Distribution Model of Personal Protective Equipment (PPE) Using the Spatial Dominance Test and Decision Tree Algorithm

Vega Purwayoga - Siliwangi University, Tasikamalaya, Indonesia
Siti Yuliyanti - Siliwangi University, Tasikamalaya, Indonesia
Andi Nurkholis - Teknokrat Indonesia University, Lampung, Indonesia
Harry Gunawan - Universitas Muhammadiyah Cirebon, Cirebon, Indonesia
Sokid Sokid - Universitas Muhammadiyah Cirebon, Cirebon, Indonesia
Nuri Kartini - Universitas Muhammadiyah Cirebon, Cirebon, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3.2471

Abstract


The COVID-19 case has developed positively, but preventive measures must be taken to anticipate SARS-CoV-2 mutations. Anticipation can include policies, preparing health workers, and providing personal protective equipment. Personal Protective Equipment (PPE) availability is a big challenge in handling pandemics, especially COVID-19. The level of need for PPE in an area depends on the number of COVID-19 cases. This research provides a solution to overcome the availability of PPE by applying the concept of cross-regional collaboration. Areas with low COVID-19 case rates can help areas with high COVID-19 case rates by sending PPE assistance. Implementing the cross-regional collaboration concept is assisted by the spatial dominance test algorithm, namely the spatial skyline query. Spatial Skyline Query works by searching for the most ideal area. The ideal area is an area with low COVID-19 case criteria. The low number of positive cases, death cases, probable cases, and close contact cases supports the low number of COVID-19 cases. Areas with the highest number of recovered cases are also priorities. The SSQ model was developed into two models for searching priority areas for PPE assistants. The first model is Sort Filter Skyline 1 (SFS1), and the second is Sort Filter Skyline 2 (SFS2). SFS1 is a form of SFS algorithm optimization that searches for the best 50% of all regions. SFS2 modifies SFS1 by selecting areas whose distance is <= the average distance of the area to the Health Crisis Centre of the Ministry of Health of the Republic of Indonesia. This research involves searching for priority areas and applying a prediction algorithm to extract knowledge built from the prediction model. The algorithm used is C5.0. The data used to apply the prediction algorithm results from the application of SFS1 and SFS2. The results of testing the prediction model by the C5.0 algorithm produced an accuracy of 77.26% for SFS1 data and 92.01% for SFS2. The average rules resulting from the C5.0 algorithm are three for SFS1 and two for SFS2.



Keywords


C5.0; COVID-19; Cross-regional Collaboration; Distribution; Personal Protective Equpment; Spatial Skyline Query

Full Text:

PDF

References


Q. Aini, R. R. Fauzi, and E. Khudzaeva, “Economic Impact due Covid-19 Pandemic: Sentiment Analysis on Twitter Using Naïve Bayes Classifier and Support Vector Machine,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, pp. 733–741, Sep. 2023, doi: 10.30630/joiv.7.3.1474.

V. Purwayoga, “Modified skyline query to measure priority region for personal protective equipment recipient of COVID-19 health workers,” Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 3, pp. 167–173, Jul. 2021, doi: 10.14710/jtsiskom.2021.14003.

M. A. Shereen, S. Khan, A. Kazmi, N. Bashir, and R. Siddique, “COVID-19 infection: Emergence, transmission, and characteristics of human coronaviruses,” J Adv Res, vol. 24, pp. 91–98, Jul. 2020, doi: 10.1016/j.jare.2020.03.005.

N. Novarisa, H. Helda, and R. Mulyadi, “Indonesia’s COVID-19 Trend After the End of a Public Health Emergency of International Concern: Preparation for an Endemic,” Kesmas: Jurnal Kesehatan Masyarakat Nasional, vol. 18, no. sp1, p. 25, Sep. 2023, doi: 10.21109/kesmas.v18isp1.7018.

Y. Liu et al., “Aerodynamic analysis of SARS-CoV-2 in two Wuhan hospitals,” Nature, vol. 582, no. 7813, pp. 557–560, Jun. 2020, doi: 10.1038/s41586-020-2271-3.

D. Ramakrishnan, “COVID-19 and Face Masks – To Use or Not to Use!,” Indian J Community Health, vol. 32, no. 2 (Supp), pp. 240–243, Apr. 2020, doi: 10.47203/IJCH.2020.v32i02SUPP.012.

I. Tubert-Brohman, W. Sherman, M. Repasky, and T. Beuming, “Improved Docking of Polypeptides with Glide,” J Chem Inf Model, vol. 53, no. 7, pp. 1689–1699, Jul. 2013, doi: 10.1021/ci400128m.

T. M. Cook, “Personal protective equipment during the coronavirus disease (COVID) 2019 pandemic – a narrative review,” Anaesthesia, vol. 75, no. 7, pp. 920–927, Jul. 2020, doi: 10.1111/anae.15071.

M. S. Sinha, F. T. Bourgeois, and P. K. Sorger, “Personal Protective Equipment for COVID-19: Distributed Fabrication and Additive Manufacturing,” Am J Public Health, vol. 110, no. 8, pp. 1162–1164, Aug. 2020, doi: 10.2105/AJPH.2020.305753.

E. Furman et al., “Prediction of personal protective equipment use in hospitals during COVID-19,” Health Care Manag Sci, vol. 24, no. 2, pp. 439–453, Jun. 2021, doi: 10.1007/s10729-021-09561-5.

S. Munir and M. Asqia, “Implementasi Skyline Query pada Sistem Rekomendasi Pemilihan Tempat Kuliner di Kota Depok, Bogor, dan Tangerang,” Jurnal Teknologi Terpadu, vol. 7, no. 2, pp. 113–119, Dec. 2021, doi: 10.54914/jtt.v7i2.440.

V. Purwayoga and B. Susanto, “Rekomendasi Daerah Penyalur Tenaga Kesehatan Covid-19 Dengan Menggunakan Skyline Query,” Fountain of Informatics Journal, vol. 7, no. 1, p. 22, Oct. 2021, doi: 10.21111/fij.v7i1.5720.

V. Purwayoga, M. Al Husaini, and H. H. Lukmana, “Visualisasi Skyline Query untuk Distribusi Tenaga Kesehatan COVID-19,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 9, no. 1, Apr. 2023, doi: 10.28932/jutisi.v9i1.5624.

C. B. Biddell et al., “Cross-sector decision landscape in response to COVID-19: A qualitative network mapping analysis of North Carolina decision-makers,” Front Public Health, vol. 10, Aug. 2022, doi: 10.3389/fpubh.2022.906602.

I. Falagara Sigala, M. Sirenko, T. Comes, and G. Kovács, “Mitigating personal protective equipment (PPE) supply chain disruptions in pandemics – a system dynamics approach,” International Journal of Operations & Production Management, vol. 42, no. 13, pp. 128–154, Dec. 2022, doi: 10.1108/IJOPM-09-2021-0608.

G. Baloch, F. Gzara, and S. Elhedhli, “Covid-19 PPE distribution planning with demand priorities and supply uncertainties,” Comput Oper Res, vol. 146, p. 105913, Oct. 2022, doi: 10.1016/j.cor.2022.105913.

A. T. C. Onstein, M. Ektesaby, J. Rezaei, L. A. Tavasszy, and D. A. van Damme, “Importance of factors driving firms’ decisions on spatial distribution structures,” International Journal of Logistics Research and Applications, vol. 23, no. 1, pp. 24–43, Jan. 2020, doi: 10.1080/13675567.2019.1574729.

A. Annisa and S. Khairina, “Location Selection Based on Surrounding Facilities in Google Maps using Sort Filter Skyline Algorithm,” Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika, vol. 7, no. 2, pp. 65–72, Jul. 2021, doi: 10.23917/khif.v7i2.12939.

S. Dwiasnati and Y. Devianto, “Classification of Flood Disaster Predictions using the C5.0 and SVM Algorithms based on Flood Disaster Prone Areas,” International Journal of Computer Trends & Technology, vol. 67, no. 07, pp. 49–53, Jul. 2019, doi: 10.14445/22312803/IJCTT-V67I7P107.

I. Z. P. Hamdan, M. Othman, Y. M. Mohmad Hassim, S. Marjudi, and M. Mohd Yusof, “Customer Loyalty Prediction for Hotel Industry Using Machine Learning Approach,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, pp. 695–703, Sep. 2023, doi: 10.30630/joiv.7.3.1335.

A. Nurkholis, Styawati, V. Purwayoga, H. H. Lukmana, A. Prihandono, and W. Koswara, “Analysis of Weather Data for Rainfall Prediction using C5.0 Decision Tree Algorithm,” in 2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), IEEE, Dec. 2022, pp. 551–555. doi: 10.1109/ISMODE56940.2022.10180907.

R. I. Komaraasih, I. S. Sitanggang, A. Annisa, and M. A. Agmalaro, “Sentinel-1A image classification for identification of garlic plants using decision tree and convolutional neural network,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 11, no. 4, p. 1323, Dec. 2022, doi: 10.11591/ijai.v11.i4.pp1323-1332.

B. R. Devi, K. Nageswara Rao, S. P. Setty, M. N. Rao, and A. Prof, “Disaster Prediction System Using IBM SPSS Data Mining Tool,” International Journal of Engineering Trends and Technology (IJETT), [Online]. Available: http://www.ijettjournal.org

Z. Guo, Y. Shi, F. Huang, X. Fan, and J. Huang, “Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management,” Geoscience Frontiers, vol. 12, no. 6, p. 101249, Nov. 2021, doi: 10.1016/j.gsf.2021.101249.

R. Agramanisti Azdy and F. Darnis, “Use of Haversine Formula in Finding Distance Between Temporary Shelter and Waste End Processing Sites,” J Phys Conf Ser, vol. 1500, no. 1, p. 012104, Apr. 2020, doi: 10.1088/1742-6596/1500/1/012104.

E. Maria, E. Budiman, Haviluddin, and M. Taruk, “Measure distance locating nearest public facilities using Haversine and Euclidean Methods,” J Phys Conf Ser, vol. 1450, no. 1, p. 012080, Feb. 2020, doi: 10.1088/1742-6596/1450/1/012080.

- Istiadi et al., “Classification of Tempeh Maturity Using Decision Tree and Three Texture Features,” JOIV : International Journal on Informatics Visualization, vol. 6, no. 4, p. 883, Dec. 2022, doi: 10.30630/joiv.6.4.983.

F. Rahmad, Y. Suryanto, and K. Ramli, “Performance Comparison of Anti-Spam Technology Using Confusion Matrix Classification,” IOP Conf Ser Mater Sci Eng, vol. 879, no. 1, p. 012076, Jul. 2020, doi: 10.1088/1757-899X/879/1/012076.

J. Kludas et al., “Machine Learning of Protein Interactions in Fungal Secretory Pathways,” PLoS One, vol. 11, no. 7, p. e0159302, Jul. 2016, doi: 10.1371/journal.pone.0159302.

A. Nurkholis and I. S. Sitanggang, “A spatial analysis of soybean land suitability using spatial decision tree algorithm,” in Sixth International Symposium on LAPAN-IPB Satellite, T. D. Pham, K. D. Kanniah, K. Arai, G. J. P. Perez, Y. Setiawan, L. B. Prasetyo, and Y. Murayama, Eds., SPIE, Dec. 2019, p. 65. doi: 10.1117/12.2541555.

N. F. Muhamad Krishnan, Z. A. Zukarnain, A. Ahmad, and M. Jamaludin, “Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models,” JOIV : International Journal on Informatics Visualization, vol. 6, no. 3, p. 597, Sep. 2022, doi: 10.30630/joiv.6.2.788.

J. Liu et al., “Optimized Query Algorithms for Top- K Group Skyline,” Wirel Commun Mob Comput, vol. 2022, pp. 1–11, Jan. 2022, doi: 10.1155/2022/3404906.

A. Annisa and L. Angraeni, “Location Selection Query in Google Maps using Voronoi-based Spatial Skyline (VS2) Algorithm,” Jurnal Online Informatika, vol. 6, no. 1, p. 25, Jun. 2021, doi: 10.15575/join.v6i1.667.

F. Li, “Logistics Distribution Path Optimization Algorithm Based on Intelligent Management System,” Comput Intell Neurosci, vol. 2022, pp. 1–12, Sep. 2022, doi: 10.1155/2022/3699990.

F. Ren, Z. Tian, J. Pan, and Y. Chiu, “Cross-regional comparative study on energy efficiency evaluation in the Yangtze River Basin of China,” Environmental Science and Pollution Research, vol. 27, no. 27, pp. 34037–34051, Sep. 2020, doi: 10.1007/s11356-020-09439-z.

J. Li, J. Wang, H. Lee, and X. Zhao, “Cross-regional collaborative governance in the process of pollution industry transfer: The case of enclave parks in China,” J Environ Manage, vol. 330, p. 117113, Mar. 2023, doi: 10.1016/j.jenvman.2022.117113.

J. L. Delgado-Gallegos et al., “Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19,” Brain Sci, vol. 13, no. 3, p. 513, Mar. 2023, doi: 10.3390/brainsci13030513.