Geomatics and Environmental Engineering, vol. 18, no. 1

Issue

Vol. 18 No. 1 (2024): Geomatics and Environmental Engineering

Date Log

Submitted
Jul 3, 2023
Accepted
Dec 10, 2023
Published
Jan 16, 2024
Creative Commons License

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

Support Vector Machine for Susceptibility Modeling of Dengue Fever in Kendari, Southeast Sulawesi

https://doi.org/10.7494/geom.2024.18.1.29
Prima Widayani
Universitas Gadjah Mada, Faculty of Geography, Yogyakarta, Indonesia
https://orcid.org/0000-0003-0891-2074
Abhista Fawwaz Sahitya
Universitas Gadjah Mada, Faculty of Geography, Yogyakarta, Indonesia.
Agatha Andriantari Saputri
Universitas Gadjah Mada, Faculty of Geography, Yogyakarta, Indonesia

Corresponding Author(s) : Prima Widayani

primawidayani@ugm.ac.id

Geomatics and Environmental Engineering, Vol. 18 No. 1 (2024): Geomatics and Environmental Engineering

Abstract

Dengue fever (DF) is an infectious disease that is still a problem in Indonesia. The total death rate due to DF was 705 people in 2021; in 2022, this increased to 1183 (Indonesian Ministry of Health, 2023). Seeing this fact, prevention efforts are still needed when handling DF cases in all of the regions of Indonesia. This research was conducted in the Kendari area of Southeast Sulawesi, where there are still cases of DF. The purpose of this study was to create a spatial model of dengue susceptibility using a support vector machine. Landsat 8 imagery was used to intercept data on building density, vegetation density, land use, and land surface temperatures. Rainfall and humidity variables were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG). Based on the modeling results, the districts of Wua-wua, Kadia, Barunga, Poasi, and Puuwatu are areas with high susceptibility. The results of testing the susceptibility model to dengue hemorrhagic fever (DHF) in Kendari obtained an area under the curve (AUC) of 0.75, meaning that this model was well-accepted.

Keywords

dengue infectious disease spatial model support vector machine susceptibility
Widayani, P., Sahitya, A. F., & Saputri, A. A. (2024). Support Vector Machine for Susceptibility Modeling of Dengue Fever in Kendari, Southeast Sulawesi. Geomatics and Environmental Engineering, 18(1), 29–50. https://doi.org/10.7494/geom.2024.18.1.29
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Hewa S.: Theories of disease causation: Social epidemiology and epidemiological transition. Galle Medical Journal, vol. 20(2), 2015, pp. 26–32. https://doi.org/10.4038/gmj.v20i2.7936.
  2. World Health Organization: Dengue and severe dengue. March 17, 2023. https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue [access: 11.02.2023].
  3. Kementerian Kesehatan Republik Indonesia: Wolbachia, Inovasi Baru Cegah Penyebaran DBD. July 22, 2022. https://sehatnegeriku.kemkes.go.id/baca/umum/20220722/3340692/wolbachia-inovasi-baru-cegah-penyebaran-dbd/ [access: 11.02.2023].
  4. Pusat Krisis Kesehatan Kementrian Kesehatan RI: Langkah Pencegahan Demam Berdarah Dengue, March 8, 2022. https://penanggulangankrisis.kemkes.go.id/4-langkah-pencegahan-demam-berdarah-dengue [access: 11.02.2023].
  5. Indonesia, Pemerintah Pusat: Undang-Undang Nomor 24 Tahun 2007 Tentang Nomor 66, Sekeretariat Negara, Jakarta 2007. https://peraturan.bpk.go.id/Details/39901/uu-no-24-tahun-2007.
  6. Skidmore A.K.: Environmental Modelling with GIS and Remote Sensing. Taylor & Francis, London 2002.
  7. Nordin N.I., Sobri N.M., Ismail N.A., Zulkifli S.N., Razak N.F.A., Mahmud M.: The classification performance using support vector machine for endemic dengue cases. Journal of Physics: Conference Series, vol. 1496, 012006. http://doi.org/10.1088/1742-6596/1496/1/012006.
  8. Hamdani H., Hatta H.R., Puspitasari N., Septiarini A., Henderi H.: Dengue classification method using support vector machines and cross-validation techniques. IAES International Journal of Artificial Intelligence, vol. 11(3), 2022, pp. 1119–1129. http://doi.org/10.11591/ijai.v11.i3.pp1119-1129.
  9. Mountrakis G., Im J., Ogole C.: Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66(3), 2011, pp. 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001.
  10. Erbek F.S., Özkan C., Taberner M.: Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International Journal of Remote Sensing, vol. 25(9), 2022, pp. 1733–1748. https://doi.org/10.1080/0143116031000150077.
  11. Liang F., Zhang X., Li H., Yu H., Lin Q., Jiang M., Zhang J.: Land use classification based on maximum likelihood method. [in:] Pan J.S., Balas V.E., Chen C.M. (eds.), Advances in Intelligent Data Analysis and Applications, Smart Innovation, Systems and Technologies, vol. 253, Springer, Singapore 2022, pp. 133–139. https://doi.org/10.1007/978-981-16-5036-9_15.
  12. Danoedoro P.: Pengantar Penginderaan Jauh Digital. Penerbit Andi, Yogyakarta 2012.
  13. Chakraborty A., Sachdeva K., Joshi P.K.: A Reflection on Image Classifications for Forest Ecology Management: Towards Landscape Mapping and Monitoring. [in:] Samui P., Sekhar S., Balas V.E. (eds.), Handbook of Neural Computation, Elsevier, Amsterdam 2017, pp. 67–85. https://doi.org/10.1016/B978-0-12-811318-9.00004-1.
  14. Badan Standardisasi Nasional: Klasifikasi penutup lahan – Bagian 1: Skala kecil dan menengah (SNI 7645-1:2014). BSN, Jakarta 2014. https://202.4.179.213/uploadsfile/sni-7645-1-2014.pdf.
  15. Ganie M.A., Nusrath A.: Determining the vegetation indices (NDVI) from Landsat 8 satellite data. International Journal of Advanced Research, vol. 4(8), 2016, pp. 1459–1463. https://doi.org/10.21474/ijar01/1348.
  16. Bouhennache R., Bouden T., Taleb-Ahmed A., Cheddad A.: A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery. Geocarto International, vol. 34(14), 2018, pp. 1531–1551. https://doi.org/10.1080/10106049.2018.1497094.
  17. Landsat Missions: Landsat 8 (L8) Data Users Handbook. Department of the Interior U.S. Geological Survey, Reston 2018.
  18. Coll C., Galve J.M., Sanchez J.M., Caselles V.: Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing, vol. 48(1), 2010, pp. 547–555. https://doi.org/10.1109/TGRS.2009.2024934.
  19. Valor E., Caselles V.: Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Remote Sensing of Environment, vol. 57(3), 1996, pp. 167–184. https://doi.org/10.1016/0034-4257(96)00039-9.
  20. Kurniadi H., Aprilia E., Utomo J.B., Kurniawan A., Safril A.: Perbandingan METODE IDW Dan Spline dalam Interpolasi Data Curah Hujan (Studi Kasus Curah Hujan Bulanan Di Jawa Timur Periode 2012–2016). Prosiding Seminar Nasional GEOTIK 2018, pp. 213–220.
  21. Motevalli A., Pourghasemi H.R., Zabihi M.: Assessment of GIS-based Machine Learning Algorithms for Spatial Modeling of Landslide Susceptibility: Case Study in Iran. [in:] Huang B. (ed.), Comprehensive Geographic Information Systems, Elsevier, Amsterdam 2018, pp. 258–280. https://doi.org/10.1016/B978-0-12-409548-9.10461-0.
  22. Cho M.Y., Hoang T.T.: Feature selection and parameters optimization SVM using particle swarm optimization for fault classification in power distribution systems. Computational Intelligence and Neuroscience, vol. 2017(3), 2017, 4135465. https://doi.org/10.1155/2017/4135465.
  23. Scavuzzo J.M., Trucco F., Espinosa M., Tauro C.B., Abril M., Scavuzzo C.M., Frery A.C.: Modeling dengue vector population using remotely sensed data and machine learning. Acta Tropica, vol. 185, 2018, pp. 167–175. https://doi.org/10.1016/j.actatropica.2018.05.003.
  24. Louis V.R., Phalkey R., Horstick O., Ratanawong P., Smith A.W., Tozan Y., Dambach P.: Modeling tools for dengue risk mapping – a systematic review. International Journal of Health Geographics, vol. 13, 2014, 50. https:// doi.org/10.1186/1476-072X-13-50.
  25. Widayani P., Yanuar S.R., Yogi H.A.: Relationship analysis of environmental factor change on the evidence of dengue fever diseases using image transformation (case study: Surakarta City). IOP Conference Series: Earth and Environmental Science, vol. 169, 2018, 012061. https://doi.org/10.1088/1755-1315/169/1/012061.
  26. Palaniyandi M.: The environmental aspects of dengue and chikungunya outbreaks in India: GIS for epidemic control. International Journal of Mosquito Research, vol. 1(2), 2014, pp. 35–40.
  27. Tjaden N.B., Caminade C., Beierkuhnlein C., Margarete S.T.: Mosquito borne diseases: Advances in modelling climate-change impacts. Trends in Parasitology, vol. 34(3), 2018, pp. 227–245. https://doi.org/10.1016/j.pt.2017.11.006.
  28. Yin S., Ren C., Shi Y., Hua J., Yuan H.-Y., Tian L.-W.A.: A systematic review on modeling methods and influential factors for mapping dengue-related risk in urban settings. International Journal of Environmental Research and Public Health, vol. 19(22), 2022, 15625. https://doi.org/10.3390/ijerph192215265.
  29. Davis C., Murphy A.K., Bambrick H., Devine G., Frentiu F., Yakob L., Huang X., Li Z., Yang W., Williams G., Hu W.: A regional suitable conditions index to forecast the impact of climate change on dengue vectorial capacity. Environmental Research, vol. 195, 2021, 110849. https://doi.org/10.1016/j.envres.2021.110849.
  30. Dickin S.K., Wallace C.J.: Assessing changing vulnerability to dengue in northeastern Brazil using a water-associated disease index approach. Global Environmental Change, vol. 29, 2014, pp. 155–164. https://doi.org/10.1016/j.gloenvcha.2014.09.007.
  31. Costa E.A.P.A., Santos E.M.M., Correia J.C., Albuquerque C.M.R.: Impact of small variations in temperature and humidity on the reproductive activity and survival of Aedes aegypti (Diptera, Culicidae). Medical and Veterinary Entomology, vol. 54(3), 2010, pp. 488–493. https://doi.org/10.1590/S0085-56262010000300021.
  32. Kesetyaningsih T.W., Andarini S., Sudarto, Pramoedyo H.: Correlation between Larvae Free Number with DHF Incidence in Sleman, Yogyakarta, Indonesia. [in:] The 2nd International Conference of Medical Health & Health Sciences and the Life Sciences Conference 2016. http://repository.umy.ac.id/handle/123456789/12653.
  33. Yudhastuti R., Satyabakti P., Basuki H.: Climate conditions, larvae free number, DHF incidence in Surabaya Indonesia. Journal of US-China Public Administration, vol. 10(11), 2013, pp. 1043–1049.
  34. Valdez L.D., Sibona G.J., Condat C.A.: Impact of rainfall on Aedes aegypti populations. Ecological Modelling, vol. 385, 2018, pp. 96–105. https://doi.org/10.1016/j.ecolmodel.2018.07.003.
  35. Iriani Y.: Hubungan antara Curah Hujan dan Peningkatan Kasus Demam Berdarah Dengue Anak di Kota Palembang. Sari Pediatri, vol. 13(6), 2012, pp. 378–383. https://doi.org/10.14238/sp13.6.2012.378-83.
  36. Mukhopadhyay A.K., Tamizharasu W., Satya Babu P., Chandra G., Hati A.K.: Effect of common salt on laboratory reared immature stages of Aedes aegypti (L). Asian Pacific Journal of Tropical Medicine, vol. 3(3), 2010, pp. 173–175. https://doi.org/10.1016/S1995-7645(10)60002-8.
  37. Siddiq A., Shukla N., Pradhan B.: Predicting dengue fever transmission using machine learning methods. [in:] IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2021, Singapore, December 13–16, 2021. IEEE 2021, IEEE, Piscataway 2021, pp. 21–26. https://doi.org/10.1109/IEEM50564.2021.9672977.
  38. Mizan R.A., Widayani P., Farda N.M.: Assessment and comparison of machine learning algorithm capability in spatial modeling of dengue fever vulnerability based on Landsat Image 8 Oli/Tirs. Jurnal Geografi, vol. 13(2), 2021, pp. 211–224. https://doi.org/10.24114/jg.v13i2.21019.
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 461 Download : 234

Downloads

Download data is not yet available.