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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Diferensial
Gani, Friansyah
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Public Health

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Implementation of K-Nearest Neighbor Algorithm on Density-Based Spatial Clustering Application with Noise Method on Stunting Clustering Gani, Friansyah; Panigoro, Hasan S.; Mahmud, Sri Lestari; Rahmi, Emli; Nasib, Salmun K.; Nashar, La Ode
JURNAL DIFERENSIAL Vol 6 No 2 (2024): November 2024
Publisher : Program Studi Matematika, Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jd.v6i2.16278

Abstract

This paper studies the implementation of the K-Nearest Neighbor (KNN) algorithm on Density-Based Spatial Clustering Application with Noise (DBSCAN) method on stunting Clustering in the eastern region of Indonesia in 2022. The DBSCAN method is used because it is more efficient to perform the Clustering process for irregular Clustering shapes. The main objective of this study is to apply the KNN algorithm to the DBSCAN Clustering technique in 161 Districts/Cities in 11 provinces in eastern Indonesia. A comparison of the performance evaluation of the DBSCAN Clustering technique is done by considering the value of the Silhouette score, BetaCV score, and Davies-Bouldin score indicating the quality of the Clusters formed with the lowest results scores of 0.67 and 1.84 with epsilon value = 3.4 and minimum point value = 2 resulting in 4 Clusters. The results of Clustering 161 Districts and Cities based on the factors that cause stunting formed 4 Clusters where Cluster 0 consists of 119 Districts and Cities with very high stunting characteristics, Cluster 1 consists of 3 Districts and Cities with high stunting characteristics, the results of Cluster 2 consist of 2 Districts and Cities with low stunting characteristics, then the results of Cluster 2 consist of 2 Districts and Cities with low stunting characteristics and Cluster 3 consists of 2 Cities with very low stunting characteristics.
Modeling Fuzzy Geographically Weighted Clustering with Flower Pollination Algorithm for Spatial Optimization and Clustering Gani, Friansyah; Pramoedyo, Henny; Efendi, Achmad
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.36800

Abstract

This study aims to analyze the clustering of districts/cities in East Nusa Tenggara Province (NTT) using the Fuzzy Geographically Weighted Clustering method optimized through the Flower Pollination Algorithm (FGWC-FPA). The data consist of eight health and sanitation indicators for 2024. The analysis produced two clusters with distinct characteristics. Cluster 1 is dominated by areas with relatively higher rates of complementary feeding and good BCG immunization coverage but still shows a higher proportion of low birth weight (LBW) infants and limited access to drinking water and sanitation. Meanwhile, Cluster 2 demonstrates significant advantages in access to proper drinking water (90.37%) and proper sanitation (83.19%), as well as more optimal Hepatitis B immunization coverage. Evaluation of cluster validity using Classification Entropy (CE) and the Separation Index (SI) shows that the best configuration is obtained at m = 1.5 with c = 2, yielding the lowest CE value (0.584872) and reasonably good cluster separation (SI = 1.069092). Thus, the FGWC-FPA method is capable of producing optimal cluster partitioning and can serve as a basis for formulating more targeted health intervention strategies in NTT.
Co-Authors Achmad Efendi Hasan S. Panigoro Henny Pramoedyo La Ode Nashar Mahmud, Sri Lestari Rahmi, Emli Salmun K. Nasib