2016.7954311.
D. D. Abdurrahman, F. Agus, and G. M. Putra, “Implementasi Algoritma Partitioning Around Medoids (PAM) untuk Mengelompokkan Hasil Produksi Komoditi Perkebunan (Studi Kasus: Dinas Perkebunan Provinsi Kalimantan Timur),” Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, vol. 16, no. 2, 2021, doi: 10.30872/jim.v16i2.6520.
A. Aditya, B. Nurina Sari, and T. Nur Padilah, “Perbandingan pengukuran jarak Euclidean dan Gower pada klaster k-medoids,” Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 1, pp. 1–7, 2021, doi: 10.14710/jtsiskom.2021.13747.
A. Moayedi, R. A. Abbaspour, and A. Chehreghan, “An evaluation of the efficiency of similarity functions in density-based clustering of spatial trajectories,” Ann GIS, vol. 25, no. 4, 2019, doi: 10.1080/19475683.2019.1679254.
K. Taghva and R. Veni, “Effects of similarity metrics on document clustering,” in ITNG2010 - 7th International Conference on Information Technology: New Generations, 2010. doi: 10.1109/ITNG.2010.65.
I. M. K. Karo, A. F. Huda, and K. MaulanaAdhinugraha, “A cluster validity for spatial clustering based on davies bouldin index and Polygon Dissimilarity function,” in Proceedings of the 2nd International Conference on Informatics and Computing, ICIC 2017, 2018. doi: 10.1109/IAC.2017.8280572.
L. R. Costella Pessutto, D. Suarez Vargas, and V. P. Moreira, “Clustering Multilingual Aspect Phrases for Sentiment Analysis,” in Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018, 2019. doi: 10.1109/WI.2018.00-91.
I. M. Karo Karo, A. Yusmanto, and R. Setiawan, “Segmentasi Nasabah Kartu Kredit Berdasarkan Perilaku Penggunaan Kartu Kreditnya Menggunakan Algoritma K-Means,” Journal of Software Engineering, Information and Communication Technology, vol. 2, no. 2, pp. 101–107, 2021, [Online]. Available: https://www.kaggle.com/arjunbhasin2013/ccdata.
D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl Soft Comput, vol. 97, 2020, doi: 10.1016/j.asoc.2019.105524.
I. M. Karo Karo and H. Hendriyana, “Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score,” Jurnal Teknologi Terpadu, vol. 8, no. 2, pp. 94–99, 2022.
L. A. Ibrahim and I. Fekete, “What machine learning can tell us about the role of language dominance in the diagnostic accuracy of German LITMUS non-word and sentence repetition tasks,” Front Psychol, vol. 9, no. JAN, 2019, doi: 10.3389/fpsyg.2018.02757.
S. A. P. Raj and Vidyaathulasiraman, “Determining Optimal Number of K for e-Learning Groups Clustered using K-Medoid,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, 2021, doi: 10.14569/IJACSA.2021.0120644.
I. M. Karo Karo, S. Dewi, M. Mardiana, F. Ramadhani, and P. Harliana, “K-Means and K-Medoids Algorithm Comparison for Clustering Forest Fire Location in Indonesia,” Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering), vol. 10, no. 1, 2023, doi: 10.33019/jurnalecotipe.v10i1.3896.
M. Nishom, “Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 4, no. 1, 2019, doi: 10.30591/jpit.v4i1.1253.
I. M. K. Karo, A. Khosuri, and R. Setiawan, “Effects of Distance Measurement Methods in K-Nearest Neighbor Algorithm to Select Indonesia Smart Card Recipient,” in 2021 International Conference on Data Science and Its Applications, ICoDSA 2021, 2021. doi: 10.1109/ICoDSA53588.2021.9617476.
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