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p-Index From 2021 - 2026
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All Journal Scientechno: Journal of Science and Technology Journal of Social Science Utilizing Technology
Gulbahor Alimova
Khujand State University

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USING MACHINE LEARNING TO PREDICT DENGUE FEVER OUTBREAKS IN INDONESIAN URBAN CENTERS BASED ON CLIMATE AND MOBILITY DATA Som Chai; Shahram Rahimov; Dilshod Tursunuv; Gulbahor Alimova
Scientechno: Journal of Science and Technology Vol. 5 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i1.2641

Abstract

Dengue fever remains a critical public-health threat in Indonesia’s densely populated urban centers, where climatic fluctuations and human mobility accelerate transmission dynamics. This study aims to develop a predictive model for dengue outbreaks using machine-learning techniques that integrate multi-source climate indicators (temperature, rainfall, humidity) and population-mobility data. A quantitative research design employing supervised learning algorithms including Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks—was applied to historical datasets from 2015–2023 across six major Indonesian cities. Model performance was evaluated using accuracy, precision, recall, and AUC metrics. Results indicate that the LSTM model achieved the highest predictive accuracy (92.3%) and superior temporal sensitivity to climatic shifts and mobility surges compared with traditional regression models. These findings demonstrate that machine-learning-based early-warning systems can identify outbreak hotspots up to four weeks in advance, providing actionable insights for urban health authorities. The study concludes that integrating climate and mobility analytics enhances the effectiveness of public-health surveillance and supports proactive dengue-control interventions in rapidly urbanizing environments.
Blockchain for Social Good: A Case Study on the Use of Distributed Ledger Technology for Transparent Supply Chains in Fair Trade Coffee Shahram Rahimov; Dilshod Tursunov; Gulbahor Alimova
Journal of Social Science Utilizing Technology Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i6.2907

Abstract

Background. The increasing demand for transparency in global supply chains has prompted the exploration of emerging technologies such as blockchain. In particular, the application of distributed ledger technology (DLT) in fair trade industries has the potential to enhance accountability, traceability, and ethical sourcing practices. This study examines the use of blockchain in the fair trade coffee sector, where transparency is crucial to ensure that producers receive fair compensation and consumers can trust the authenticity of ethical claims. Purpose. The primary aim of this research is to investigate how blockchain technology can improve transparency and efficiency in the coffee supply chain, from farm to consumer. Method. The study employs a case study methodology, focusing on a fair trade coffee cooperative that implemented blockchain to track the journey of coffee beans. Qualitative interviews with stakeholders, including farmers, distributors, and consumers, were conducted to gather insights into the practical applications and challenges of blockchain in this context. Results. The findings indicate that blockchain enhances transparency by providing immutable records of transactions, but challenges related to technology adoption, cost, and scalability remain. Conclusion. The study concludes that while blockchain shows promise for social good in fair trade coffee, further research and development are needed to address the limitations and expand its implementation across the industry.
Co-Authors Dilshod Tursunov Dilshod Tursunuv Shahram Rahimov Som Chai