PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clustering
Assuring effective allocation requires targeted distribution of aid, which makes aid clustering a crucial component. For the purpose of using data-driven segmentation based on important characteristics to determine effective help targeting, accuracy in clustering is essential. The study explores th...
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| Format: | Article |
| Language: | English |
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Informatics Department, Engineering Faculty
2025-07-01
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| Series: | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi |
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| Online Access: | http://www.kursorjournal.org/index.php/kursor/article/view/460 |
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| author | Achmad Jauhari Ika Oktavia Suzanti Devie Rosa Anamisa Fadhila Tangguh Admojo |
| author_facet | Achmad Jauhari Ika Oktavia Suzanti Devie Rosa Anamisa Fadhila Tangguh Admojo |
| author_sort | Achmad Jauhari |
| collection | DOAJ |
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Assuring effective allocation requires targeted distribution of aid, which makes aid clustering a crucial component. For the purpose of using data-driven segmentation based on important characteristics to determine effective help targeting, accuracy in clustering is essential. The study explores the combination of Principal ComponentAnalysis (PCA), k-means, and k-medoids to enhance aid clusters, with the goal ofincreasing aid distribution accuracy and efficiency. The information gathered consists of 1600 records with 13 attributes. In order to standardized the data having two processes in it, preprocessing is first applied. When used with PCA, it makes measuring variance easier and preserves 80% of the variation by choosing five components. Thenumber of clusters may be determined with the use of PCA, k-medoids, and the k-means approach. Greater PCA-k-means silhouette coefficients, which indicate betterclustering ability, are highlighted by comparative analysis. This analysis shows thatPCA-k-means is an effective technique for creating accurate and unique clusters withina data set's structure.The clustering results using the PCA-k-means algorithm have produced the greatest accuracy in the silhouette score of 0.49 and the DBI score is 0.84.
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| format | Article |
| id | doaj-art-4a5db65048a344ee897f358b64d64a6a |
| institution | DOAJ |
| issn | 0216-0544 2301-6914 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Informatics Department, Engineering Faculty |
| record_format | Article |
| series | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi |
| spelling | doaj-art-4a5db65048a344ee897f358b64d64a6a2025-08-20T02:58:03ZengInformatics Department, Engineering FacultyJurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi0216-05442301-69142025-07-0113110.21107/kursor.v13i1.460PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clusteringAchmad Jauhari0Ika Oktavia Suzanti1Devie Rosa Anamisa2Fadhila Tangguh Admojo3University of Trunojoyo MaduraUniversity of Trunojoyo MaduraUniversity of Trunojoyo MaduraUniversiti kuala lumpur Assuring effective allocation requires targeted distribution of aid, which makes aid clustering a crucial component. For the purpose of using data-driven segmentation based on important characteristics to determine effective help targeting, accuracy in clustering is essential. The study explores the combination of Principal ComponentAnalysis (PCA), k-means, and k-medoids to enhance aid clusters, with the goal ofincreasing aid distribution accuracy and efficiency. The information gathered consists of 1600 records with 13 attributes. In order to standardized the data having two processes in it, preprocessing is first applied. When used with PCA, it makes measuring variance easier and preserves 80% of the variation by choosing five components. Thenumber of clusters may be determined with the use of PCA, k-medoids, and the k-means approach. Greater PCA-k-means silhouette coefficients, which indicate betterclustering ability, are highlighted by comparative analysis. This analysis shows thatPCA-k-means is an effective technique for creating accurate and unique clusters withina data set's structure.The clustering results using the PCA-k-means algorithm have produced the greatest accuracy in the silhouette score of 0.49 and the DBI score is 0.84. http://www.kursorjournal.org/index.php/kursor/article/view/460Aid dataClusteringK-MeansK-MedoidsPrincipal Component Analysis |
| spellingShingle | Achmad Jauhari Ika Oktavia Suzanti Devie Rosa Anamisa Fadhila Tangguh Admojo PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clustering Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi Aid data Clustering K-Means K-Medoids Principal Component Analysis |
| title | PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clustering |
| title_full | PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clustering |
| title_fullStr | PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clustering |
| title_full_unstemmed | PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clustering |
| title_short | PCA-counseled k-means and k-medoids with dimension reduction for improved in determining optimal aid clustering |
| title_sort | pca counseled k means and k medoids with dimension reduction for improved in determining optimal aid clustering |
| topic | Aid data Clustering K-Means K-Medoids Principal Component Analysis |
| url | http://www.kursorjournal.org/index.php/kursor/article/view/460 |
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