Prediksi Jumlah Kasus Penyakit di Jawa Timur Memanfaatkan Metode Simple Moving Average

Authors

  • Shynta Ayu Dwi Darmawan Universitas Gunadarma
  • Karmilasari Karmilasari Universitas Gunadarma

DOI:

https://doi.org/10.32493/jtsi.v7i2.38653

Keywords:

prediction; simple moving average; number of disease cases; East Java provincial health office

Abstract

Based on a review of the East Java Provincial Health Profile book for 2017 to 2021, public health conditions over the past few years show considerable differences in disease cases across 38 cities or districts. Determining disease management priorities based on health profiles, as well as predicting future disease case trends, is a difficult task. Therefore, a prediction strategy that uses historical data to estimate the pattern of disease cases in each region from year to year is needed, as well as a web-based system to implement the prediction. The steps taken include a literature study, the creation of a prediction model using the Simple Moving Average approach, and the implementation of the system with a MySQL database, PHP backend, and Angular frontend. The results showed the success of the disease case trend prediction application using historical data for five periods. Functionality testing and browser compatibility show that the system runs as expected in various environments, while usability testing using the WebQual 4.0 technique produces an average score of 4.34 (excellent), which indicates that the system successfully meets user needs.

References

Aji, B. G., Sondawa, D. C. A., Anindika, F. A., & Januarita, D. (2022). Analisis Peramalan Obat Menggunakan Metode Simple Moving Average, Weighted Moving Average, Dan Exponential Smoothing. JURIKOM (Jurnal Riset Komputer), 9(4), 959–965. https://doi.org/10.30865/jurikom.v9i4.4454

Choi, S. B., & Ahn, I. (2020). Forecasting imported COVID-19 cases in South Korea using mobile roaming data. PLOS ONE, 15(11), 1–10. https://doi.org/10.1371/journal.pone.0241466

Choudhary, A., Kumar, P., Sahu, S. K., Pradhan, C., Singh, S. K., Gašparovic, M., Shukla, A., & Singh, A. K. (2022). Time Series Simulation and Forecasting of Air Quality Using In-situ and Satellite-Based Observations Over an Urban Region. Nature Environment and Pollution Technology, 21(3), 1137–1148. https://doi.org/10.46488/NEPT.2022.v21i03.018

Dinas Kesehatan Provinsi Jawa Timur. (2018). Profil Kesehatan Provinsi Jawa Timur Tahun 2017.

Dinas Kesehatan Provinsi Jawa Timur. (2019). Profil Kesehatan Provinsi Jawa Timur Tahun 2018.

Dinas Kesehatan Provinsi Jawa Timur. (2020). Profil Kesehatan Provinsi Jawa Timur Tahun 2019.

Dinas Kesehatan Provinsi Jawa Timur. (2021). Profil Kesehatan Provinsi Jawa Timur Tahun 2020.

Dinas Kesehatan Provinsi Jawa Timur. (2022). Profil Kesehatan Provinsi Jawa Timur Tahun 2021.

Fiarni, C., Kurniawa, H., & Mulyono, F. H. (2021). Perancangan Integrated Demand Forecast untuk Manajemen Informasi Sistem Pelayanan Kegawatdaruratan. Telematika, 16(1), 10–17.

Sari, N. L., & Hasanuddin, T. (2020). Analisis Performa Metode Moving Average Model untuk Prediksi Jumlah Penderita Covid-19. Indonesian Journal of Data and Science (IJODAS), 1(3), 87–95. https://kawalcovid19.id/

Lubis, I. S. Br., & Azhar, A. H. (2023). Perancangan Aplikasi Peramalan Penjualan Obat Menggunakan Metode Single Moving Average. Jurnal ITCC (Information Technology and Cyber Crime), 1(2), 10–20.

Mahmoudian, Y., Nemati, A., & Safaei, A. S. (2023). A forecasting approach for hospital bed capacity planning using machine learning and deep learning with application to public hospitals. Healthcare Analytics, 4, 100245. https://doi.org/10.1016/j.health.2023.100245

Nwosu, U. I., & Obite, C. P. (2021). Modeling Ivory Coast COVID-19 cases: Identification of a high-performance model for utilization. Results in Physics, 20, 103763. https://doi.org/10.1016/j.rinp.2020.103763

Oshinubi, K., Al-Awadhi, F., Rachdi, M., & Demongeot, J. (2021). Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics. Kuwait Journal of Science, 1–30. https://doi.org/10.48129/kjs.splcov.14501

Rahman, M. M., Uddin, M. G., Islam, M. R., Kibria, M. K., & Mollah, M. N. H. (2023). Day Level Forecasting for COVID-19 Pandemic Spread in SAARC Countries. International Journal of Statistical Sciences, 23(2), 129–142. https://doi.org/10.3329/ijss.v23i2.70135

Rostami-Tabar, B., & Rendon-Sanchez, J. F. (2021). Forecasting COVID-19 daily cases using phone call data. Applied Soft Computing, 100, 106932. https://doi.org/10.1016/j.asoc.2020.106932

Salsabila, Y. L., & Ratnasari, V. (2022). Pengelompokan Kabupaten/Kota di Jawa Timur Berdasarkan Indikator Kesehatan Masyarakat dengan Pendekatan Metode Ensemble ROCK. Jurnal Sains Dan Seni ITS, 11(2), D220–D227. https://doi.org/10.12962/j23373520.v11i2.73441

Shodiq, M., Priyono, A., & Ramanda, F. (2023). Prediksi Jumlah Penyakit Infeksi Saluran Pernapasan Akut (ISPA) Menggunakan Simple Moving Average. Jurnal Informatika Medis, 1(2), 48–52.

Siregar, M. T., Made, G., Sasmita, A., Agung, G., & Putri, A. (2022). Perbandingan Analisis Metode Peramalan Jumlah Kasus Penyakit Menular di Kota Bandung (Studi Kasus: Dinas Kesehatan Kota Bandung). JITTER-Jurnal Ilmiah Teknologi Dan Komputer, 3(1), 831–842.

Widjiyati, N. (2022). Analisa Prediksi Algoritma Simple Moving Average Dengan Pendekatan Multi Periode. Smart Comp: Jurnalnya Orang Pintar Komputer, 11(1), 96–99. https://doi.org/10.30591/smartcomp.v11i1.3206

Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 2 April 2024

Downloads

Published

2024-04-30

How to Cite

Darmawan, S. A. D., & Karmilasari, K. (2024). Prediksi Jumlah Kasus Penyakit di Jawa Timur Memanfaatkan Metode Simple Moving Average. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(2), 770–778. https://doi.org/10.32493/jtsi.v7i2.38653

Issue

Vol. 7 No. 2 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

Section

Article

License

Copyright (c) 2024 Shynta Ayu Dwi Darmawan, Karmilasari Karmilasari

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.

In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License

YOU ARE FREE TO:

  • Adapt - remix, transform, and build upon the material for any purpose, even commercially.
  • The licensor cannot revoke these freedoms as long as you follow the license terms