Prediction and Analysis of The Number of ARI Cases based on PM2.5 Concentration with Co-Kriging Approach

Nur Chamidah, Putu Eka Andriani, Marfa Audilla Fitri, Sofia Andika Nur Fajrina, Alda Fuadiyah Suryono, Victoria Anggia Alexandra

Abstract


Air quality significantly impacts global environmental health, influencing both human well-being and climate change. According to the World Health Organization (WHO), air pollution is one of the most substantial environmental threats to human health, with Indonesia experiencing particularly severe air quality issues. The World Air Quality Report ranks Indonesia 14th globally and 1st in Southeast Asia for poor air quality, with a notable increase in PM2.5 concentrations to 37.1 µg/m³ in 2023. Major sources of pollution include coal-fired power plants, motor vehicles, forest fires, and agricultural activities. In urban areas like Surabaya, PM2.5 levels have risen, contributing to high incidences of Acute Respiratory Infections (ARI). Spatial analysis reveals a correlation between PM2.5 levels and ARI cases, with spatial regression and co-kriging methods offering accurate estimation models. This study utilizes co-kriging, incorporating PM2.5 data from nine districts in Surabaya, to estimate ARI cases. The Exponential semivariogram model provided the most accurate predictions, with a MAPE value of 5.11%. The highest estimated ARI cases were in the Kenjeran district, highlighting the need for targeted interventions. Future research should expand observation points and consider additional influencing factors such as weather, population density, and socioeconomic conditions to enhance prediction accuracy and support effective public health strategies.

Keywords


Co-kriging; Spatial Data; Acute Respiratory Infection; PM2.5

Full Text:

PDF

References


M. R. P. Musa, A. B. Lesmana, R. N. Arthamevia, P. A. Pratama and N. Savitri, "Human Rights and Pancasila: A Case of Tionghoa Ethnic Discrimination in Indonesia," Indonesian Journal of Pancasila and Global Constitutionalism, vol. 1, no. 1, pp. 119-170, 2022. 

E. Kahya-Özyirmidokuz, "Analyzing unstructured Facebook social network data through web text mining: A study of online shopping firms in Turkey," Information Development, vol. 32, no. 1, pp. 70-80, 2016. 

H. Choi, M. Kim, G. Lee and W. Kim, "Unsupervised learning approach for network intrusion detection system using autoencoders," The Journal of Supercomputing, vol. 75, no. 9, pp. 5597-5621, 2019. 

S. Andleeb, R. Ahmed, Z. Ahmed and M. Kanwal, "Identification and classification of cybercrimes using text mining technique," In 2019 International Conference on Frontiers of Information Technology (FIT), pp. 227-232, 2019. 

I. Insiyah, M. Khasanah and T. P. Hendarsyah, "Penerapan Metode Ward Clustering Untuk Pengelompokkan Daerah Rawan Kriminalitas Di Jawa Timur Tahun 2021," Jurnal Statistika dan Komputasi, vol. 2, no. 1, pp. 44-54, 2023. 

H. D. Tampubolon, S. Suhada, M. Safii, S. Solikhun and D. Suhendro, "Penerapan Algoritma K-Means dan K-Medoids Clustering untuk Mengelompokkan Tindak Kriminalitas Berdasarkan Provinsi," Jurnal Ilmu Komputer dan Teknologi, vol. 2, no. 2, pp. 6-12, 2021. 

R. N. Fahmi, M. Jajuli and N. Sulistiyowati, "Analisis Pemetaan Tingkat Kriminalitas di Kabupaten Karawang Menggunakan Algoritma K-Means," INTECOMS: Journal of Information Technology and Computer Science, vol. 4, no. 1, pp. 67-79, 2021. 

A. D. Putra, G. S. Martha, M. Fikram and R. J. Yuhan, "Faktor-Faktor yang Memengaruhi Tingkat Kriminalitas di Indonesia Tahun 2018," Indonesian Journal of Applied Statistics, vol. 3, no. 2, pp. 123-131, 2021. 

A. O. Edwart and Z. Azhar, "Pengaruh Tingkat Pendidikan, Kepadatan Penduduk dan Ketimpangan Pendapatan Terhadap Kriminalitas di Indonesia.," Jurnal Kajian Ekonomi Dan Pembangunan, vol. 1, no. 3, pp. 759-768, 2019. 

R. Soesilo, KUHP Kitab Undang Undang Hukum Pidana Lengkap serta Komentarnya, Bogor: Politea, 1976. 

M. D. Wuryandari and I. Afrianto, "Perbandingan Metode Jaringan Syaraf Tiruan Backpropagation Dan Learning Vector Quantization Pada Pengenalan Wajah," Jurnal Komputer dan Informatika (Komputa), vol. 1, no. 1, pp. 45-51, 2012. 

S. Haykin, Neural Networks: A Comprehensive Foundation, New York: Macmillan, 1994. 

R. Feldman and J. Sanger, The text mining handbook: advanced approaches in analyzing unstructured data, Cambridge: Cambridge University Press, 2006. 

V. Gupta and G. S. Lehal, "A Survey of Text Mining Techniques and Applications," Journal of Emerging Technologies in Web Intelligence, vol. 1, p. 60–76, 2009. 

F. Nurhuda, S. W. Sihwi and A. Doewes, "Analisis sentimen masyarakat terhadap calon Presiden Indonesia 2014 berdasarkan opini dari Twitter menggunakan metode Naive Bayes Classifier," ITSmart: Jurnal Teknologi dan Informasi, vol. 2, no. 2, pp. 35-42, 2016. 

A. T. J. Harjanta, "Preprocessing Text untuk Meminimalisir Kata yang Tidak Berarti dalam Proses Text Mining," Jurnal Informatika UPGRIS, vol. 1, 2015. 

C. Triawati, M. A. Bijaksana, N. Indrawati and W. A. Saputro, "Pemodelan Berbasis Konsep untuk Kategorisasi Artikel Berita Berbahasa Indonesia," In Seminar Nasional Aplikasi Teknologi Informasi (SNATI), 2009. 

S. Bird, E. Klein and E. Loper, Natural language processing with Python: analyzing text with the natural language toolkit, O'Reilly Media, Inc, 2009. 

C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge: Cambridge University Press, 2008. 

H. Jiawei, K. Micheline and P. Jian, "Data Mining: Concepts and Techniques The Morgan Kaufmann Series in Data Management Systems," Elsevier, 2011. 

T. Schmiedel, O. Müller and J. Vom Brocke, "Topic modeling as a strategy of inquiry in organizational research: A tutorial with an application example on organizational culture," Organizational Research Methods, vol. 22, no. 4, pp. 941-968, 2019. 

H. Jelodar, Y. Wang, C. Yuan, X. Feng, X. Jiang, Y. Li and L. Zhao, "Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey," Multimedia tools and applications, vol. 78, pp. 15169-15211, 2019. 




DOI: http://dx.doi.org/10.12962/j27213862.v8i1.20512

Refbacks

  • There are currently no refbacks.




Creative Commons License
Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.

ISSN:  0216-308X

e-ISSN: 2721-3862

Web
Analytics Made Easy - StatCounter View My Stats