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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