Pemetaan Penyebaran Tingkat Kepatuhan Masyarakat dalam Menggunakan Masker di Pasar Tradisional Kota Surabaya dengan Metode Hot Spot Analysis (Getis-Ord Gi*)
Abstract
According to a study conducted to see the level of effectiveness for using masks in case study by [1]), explains that if the use of masks has a positive impact on resulting transmission. Research conducted invloving cloth masks, surgical masks and Filtering Face Piece 2 (FFFP2) masks, with output results that the three types of masks show stable resuts to protect all the time and doesn’t depend on persons activities. At this moment, CoronaVirus Disease 2019 (COVID-19) is one of the disease that causes global pandemic and creates several new clusters, especially traditional market clusters. Traditional market is one of the driving wheels that can pursue society economy. In that case, there needs to be serious attention in taking self care by using protective equipment at least a mask. This mapping of the distribution level people for using masks combines many aspect such as map visualization, website information system and image processing for masks detection. The use of image processing plays an important role in the mapping system, that’s because the processing of manually counting people who are not using masks will take a lot of labor in its implementation. Image processing used is face mask detector and people counter with 82% average accuracy at the implementation process. The hot spot analysis mapping method cannot be used in static data types such as traditional market, because it will gave the same result in the same density of market location from day to day. The most exact method for the data type that compares the values of not using mask to the entire traditional market is Inverse Distance Weighted (IDW) Interpolated Method. Testing results using the new method show that there is a change for highest or lowest not using masks by people at the traditional market. This method only calculates the value and not calculate distance between each market.References
[1] van der Sande M, Teunis P, Sabel R (2008) Professional and
Home-Made Face Masks Reduce Exposure to Respiratory
Infections among the General Population. PLoS ONE 3(7):
e2618. https://doi.org/10.1371/journal.pone.0002618
[2] BBC News Indonesia. (2020, Juni 18). Covid-19 Indonesia
dan klaster pasar tradisional: Antara keselamatan dan
tuntutan perut, ‘kalau nggak jualan, mau makan apa’ kata
pedagang. Diambil kembali dari BBC News Indonesia:
https://www.bbc.com/indonesia/indonesia-53094297
[3] Kompas.com. (2020, September 24). Perkantoran Masih Jadi
Salah Satu Klaster Tertinggi Penyebaran Covid-19 di Jakarta.
Diambil kembali dari Kompas.com:
https://megapolitan.kompas.com/read/2020/09/24/11141021/
perkantoran-masih-jadi-salah-satu-klaster-tertinggipenyebaran-covid-19
[4] detikNews. (2020, Oktober 6). Klaster Keluarga
Bermunculan, Ibu Jadi Kunci Pencegah COVID-19 . Diambil
kembali dari detikNews.com: https://news.detik.com/berita/d5200977/klaster-keluarga-bermunculan-ibu-jadi-kuncipencegah-covid-19
[5] Kementerian Kesehatan republik Indonesia. (2020, Agustus
30). Kampanye Nasional Disiplin Pakai Masker. Diambil
kembali dari Kementerian Kesehatan republik Indonesia:
https://www.kemkes.go.id/article/print/20083000003/kampa
nye-nasional-disiplin-pakai-masker.html
[6] Republika.co.id. (2020, Agustus 11). Presiden: 70%
Masyarakat Belum Pakai Masker! Retrieved from
ayotasik.com:
https://www.ayotasik.com/read/2020/08/11/6059/presiden70-masyarakat-belum-pakai-masker
[7] Kriegel, H.-P., Kröger, P., Sander, J. and Zimek, A. (2011),
Density-based clustering. WIREs Data Mining Knowl Discov,
1: 231-240. https://doi.org/10.1002/widm.30
[8] MacQueen, J. (1967). Some methods for classification and
analysis of multivariate observations. Proc. 5th Berkeley
Symp. Math. Statistics and Probability., 281-297.
[9] Nielsen, F. (2016). Introduction to HPC with MPI for Data
Science. Hierarchical Clustering, 222-239.[10] Getis, A., & Ord, J. (1992). The Analysis of Spatial
Association by Use of Distance Statistics. Geographical
Analysis,Vol.24,No.3 (July 1992).
[11] Public Health Columbia. (2020, Oktober 12). Hot Spot Spatial
Analysis. Diambil kembali dari publichealthcolumbia:
https://www.publichealth.columbia.edu/research/populationhealth-methods/hot-spot-spatial-analysis
[12] Ursullia, D. S. (2018). Analisis Spasial Persebaran Penderita
HIV serta Lokasi dan Lokalisasi Prostitusi di Kota Sorong
Tahun 2016. Surakarta: Fakultas Geografi Universitas
Muhammadiyah Surakarta. URI :
http://eprints.ums.ac.id/id/eprint/65620
[13] Pemerintah Kota Surabaya. (2020, Oktober 28). Peta Sebaran
Pasien. Diambil kembali dari Surabaya Lawan COVID-19:
https://lawancovid-19.surabaya.go.id/visualisasi/sebaran
[14] Kurniawan, D. R., Susetyo, B., & Hermawan, E. (2019).
Analisis Spasial K-Means Clustering Sebaran Keluhan
Pelanggan PDAM Tirta Pakuan Berbasis Webgis. SEMNATI
2019, 119-131.
[15] Kurniawan, A., & Sadali, M. I. (2016). Pemanfaatan Analisis
Spasial Hot Spot (Getis Ord Gi*) untuk Pemetaan Klaster
Industri di Pulau Jawa dengan Memanfaatkan Sistem
Informasi Geografi. Hibah Penelitian Dosen Sekolah Vokasi
Universitas Gadjah Mada.
[16] Visa, Sofia & Ramsay, Brian & Ralescu, Anca & Knaap,
Esther. (2011). Confusion Matrix-based Feature Selection..
CEUR Workshop Proceedings. 710. 120-127.
[17] Jurafsky, D. and Martin, J. 2019. Naive bayes and sentiment
classification. Speech and Language Processing. 1–21.
[18] Pasaribu, J. M., & Haryani, N. S. (2012). Perbandingan Teknik
Interpolasi DEM SRTM dengan Metode Inverse Distance
Weighted(IDW), Natural Neighbor dan SPLINE. Jurnal
Penginderaan Jauh Vol. 9 No. 2 , 126-132. Diambil kembali
http://jurnal.lapan.go.id/index.php/jurnal_inderaja/article/vie
wFile/1787/1621.
[19] Gonzales, A. R., Schofield, R. B., & Schofield, R. B. (2005).
Mapping Crime: Understanding Hot Spots . Washington, DC:
U.S. Department of Justice Office of Justice Programs
