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All Journal Jurnal Administrasi Kesehatan Indonesia Window of Health : Jurnal Kesehatan
Surya, Asik
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Health Professions Medicine & Pharmacology Nursing Public Health

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Machine Learning Approach to Predict the Dengue Cases Based on Climate Factors Nasir, Muhammad; Aldillah Wulandhari, Shobiechah; Tenrisau, Dhihram; Haris Ibrahim, Muhammad; Rahastri, Ajeng; Sa’adatar Rohmah, Nilna; Surya, Asik; Thohir, Burhanuddin; Aryani, Desfalina; Firdaus Kasim, Muhammad
Window of Health : Jurnal Kesehatan Vol 7 No 2 (April 2024)
Publisher : Fakultas Kesehatan Masyarakat Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/woh.vi.1428

Abstract

Dengue is a global health issue threatening public health, particularly in developing countries. Effective disease surveillance is critical to anticipate impending outbreaks and implement appropriate control responses. However, delays in dengue case reporting are frequent due to human resource shortfalls. Improved outbreak predictive capacity also requires additional input on vector presence and abundance, which is currently not captured in the surveillance platform. Thus, we developed a prototype AI application, “Dengue Forecasting", that leverages machine learning methods in filing the dengue case report and incorporates dengue vector and climatic parameters. This application simplifies the recording of dengue cases, vector abundance (Angka Bebas Jentik/ABJ), and selected climatic variables (sun exposure, temperature, humidity, wind speed, and precipitation) in Bandung City. The relevant data were extracted from Indonesia’s Ministry of Health and the Meteorological, Climatological, and Geophysical Agency. The entire process, from developing the model to deployment, was conducted under R programming language version 4.2.2 using packages (caret, shiny.io). The linear regression model demonstrated the highest precision (RMSE= 268.32 and MAE= 164.1) in predicting the dengue cases and outbreaks. We also applied this to the application deployment. “Dengue Forecasting” has the potential to assist policymakers at the district level, complementing Dengue EWARS, in anticipating and mitigating dengue outbreaks, especially in Bandung City.
SPATIAL ANALYSIS FOR MICROPLANNING TO ADDRESS IMMUNIZATION INEQUALITIES IN INDONESIA Astutik, Erni; Hargono, Arief; Artanti, Kurnia Dwi; Hidajah, Atik Choirul; Husnina, Zida; Sari, Siti Shofiya Novita; Sitohang, R. Vensya; Surya, Asik; Hapsari, Ratna Budi; Feletto, Marta
Indonesian Journal of Health Administration (Jurnal Administrasi Kesehatan Indonesia) Vol. 13 No. 1 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jaki.v13i1.2025.68-81

Abstract

Background: To achieve high and equitable immunization coverage, it is important to understand the access and utilization barriers, as well as the influencing determinants among population groups. Aims: This study aims to identify high-risk regencies and explore the application of spatial analysis to support microplanning in immunization programs. Methods: This study employed an implementation research design conducted in Aceh Province, Indonesia. Secondary datasets on immunization coverage, health human resources, facilities, and socio-economic parameters were analyzed. Focus group discussions (FGDs) and training sessions were conducted with health workers. Results: The average coverage of universal child immunization (UCI) across villages was 24.18%, while complete basic immunization (CBI) reached 55.85%. In general, regencies with low UCI and CBI often had limited human resources, inadequate health facilities, and a high proportion of high-risk populations. This study identified hot spots and cold spots in the study area. Additionally, participants reported that mapping using the application was easier and beneficial for supporting the preparation of immunization micro-planning. Conclusion: Spatial analysis can help address inequalities in immunization services and support resources during immunization. Qualitative approaches provided a deeper understanding of undocumented information. The use of mapping applications facilitated more effective microplanning in immunization programs. Keywords: Child mortality, health risk, immunization, microplanning, vaccine.
Co-Authors Aldillah Wulandhari, Shobiechah Arief Hargono Aryani, Desfalina Astutik, Erni Atik Choirul Hidajah Feletto, Marta Firdaus Kasim, Muhammad Hapsari, Ratna Budi Haris Ibrahim, Muhammad Kurnia Dwi Artanti Rahastri, Ajeng Rony, Zahara Tussoleha Sari, Siti Shofiya Novita Sa’adatar Rohmah, Nilna Sitohang, R. Vensya Tenrisau, Dhihram Thohir, Burhanuddin Zida Husnina