Fuzzy Time Series Saxena Easo on Forecasting the Number of Dengue Hemorrhagic Fever Cases in Bengkulu Province
DOI:
https://doi.org/10.20956/j.v22i1.45365Keywords:
Dengue Hemorrhagic Fever (DHF), Kota Bengkulu, Forecasting, Fuzzy Time SeriesAbstract
Dengue Hemorrhagic Fever (DHF) is a viral infectious disease that is transmitted through mosquitoes and is a public health problem in the world. Untreated Dengue Hemorrhagic Fever (DHF) can trigger extraordinary events (KLB), severe dengue and even cause death. Tropical countries like Indonesia cause cases of Dengue Hemorrhagic Fever (DHF) to be found in almost all provinces. This disease is endemic, especially in Bengkulu Province. The increase and decrease in the number of cases can also be seen using forecasting methods. The time series forecasting method used in this research is the Saxena-Easo fuzzy time series. Where this method is a modification and refinement of the previous method, namely the Stevenson & Porter fuzzy time series method. The Stevenson & Porter method changes the actual data into percentage change which was then developed by Saxena Easo with modifications to the number of data intervals. The data developed by Saxena Easo is dividing the number of intervals into sub-intervals based on the number of frequencies. This research aims to predict the number of Dengue Hemorrhagic Fever cases in Bengkulu Province from the 1st week of 2022 to the 24th week of 2024, and compare it with existing data on the number of cases. Based on the research, the results of the accuracy level of the Mean Absolute Percentage Error (MAPE) in forecasting the number of cases of Dengue Hemorrhagic Fever in Bengkulu Province using the FTS Saxena Easo method obtained a value of 0.359637893 or 3.5%, so the forecast accuracy value obtained is included in the criteria for very good forecasting, because it has an accuracy value below 10%. It can be said that the comparison between actual data and forecasts is not much different.
References
[1] Denis, R., 2023. Tingkat Kerawanan Demam Berdarah Dengue Berdasarkan Indeks Kerawanan Penyakit di Kabupaten Kepahiang Provinsi Bengkulu. Jurnal Vokasi Kesehatan (JUVOKES), Vol. 2, No. 1, 23-32,.
[2] Hikmah, M. & Kasmini, 2015. Faktor yang Berhubungan dengan Kejadian Kematian Akibat Demam Berdarah Dengue. Unnes Journal of Public Health, Vol. 4, No. 4, 180-189.
[3] Kementerian Kesehatan RI. Demam Berdarah Dengue. [Online]. Available: https://ayosehat.kemkes.go.id/topik/demam-berdarah-dengue. [Diakses 17 Februari 2024].
[4] Kementerian Kesehatan RI, 2022. Laporan Tahunan Demam Berdarah Dengue. Jakarta.
[5] Makridakis, S., & McGee, V.E., 1995. Metode dan Aplikasi Peramalan. Jakarta: Erlangga.
[6] Makridakis, S., Wheelwright, S. C., & McGee, V. E., 1999. Metode dan Aplikasi Peramalan. Jakarta: Erlangga.
[7] Rahmadhani, L.C., Anggraeni, D., & Kamsyakawuni, 2019. A. Fuzzy Time Series Saxena Easo pada Peramalan Laju Inflasi Indonesia. Jurnal Matematika Unej, Vol. 20, 53-60.
[8] Saxena, P., & Easo, S., 2012. A New Method for Forecasting Enrollments Based on Fuzzy Time Series with Higher Forecast Accuracy Rate. International Journal of Computer Technology & Applications.Vol. 3, 2033-2037.
[9] Setiabudi & Djatnika, 2019. Memahami Demam Berdarah Dengue Part 1. Infeksi dan Penyakit Tropis, Vol. 1, 155-181.
[10] Stevenson, M., & Porter, J. E., 2009. Fuzzy Time Series Forecasting Using Percentage Change as The Universe of Discourse. Proceedings of World Academy Science, Engineering and Technology, Vol. 55, 154-157
[11] Yohan, B., 2018. Demam Berdarah Dengue: Problematika Interasi Virus, Pejamu, Vektor. Ejikman Institute for Molecular Biology, Vol. 1, 1-2.
[12] Witono, Tursina, & Anggi, S. S., 2022. Perbandingan Model Saxena Easo dan Model Chen Hsu pada Fuzzy Time Series untuk Prediksi Harga Emas. Jurnal Sistem dan Teknologi Informasi, Vol. 10, 403-410.
[13] World Health Organization, 2012 Global Strategy for Dengue Prevention and Control, WHO Press.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal Matematika, Statistika dan Komputasi

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

This work is licensed under a Creative Commons Attribution 4.0 International License.
Jurnal Matematika, Statistika dan Komputasi is an Open Access journal, all articles are distributed under the terms of the Creative Commons Attribution License, allowing third parties to copy and redistribute the material in any medium or format, transform, and build upon the material, provided the original work is properly cited and states its license. This license allows authors and readers to use all articles, data sets, graphics and appendices in data mining applications, search engines, web sites, blogs and other platforms by providing appropriate reference.




