Perbandingan Kinerja Agglomerative Clustering Pada Data Stunting Untuk Segmentasi Wilayah
Abstract
Penelitian ini melakukan segmentasi wilayah berdasarkan faktor – faktor yang mempengaruhi stunting melalui teknik data mining yaitu agglomerative clustering. Hasil uji coba menunjukkan ketika menggunakan single linkage, maka cluster optimal terjadi ketika terbentuk 10 cluster, sedangkan ketika menggunakan average linkage dan complete linkage maka cluster optimal terjadi ketika terbentuk 3 cluster. Hasil analisis yang dapat diambil dari terbentuknya 3 cluster adalah, untuk Kabupaten/Kota yang menjadi anggota cluster 1 merupakan daerah yang mempunyai tingkat kepadatan penduduk tinggi, sedangkan Kabupaten/Kota yang menjadi anggota cluster 3 adalah daerah yang mempunyai tingkat kepadatan penduduk rendah, sedangkan anggota cluster 2 adalah daerah yang mempunyai tingkat kepadatan penduduk diantara anggota cluster 1 dan anggota cluster 3. Berkaitan dengan data stunting, maka kabupaten kotamadya yang termasuk dalam cluster 1 memiliki tingkat stunting rendah, sedangkan cluster 2 memiliki tingkat stunting sedang, dan cluster 3 adalah kabupaten kotamadya yang termasuk ke dalam daerah yang memiliki tingkat stunting tinggi.References
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[5] F. Oktaviana, M. N. Widyawati, K. Kurnianingsih e N. Kubota, “Early Detection of the Risk of Stunting in Pregnant Women and Its Recommendations,” em 2020 International Symposium on Community-centric Systems (CcS), Tokyo, Japan, 2020.
[6] BAPPENAS, “Tujuan Pembangunan Berkelanjutan,” 2022. [Online]. Available: https://sdgs.bappenas.go.id/.
[7] J. Han, M. Kamber e J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann Publishers, 2011.
[8] P. Tan, M. Steinbach e V. Kumar, Cluster Analysis: Basic Concepts and Algorithms. In: Introduction to Data Mining, Boston: Addison-Wesley, 2005.
[9] F. Gorunescu, Data Mining Concepts, Models and Techniques, Heidelberg: Springer Science dan Business Media, 2011.
[10] S. Winiarti, H. Yuliansyah e A. A. Purnama, “Identification of Toddlers’ Nutritional Status using Data Mining Approach,” International Journal of Advanced Computer Science and Applications (IJACSA), pp. 164-169, 2018.
[11] S. Wulandari e R. Kurniawan, “Pengelompokan Kabupaten/Kota Di Jawa Timur Berdasarkan Kasus Stunting Balita Menggunakan Algoritme Fuzzy Particle Swarm Optimization-Fuzzy C-Means,” Statistika, Vol. 7, No. 1, 2019.
[12] D. Jollyta, S. Efendi, M. Zarlis e H. Mawengkang, “Optimasi Cluster Pada Data Stunting Teknik Evaluasi Cluster Sum of Square Error dan Davies Bouldin Index,” em Prosiding Seminar Nasional Riset Information Science (SENARIS), 2019.
[13] BPS, “BPS Propinsi Jawa Timur,” [Online]. Available: https://jatim.bps.go.id/subject/30/kesehatan.html#subjekViewTab3.
[14] P. Praveen, R. Kumar, M. A. Shaik, R. Ravikumar e R. Kiran, “The Comparative Study On Agglomerative Hierarchical Clustering Using Numerical Data,” em ICRAEM 2020.
[15] A. A. Munshi, “Clustering of Wind Power Patterns Based on Partitional and Swarm Algorithms,” IEEE Access ( Volume: 8), pp. 111913 - 111930, 2020.
Published
2023-12-11
How to Cite
OKTAVIANTO, Hardian; BAKTI, Budi Satria; COBANTORO, Adi Fajaryanto.
Perbandingan Kinerja Agglomerative Clustering Pada Data Stunting Untuk Segmentasi Wilayah.
BINA INSANI ICT JOURNAL, [S.l.], v. 10, n. 2, p. 145-154, dec. 2023.
ISSN 2527-9777.
Available at: <http://ejournal-binainsani.ac.id/index.php/BIICT/article/view/2657>. Date accessed: 08 sep. 2024.
doi: https://doi.org/10.51211/biict.v10i2.2657.
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
