Geographic Information System Mapping Risk Factors Stunting Using Methods Geographically Weighted Regression
Abstract
Technological developments in this era of globalization are very rapid. This requires humans to enter life together with information and technology. Stunting as a chronic nutritional problem in children, continues to be a global challenge. Geographic Information Systems (GIS) have proven to be effective tools in spatial analysis and distribution mapping stunting. In this context, method Geographically Weighted Regression (GWR) has been used to model the spatial relationship between factors that contribute to stunting. This research will produce a Geographic Information System using the method Geographically Weighted Regression. With this Geographic Information System, it can display location points and affected information stunting. Because of this system, the Padang Lawas Utara District Health Office does not need to store location data stunting in archive form again but digitally. This study underscores the importance of using GIS with the GWR method in mapping patient locations stunting. Through the integration of geographic data and spatial analysis, we can generate a better understanding of the influencing factors stunting at the local level, which in turn can support prevention and response efforts stunting which is more effective.
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References
Ade Saputra, Yoyok Seby Dwanoko, & Aan Jelli Priana. (2020). Rancang Bangun Sistem Informasi Geografis Pemetaan Penyebaran Penyakit Stunting Di Kabupaten Malang. Rainstek Jurnal Terapan Sains Dan Teknologi, 2(4), 260–269. https://doi.org/10.21067/jtst.v2i4.5064
Amarrohman, F. J., Wahyuddin, Y., & Patricia, E. (2023). Analysis of Land Value in the Area Surrounding the Central Business District (CBD) of Simpang Lima, Semarang City Using Geographically Weighted Regression (GWR). Journal of Applied Geospatial Information, 7(2), 919–926. https://doi.org/10.30871/jagi.v7i2.6361
Amelia, K., Asril, L. O., & Febrianti, L. (2020). Pemodelan Incident Rate Demam Berdarah Dengue Di Indonesia Yang Berkaitan Dengan Faktor Lingkungan Menggunakan Metode Geographically Weighted Regression (Gwr). Ekologia, 20(2), 64–73. https://doi.org/10.33751/ekologia.v20i2.2167
Andarsyah, R., & Fadilla, R. (2020). APLIKASI LELANG ONLINE GEOGRAPHIC INFORMATION SYSTEM ( WEBGIS ) INTELLIGENCE PT . PEGADAIAN ( PERSERO ) MENGGUNAKAN METODE RESEARCH AND DEVELOPMENT ( R & D ). 12(2).
Chen, L., Zhong, Q., & Li, Z. (2023). Analysis of spatial characteristics and influence mechanism of human settlement suitability in traditional villages based on multi-scale geographically weighted regression model: A case study of Hunan province. Ecological Indicators, 154(May), 110828. https://doi.org/10.1016/j.ecolind.2023.110828
Chen, X., Emam, M., Zhang, L., Rifhat, R., Zhang, L., & Zheng, Y. (2023). Analysis of spatial characteristics and geographic weighted regression of tuberculosis prevalence in Kashgar, China. Preventive Medicine Reports, 35(April), 102362. https://doi.org/10.1016/j.pmedr.2023.102362
Diastina, A. R. N., Handajani, S. S., & Slamet, I. (2019). Analisis Model Geographically Weighted Regression (GWR) pada Kasus Jumlah Peserta KB Aktif di Provinsi Jawa Tengah. Prosiding Seminar Nasional Geotik, 364–373.
Fadliana, A., & Darajat, P. P. (2021). Pemetaan Faktor Risiko Stunting Berbasis Sistem Informasi Geografis Menggunakan Metode Geographically Weighted Regression. Jurnal IkraiTth-Informatika, 5(3), 91–102.
Fathurrahman, I., Suhartini, S., Ahmadi, H., & Fathurrahman, F. (2022). Sistem Informasi Geografis Pemetaan Lokasi Stunting Di Desa Gereneng Timur Berbasis Web. Jurnal Komtika (Komputasi Dan Informatika), 6(2), 122–132. https://doi.org/10.31603/komtika.v6i2.8157
Febrian, D., & Nasir, M. (2021). Sistem Informasi Geografis Pariwisata Kabupaten Bangka Barat Berbasis WEB. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 10(3), 334–339. https://doi.org/10.32736/sisfokom.v10i3.1262
Firmansyah, G. A., Zeniarja, J., Azies, H. Al, Winarno, S., & Ganiswari, S. P. (2023). Machine Learning-Enhanced Geographically Weighted Regression for Spatial Evaluation of Human Development Index across Western Indonesia. 7(2).
Halimah, N., & Suntin, S. (2020). Proyeksi dan Pemetaan Wilayah Sebaran Balita Stunting Di Kota Makassar Berbasis Sistem Informasi Geografi (SIG). Promotif: Jurnal Kesehatan Masyarakat, 10(2), 173–184.
Kartika, S., & Kholijah, G. (2020). Penggunaan Metode Geograhically Weighted Regression ( GWR ) Untuk Mengestimasi Faktor Dominan yang Mempengaruhi Penduduk Miskin di Provinsi Jambi. Journal of Matematics: Theory and Applications, 2(2), 37–45. https://ojs.unsulbar.ac.id/index.php/Mathematics/article/view/998
Kurniawan, A., Suendri, & Triase. (2019). Sistem Informasi Geografis Pemetaan Lokasi Panti Asuhan Di Kota Medan. JISTech (Journal of Islamic Science and Technology), 4(2), 118–128. http://jurnal.uinsu.ac.id/index.php/jistech/article/view/6541
Kurniawan, H., Apriliah, W., Kurnia, I., & Firmansyah, D. (2021). Penerapan Metode Waterfall Dalam Perancangan Sistem Informasi Penggajian Pada Smk Bina Karya Karawang. Jurnal Interkom: Jurnal Publikasi Ilmiah Bidang Teknologi Informasi Dan Komunikasi, 14(4), 13–23. https://doi.org/10.35969/interkom.v14i4.78
Liu, X., Wang, X., Wright, G., Cheng, J. C. P., Li, X., & Liu, R. (2017). A state-of-the-art review on the integration of Building Information Modeling (BIM) and Geographic Information System (GIS). ISPRS International Journal of Geo-Information, 6(2), 1–21. https://doi.org/10.3390/ijgi6020053
Lutfiani, N., Sugiman, & Mariani, S. (2019). Pemodelan Geographically Weighted Regression (GWR) dengan Fungsi Pembobot Kernel Gaussian dan Bi-square. UNNES Journal of Mathematics, 5(1), 82–91. http://journal.unnes.ac.id/sju/index.php/ujmUJM8
Muna, N., Herdayanti, D. M., Erawantini, F., & Yunus, M. (2022). Pemetaan Persebaran dan Pengukuran Balita Stunting di Kabupaten Jember Berbasis Sistem Informasi Geografis. 13(November), 153–160.
Putra, P. A. B., & Suariyani, N. L. P. (2021). Pemetaan Distribusi Kejadian Dan Faktor Risiko Stunting Di Kabupaten Bangli Tahun 2019 Dengan Menggunakan Sistem Informasi Geografis. Archive of Community Health, 8(1), 72. https://doi.org/10.24843/ach.2021.v08.i01.p06
Rahayu, A., Yulidasari, F., Putri, A. O., & Anggraini, L. (2018). Study Guide - Stunting dan Upaya Pencegahannya. In Buku stunting dan upaya pencegahannya.
RI, K. K. (2022). Hasil Survei Status Gizi Indonesia (SSGI). Https://Kesmas.Kemkes.Go.Id/.
Riswandi, A., Zufria, I., Irawan, M. D., Infromasi, S., Teknologi, S., Islam, U., Sumatera, N., Iain, J., Gaharu, N., Medan, K., Medan, K., & Utara, S. (2023). Sistem Informasi Geografis Untuk Monitoring Menara Telekomunikasi Menggunakan Metode Haversine Berbasis Android Geographic Information System for Monitoring Telecommunication Towers Using the Haversine Method Based on Android. 0(01), 15–21.
Samosir, R. S., & Purwandri, N. (2020). Aplikasi Literasi Digital Berbasis Web Dengan Metode. 19(2), 157–167.
Vennithasari, R., & Papilaya, F. S. (2020). Analysis of Green Land Changes to Building Land Using Geographic Information System (GIS) in Salatiga City from 2013 to 2019. Journal of Applied Geospatial Information, 4(2), 350–355. https://doi.org/10.30871/jagi.v4i2.2048
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