Published: 2025-04-01
Analisis Cluster Untuk Pemetaan Status Gizi Balita Puskesmas Lowokwaru Berdasarkan Antropometri
DOI: 10.35870/jtik.v9i2.3261
Viry Puspaning Ramadhan, Devita Maulina Putri, Indra Dwi Laksana, Dian Fitri Islamiah Munisah
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Abstract
This research examines the mapping of nutritional status in toddlers as an important indicator for assessing child health and development, focusing on the Lowokwaru Health Center in Malang. Data shows that the mapping of nutritional status is still not accurate, prompting this study to identify toddlers at risk of stunting and design appropriate interventions. Quick and accurate mapping of nutritional status is crucial for reducing the risk of stunting and preventing malnutrition in toddlers. The data used comes from anthropometric measurements, including height-for-age (HAZ), weight-for-height (WHZ), and weight-for-age (WAZ). The method applied is K-Means clustering, where, after preprocessing the data, the researchers determine the number of clusters and centroids. Grouping is conducted by calculating Euclidean distance. The results indicate that 75% of toddlers at the Lowokwaru Health Center are experiencing excess nutrition and are at risk of stunting. These findings provide a strong basis for enhancing interventions and preventing stunting in the region.
Keywords
Nutritional Status ; K-Means ; Clustering
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This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 9 No. 2 (2025)
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Section: Computer & Communication Science
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/jtik.v9i2.3261
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Viry Puspaning Ramadhan
Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Merdeka Malang, Kota Malang, Provinsi Jawa Timur, Indonesia.
Devita Maulina Putri
Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Merdeka Malang, Kota Malang, Provinsi Jawa Timur, Indonesia.
Indra Dwi Laksana
Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Merdeka Malang, Kota Malang, Provinsi Jawa Timur, Indonesia.
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Ali, A. (2020). Clustering Data Antropometri Balita Untuk Menentukan Status Gizi Balita Di Kelurahan Jumput Rejo Sukodono Sidoarjo. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 7(3), 395-407. https://doi.org/10.35957/jatisi.v7i3.530.
-
Ashari, I. F., Nugroho, E. D., Baraku, R., Yanda, I. N., & Liwardana, R. (2023). Analysis of elbow, silhouette, Davies-Bouldin, Calinski-Harabasz, and rand-index evaluation on k-means algorithm for classifying flood-affected areas in Jakarta. Journal of Applied Informatics and Computing, 7(1), 95-103. https://doi.org/10.30871/jaic.v7i1.4947.
-
Fatonah, N. S., & Pancarani, T. K. (2022). Analisa Perbandingan Algoritma Clustering Untuk Pemetaan Status Gizi Balita Di Puskesmas Pasir Jaya. Konvergensi Teknologi Informasi & Komunikasi, 18(1), 1-9. https://doi.org/10.30996/konv.v18i1.5497.
-
-
Hasanah, N. N., & Purnomo, A. S. (2022). Implementasi Data Mining Untuk Pengelompokan Buku Menggunakan Algoritma K-Means Clustering (Studi Kasus: Perpustakaan Politeknik LPP Yogyakarta). Jurnal Teknologi Dan Sistem Informasi Bisnis, 4(2), 300-311. https://doi.org/10.47233/jteksis.v4i2.499.
-
Hidayati, R., Indana, L., Karyudi, M. D. P., & Sasongko, R. Z. (2024). Analisis Cluster dengan K-Means untuk Pengelompokan Kabupaten/Kota di Provinsi Jawa Timur Berdasarkan Pembangunan TIK Tahun 2021-2022. Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), 8(2), 405-411. https://doi.org/10.35870/jtik.v8i2.1815.
-
Julyantari, N. K. S., Budiarta, I. K., & Putri, N. M. D. K. (2021). Implementasi K-Means Untuk Pengelompokan Status Gizi Balita (Studi Kasus Banjar Titih). Jurnal Janitra Informatika Dan Sistem Informasi, 1(2), 92-101. https://doi.org/10.25008/janitra.v1i2.134.
-
Loka, S. K. P., & Marsal, A. (2023). Perbandingan Algoritma K-Nearest Neighbor dan Naïve Bayes Classifier untuk Klasifikasi Status Gizi Pada Balita: Comparison Algorithm of K-Nearest Neighbor and Naïve Bayes Classifier for Classifying Nutritional Status in Toddlers. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(1), 8-14. https://doi.org/10.57152/malcom.v3i1.474.
-
Maharani, L. P. K., Angin, A. N. P., Wardhana, P. R., & Achmad, F. (2023). Evaluasi Performa Mahasiswa pada Pembelajaran Mata Kuliah Data Analitik Menggunakan K-Means Clustering: Studi Kasus di Telkom University. Jurnal Konatif: Jurnal Ilmiah Pendidikan, 1(2). https://doi.org/10.62203/jkjip.v1i2.43.
-
Masdarwati, M., Kadir, E., Serli, S., Ruben, S. D., Pannyiwi, R., & Rante, A. (2023). Penyuluhan Tentang Makanan Pendamping Asi Dengan Status Gizi Balita. Sahabat Sosial: Jurnal Pengabdian Masyarakat, 1(2), 40-42. https://doi.org/10.59585/sosisabdimas.v1i2.28.
-
Nurainun, N., Haerani, E., Syafria, F., & Oktavia, L. (2023). Penerapan Algoritma Naïve Bayes Classifier Dalam Klasifikasi Status Gizi Balita dengan Pengujian K-Fold Cross Validation. Journal of Computer System and Informatics (JoSYC), 4(3), 578-586. https://doi.org/10.47065/josyc.v4i3.3414.
-
Rahmadini, R., LorencisLubis, E. E., Priansyah, A., Yolanda, R. W. N., & Meutia, T. (2023). Penerapan Data Mining Untuk Memprediksi Harga Bahan Pangan Di Indonesia Menggunakan Algoritma K-Nearest Neighbor. Jurnal Mahasiswa Akuntansi Samudra, 4(4), 223-235. https://doi.org/10.33059/jmas.v4i4.7074.
-
Rahmawati, T., Wilandari, Y., & Kartikasari, P. (2024). ANALISIS PERBANDINGAN SILHOUETTE COEFFICIENT DAN METODE ELBOW PADA PENGELOMPOKKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR IPM DENGAN K-MEDOIDS. Jurnal Gaussian, 13(1), 13-24. https://doi.org/10.14710/j.gauss.13.1.13-24.
-
Ros, F., Riad, R., & Guillaume, S. (2023). PDBI: A partitioning Davies-Bouldin index for clustering evaluation. Neurocomputing, 528, 178-199. https://doi.org/10.1016/j.neucom.2023.01.043.
-
Rosaliyah, I., & Nurhakim, B. (2023). CLUSTERING KEJADIAN BENCANA ALAM di JAWA BARAT BERDASARKAN JENIS BENCANA MENGGUNAKAN K-MEANS. E-Link: Jurnal Teknik Elektro dan Informatika, 18(1), 10-16. https://doi.org/10.30587/e-link.v18i1.5318.
-
Saputra, A., & Yusuf, R. (2024). Perbandingan Algoritma DBSCAN dan K-MEANS dalam Segmentasi Pelanggan Pengguna Transportasi Publik Transjakarta Menggunakan Metode RFM: Comparison of the DBSCAN and K-MEANS Algorithms in Segmenting Customers Using Public Transportation of Transjakarta Using the RFM Method. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(4), 1346-1361. https://doi.org/10.57152/malcom.v4i4.1516.
-
Septyanto, A. W., & Hariyanto, H. L. (2024). Perbandingan Teknik Klasifikasi Catatan Medis untuk Indeks Antropometri Status Gizi Balita. Jurnal Teknologi Dan Sistem Informasi Bisnis, 6(1), 229-235. https://doi.org/10.47233/jteksis.v6i1.1064.
-
-
Tasia, E. T. E. (2023). Perbandingan Algoritma K-Means Dan K-Medoids Untuk Clustering Daerah Rawan Banjir Di Kabupaten Rokan Hilir: Comparison Of K-Means And K-Medoid Algorithms For Clustering Of Flood-Prone Areas In Rokan Hilir District. Indonesian Journal of Informatic Research and Software Engineering (IJIRSE), 3(1), 65-73.

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