Analisis Geospasial Kasus Stunting menggunakan Artificial Neural Network (ANN) di Kecamatan Gadingrejo, Pringsewu-Lampung

https://doi.org/10.22146/mgi.70474

Mochamad Firman Ghazali(1*), Araneta Aqzela(2), Christas Gracia(3), Raudya Santy Febriningtyas(4), Dewi Wijayanti(5)

(1) Teknik Geodesi dan Geomatika, Fakultas Teknik, Universitas Lampung
(2) Teknik Geodesi dan Geomatika, Fakultas Teknik, Universitas Lampung-Lampung
(3) Teknik Geodesi dan Geomatika, Fakultas Teknik, Universitas Lampung-Lampung
(4) Teknik Geodesi dan Geomatika, Fakultas Teknik, Universitas Lampung-Lampung
(5) Teknik Geofisika, Fakultas Teknik, Universitas Lampung-Lampung
(*) Corresponding Author

Abstract


Abstrak.Tingginya prevalensi stunting dipicu oleh kurangnya kualitas hidup balita di awal pertumbuhannya. Hal ini dapat berpengaruh pada rendahnya kualitas sumberdaya manusia dari banyak generasi penerus bangsa. Kajian stunting secara spasial menggunakan artificial neural network (ANN) bertujuan untuk mengetahui pola spasial dan prediksi tingkat kerawanan di wilayah lain di sekitarnya. Analisis dilakukan berdasarkan kondisi sosial-ekonomi dan budaya dari orang tua balita penderita stunting yang diperoleh dari wawancara, diolah dengan inverse distance weighted (IDW) dan diintegrasikan dengan hasil olah citra satelit Landsat 8 OLI-TIRS, berupa percent building density (PBD), land surface temperature (LST), normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), dan normalized difference built-up index (NDBI). Model ANN dijalankan dengan metode back propagation, variasi jumlah hidden layer sebanyak 3, 5, dan 7, dengan variasi input prediksi mampu menghasilkan variasi distribusi stunting dan tingkat akurasinya. Berdasarkan nilai root mean square error (RMSE), bertambahnya jumlah hidden layer dan variasi input prediksi berkontribusi untuk menghasilkan akurasi hasil prediksi lebih baik, yakni 68%-93%. Secara spasial, keduanya secara langsung menjelaskan juga perubahan distribusi pola spasial kerawanan stunting di keseluruhan wilayah studi.

 

Abstract. Lower toddler's life quality triggers the high prevalence of stunting at the beginning of their growth. This factor can affect many future generations' low quality of human resources. Studying stunting spatially using an artificial neural network (ANN) aims to determine the spatial pattern and predict the level of vulnerability in other surrounding areas. The analysis was carried out based on the socio-economic and cultural conditions of parents of children with stunting obtained from interviews, processed by inverse distance weighted (IDW) and integrated with the results of Landsat 8 OLI-TIRS satellite imagery, in the form of percent building density (PBD), land surface temperature (LST), normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and normalized difference built-up index (NDBI). The ANN model is run using the back propagation method, with variations in the number of hidden layers as many as 3, 5, and 7, with variations in predictive input capable of producing variations in the stunting distribution and the level of accuracy. Based on the value of the root mean square error (RMSE), the increasing number of hidden layers and variations in input predictions contribute to producing better prediction accuracy, which is 68%-93%. Spatially, both directly explain the changes in the distribution of the spatial pattern of stunting susceptibility in the entire study area.



 


Keywords


analisis spasial; stunting; artificial neural network; social-ekonomi dan budaya; Landsat 8

Full Text:

PDF


References

Ajaj, Q. M., Shareef, M. A., Hassan, N. D., Hasan, S. F., & Noori, A. M. (2018). GIS based spatial modeling to mapping and estimation relative risk of different diseases using inverse distance weighting (IDW) interpolation algorithm and evidential belief function (EBF) (Case study: Minor Part of Kirkuk City, Iraq). International Journal of Engineering and Technology(UAE), 7(4), 185–191. https://doi.org/10.14419/ijet.v7i4.37.24098

Akhand, K., Nizamuddin, M., Roytman, L., & Kogan, F. (2016). Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh. Remote Sensing and Modeling of Ecosystems for Sustainability XIII, 9975, 997508. https://doi.org/10.1117/12.2237214

Alfarisi, R., Nurmalasari, Y., Nabilla, S., Dokter, P. P., Kedokteran, F., Malahayati, U., … Malahayati, U. (2019). Status Gizi Ibu Hamil Dapat Menyebabkan. Jurnal Kebidanan, 5(3), 271–278.

Aramico, B., Sudargo, T., & Susilo, J. (2016). Hubungan sosial ekonomi, pola asuh, pola makan dengan stunting pada siswa sekolah dasar di Kecamatan Lut Tawar, Kabupaten Aceh Tengah. Jurnal Gizi Dan Dietetik Indonesia (Indonesian Journal of Nutrition and Dietetics), 1(3), 121. https://doi.org/10.21927/ijnd.2013.1(3).121-130

Ardiansyah, Hernina, R., Suseno, W., Zulkarnain, F., Yanidar, R., & Rokhmatuloh, R. (2019). Percent of building density (PBD) of urban environment: A multi-index Approach Based Study in DKI Jakarta Province. Indonesian Journal of Geography, 50(2), 154–161. https://doi.org/10.22146/ijg.36113

Bappenas. (2019). Pembangunan Gizi di Indonesia. In Kementerian PPN/Bappenas.

Canziani, G., Ferrati, R., Marinelli, C., & Dukatz, F. (2008). Artificial neural networks and remote sensing in the analysis of the highly variable pampean shallow lakes. Mathematical Biosciences and Engineering, 5(4), 691–711. https://doi.org/10.3934/mbe.2008.5.691

Cartalis, C. (2019). Advanced Thermal Remote Sensing. 2019 Advanced International Training Course in Land Remote Sensing, 80. Chongqing: Chongqing University.

Chai, T., & Draxler, R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? –Arguments against avoiding RMSE in the literature. Geoscience Model Development, 7, 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014

Damayanti, N. (2017). Klasifikasi Penyakit Paru Dengan Metode Artificial Neural Network (ANN) (Studi Kasus : RSUD Kertosono). Institut Teknologi Sepuluh Nopember Repository, 115.

Gao, B. C. (1996). NDWI-A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing Environment, 58, 257–266.

Gopal, S. (2016). Artificial Neural Networks in Geospatial Analysis. In International Encyclopedia of Geography: People, the Earth, Environment and Technology (pp. 1–7). https://doi.org/10.1002/9781118786352.wbieg0322

Hermantoro, Rudiyanto, & Suprayogi, S. (2008). Aplikasi Model Artificial Neural Network Terintegrasi Dengan Geographycal Information System Untuk Evaluasi Kesesuaian Lahan Perkebunan Kakao. Jurnal Keteknikan Pertanian, 22(1), 15–20.

Huete, A. R. (2004). R Emote S Ensing for. In Environmental Monitoring and Characterization. Elsevier, Inc. https://doi.org/10.1016/B978-0-12-064477-3.50013-8

Indrastuty, D., & Pujiyanto, P. (2019). Determinan Sosial Ekonomi Rumah Tangga dari Balita Stunting di Indonesia: Analisis Data Indonesia Family Life Survey (IFLS) 2014. Jurnal Ekonomi Kesehatan Indonesia, 3(2), 68–75. https://doi.org/10.7454/eki.v3i2.3004

Kemenkes RI. (2015). Kesehatan dalam Kerangka Sustainable Development Goals (SDGs). Rakorpop Kementerian Kesehatan RI, 85. Jakarta. Retrieved from https://sdgs.bappenas.go.id/wp-content/uploads/2017/09/Kesehatan-Dalam-Kerangka-SDGs.pdf

KemenKes RI. (2017). Hasil Pemantauan Status Gizi (PSG) Tahun 2016. Jakarta. Retrieved from https://kesmas.kemkes.go.id/assets/uploads/contents/others/Buku-Saku-Hasil-PSG-2016_842.pdf

Kerkhoff, D., & Nussbeck, F. W. (2019). The influence of sample size on parameter estimates in three-level random-effects models. Frontiers in Psychology, 10, 18. https://doi.org/10.3389/fpsyg.2019.01067

Kholia, T., Fara, Y. D., Mayasari, A. T., & Abdullah. (2020). Hubungan Faktor Ibu Dengan Kejadian Stunting. Jurnal Maternitas Aisyah, 1(3), 189–197. Retrieved from https://proceedings.uhamka.ac.id/index.php/semnas/article/view/171

Lin, C., Thomson, G., & Popescu, S. C. (2016). An IPCC-compliant technique for forest carbon stock assessment using airborne LiDAR-derived tree metrics and competition index. Remote Sensing, 8(6), 1–19. https://doi.org/10.3390/rs8060528

Mahendra, T. T., Setiawati, S., & Wandini, R. (2022). Status gizi ibu saat hamil dengan kejadian stunting pada batita. Holistik Jurnal Kesehatan, 15(4), 674–681. https://doi.org/10.33024/hjk.v15i4.1617

Marchand, V. (2012). The toddler who is falling off the growth chart. Position statements and practice points. Canadian Paediatric Society. Paediatr Child Health, 17(8), 447.

Mas, J. F., & Flores, J. J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3), 617–663. https://doi.org/10.1080/01431160701352154

McGovern, M. E., Krishna, A., Aguayo, V. M., & Subramanian, S. V. (2017). A review of the evidence linking child stunting to economic outcomes. International Journal of Epidemiology, 46(4), 1171–1191. https://doi.org/10.1093/ije/dyx017

Mollalo, A., Mao, L., Rashidi, P., & Glass, G. E. (2019). A gis-based artificial neural network model for spatial distribution of tuberculosis across the continental united states. International Journal of Environmental Research and Public Health, 16(1), 1–17. https://doi.org/10.3390/ijerph16010157

Nshimyiryo, A., Hedt-Gauthier, B., Mutaganzwa, C., Kirk, C. M., Beck, K., Ndayisaba, A., … El-Khatib, Z. (2019). Risk factors for stunting among children under five years: A cross-sectional population-based study in Rwanda using the 2015 Demographic and Health Survey. BMC Public Health, 19(1), 1–10. https://doi.org/10.1186/s12889-019-6504-z

Nurjanna. (2019). Determinan Sosial Budaya Kejadian Stunting Pada Suku Makassar Di Kecamatan Turatea Kabupaten Jeneponto (Universitas Islam Negeri). Universitas Islam Negeri. Retrieved from http://repositori.uin-alauddin.ac.id/16406/1/NURJANNA_70200115040.pdf

Pusat Data dan Informasi. (2018). Situasi Balita Pendek (Stunting) di Indonesia. In E. S. Sakti (Ed.), Buletin Jendela Data dan Informasi Kesehatan (1st ed.). Jakarta: Pusat Data dan Informasi Sekretaris Jendral Kementrian Kesehatan RI. Retrieved from https://pusdatin.kemkes.go.id

Puspasari, N., & Andriani, M. (2017). Hubungan Pengetahuan Ibu tentang Gizi dan Asupan Makan Balita dengan Status Gizi Balita (BB/U) Usia 12-24 Bulan. Amerta Nutrition, 1(4), 369–378. https://doi.org/10.20473/amnt.v1.i4.2017.369-378

Queensland Health UNSW. (2011). Centre for Primary Health Care and Equity Research that makes a difference Housing density and health : A review of the literature and health impact assessments. UNSW Research Centre for Primary Helath Care and Equity, (August).

Ramli, Agho, K. E., Inder, K. J., Bowe, S. J., Jacobs, J., & Dibley, M. J. (2009). Prevalence and risk factors for stunting and severe stunting among under-fives in North Maluku province of Indonesia. BMC Pediatrics, 9, 64. https://doi.org/10.1186/1471-2431-9-64

Razali, M., & Wandi, R. (2019). Inverse Distance Weight Spatial Interpolation for Topographic Surface 3D Modelling. TECHSI - Jurnal Teknik Informatika, 11(3), 385. https://doi.org/10.29103/techsi.v11i3.1934

Rouse, J. W., Haas, R. H., Scheel, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. 3rd Earth Resource Technology Satellite Symposium, 1, 309–317. https://doi.org/19740022614

Saputri, R. A., & Tumangger, J. (2019). Hulu-Hilir Penanggulangan Stunting Di Indonesia. Journal of Political Issues, 1(1), 1–9. https://doi.org/10.33019/jpi.v1i1.2

Seyedmohammadi, J., Esmaeelnejad, L., & Shabanpour, M. (2016). Spatial variation modelling of groundwater electrical conductivity using geostatistics and GIS. Modeling Earth Systems and Environment, 2(4), 1–10. https://doi.org/10.1007/s40808-016-0226-3

Shaft, I., Ahmad, J., Shah, S. I., & Kashif, F. M. (2006). Impact of varying neurons and hidden layers in neural network architecture for a time frequency application. 10th IEEE International Multitopic Conference 2006, INMIC, (January), 188–193. https://doi.org/10.1109/INMIC.2006.358160

Suprayogi, I., Trimaijon, & Mahyudin. (2014). Model Prediksi Liku Kalibrasi Menggunakan Pendekatan Jaringan Saraf Tiruan (ZST) (Studi Kasus : Sub DAS Siak Hulu). Jurnal Online Mahasiswa Fakultas Teknik Universitas Riau, 1(1), 1–18.

Sutarto, Sri Agustina, Kinanti Rahmadhita, Susianti, & Roro Rukmi Windi Perdani. (2021). Relationship Between Low Born Weight (Lbw) And Stunting Events In Children (Age 24-59 Months). Indonesian Journal of Medical Anthropology, 2(1), 31–35. https://doi.org/10.32734/ijma.v2i1.4696

United Nations. (2019). Sustainable Development Goal 15-Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.

Uzair, M., & Jamil, N. (2020). Effects of Hidden Layers on the Efficiency of Neural networks. Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020, 1–6. https://doi.org/10.1109/INMIC50486.2020.9318195

WHO. (2007). Weight for age birth: WHO Child Growth Standards. In World Health Organization. Geneva.

Yadika, A. D. N., Berawi, K. N., & Nasution, S. H. (2019). Pengaruh Stunting terhadap Perkembangan Kognitif dan Prestasi Belajar. Jurnal Majority, 8(2), 273–282.

Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/https://doi.org/10.1080/01431160210144570



DOI: https://doi.org/10.22146/mgi.70474

Article Metrics

Abstract views : 6715 | views : 6197

Refbacks

  • There are currently no refbacks.




Copyright (c) 2023 Mochamad Firman Ghazali, Araneta Aqzela, Christas Gracia, Raudya Santy Febriningtyas, Dewi Wijayanti

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


 

Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 164/E/KPT/2021

Volume 35 No 2 the Year 2021 for Volume 39 No 1 the Year 2025

ISSN  0215-1790 (print) ISSN 2540-945X  (online)

 

website statistics Statistik MGI