Publisher

Tel-UTel-U

Journal International Journal on Information and Communication Technology (IJoICT)International Journal on Information and Communication Technology (IJoICT) Abstract

In Indonesia, dengue hemorrhagic fever (DHF) has become a serious community health concern due to fluctuating incidence rates influenced by several factors. It requires comprehensive control strategies to prevent the rise of the incidence. This study seeks to classify the future spread of DHF in Bandung City, accompanied by optimal factors that influence the increase in its spread. This study proposes using Decision Tree to predict a classification of DHF spread with implementation of spatial time-based feature expansion. The developed scenario is to build a target class with class prediction model based on the previous time period. From the developed scenario, the selected model has optimal performance to form a class prediction model in the future. The used classes itselves are formed by ranging the incidence rate (IR) into low, medium and high class. The data used includes spatial-temporal information such as population, education level, rainfall, temperature, and blood type from 2017 to 2021. The results obtained show that the performance of Decision Tree using time-based feature expansion is more than 90%, with visual predictions that help identify high risk areas. The contribution of this study is to inform the public and health institution regarding DHF spread for the future and influential factor so that the government can provide policies as early as possible to prevent DHF spread.

Conclusion

This research successfully predicted the spread of DHF in Bandung using a Decision Tree with time-based feature expansion, achieving accuracy levels exceeding 90%.The implementation of feature expansion from previous time periods proved effective in enhancing prediction accuracy.The findings contribute to informing public health initiatives and government policies for proactive DHF prevention.

Future Research

Further research could investigate the integration of real-time environmental data, such as humidity and mosquito breeding site locations, to refine the prediction models and enhance their responsiveness to immediate conditions. Exploring the application of deep learning techniques, like recurrent neural networks (RNNs), alongside Decision Trees could potentially capture more complex temporal dependencies in DHF spread patterns, leading to improved forecasting accuracy. Additionally, a study focusing on the socio-economic factors influencing DHF vulnerability within specific sub-districts of Bandung could provide valuable insights for targeted intervention strategies, considering factors like housing quality, sanitation access, and community awareness levels. These investigations, building upon the current research, could contribute to a more comprehensive and effective approach to DHF control and prevention in Bandung and similar urban environments, ultimately reducing the burden of this significant public health concern. The combination of these approaches will provide a more holistic understanding of DHF dynamics and enable the development of more effective prevention and control strategies.

References

1. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informas)i. application na bayes classifier algorithm classify... jurnal.iaii.or.id/index.php/RESTI/article/view/4498Jurnal RESTI Rekayasa Sistem dan Teknologi Informas i application na bayes classifier algorithm classify jurnal iaii index php RESTI article view 4498
2. Association of dengue disease severity and blood group: A systematic review and meta‐analysis... doi.org/10.1002/rmv.2147Association of dengue disease severity and blood group A systematic review and metayAAAaanalysis doi 10 1002 rmv 2147

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Pages	17
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