Penerapan dan analisis metode ARIMAX pada penyakit demam berdarah di benua amerika = Implementation and analysis of the ARIMAX method for dengue fever in the americas

Gabriela, Arnetta (2024) Penerapan dan analisis metode ARIMAX pada penyakit demam berdarah di benua amerika = Implementation and analysis of the ARIMAX method for dengue fever in the americas. Bachelor thesis, Universitas Pelita Harapan.

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Abstract

Penyakit demam berdarah merupakan masalah kesehatan global di berbagai belahan dunia, termasuk di Benua Amerika. Penelitian ini bertujuan untuk mengevaluasi efektivitas dan akurasi metode ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables) dalam memodelkan dan memprediksi jumlah kasus demam berdarah di Benua Amerika dengan tiga negara perwakilan berdasarkan cluster, yaitu Brazil, Mexico, dan Saint Kitts and Nevis. Data yang digunakan adalah data deret waktu dari jumlah kasus demam berdarah pada tahun 1980 hingga 2022, serta faktor sosial ekonomi yang relevan dengan penyebaran penyakit tersebut. Faktor sosial ekonomi sebagai variabel eksogen dalam ARIMAX ini direduksi menjadi beberapa komponen dengan menggunakan Principal Component Analysis. Hasil peramalan deret waktu menggunakan metode ARIMAX dievaluasi dan dibandingkan dengan data aktual jumlah kasus demam berdarah untuk menilai akurasi dan efektivitasnya. Dari hasil analisis, ditunjukkan bahwa model ARIMAX mampu memberikan peramalan yang relatif akurat untuk jumlah kasus demam berdarah di Brazil, Mexico, dan Saint Kitts and Nevis. Namun, terdapat beberapa perbedaan dalam performa model antar negara, dengan model untuk Mexico menunjukkan performa yang lebih baik dibandingkan dengan dua negara lainnya. Hasil ini mengindikasikan bahwa faktor-faktor sosial ekonomi dapat memiliki pengaruh yang signifikan dalam penyebaran penyakit demam berdarah. Penemuan ini memberikan kontribusi penting dalam memahami dinamika epidemiologi penyakit demam berdarah dan dapat digunakan sebagai dasar untuk merancang strategi intervensi yang lebih efektif dalam pengendalian penyakit ini di masa depan. Selain itu, dalam penelitian ini juga ditekankan pentingnya pengumpulan data yang lengkap dan akurat serta pemantauan dalam jangka panjang terhadap penyakit demam berdarah untuk mendukung peramalan yang lebih baik. / Dengue fever is a global health issue in various parts of the world, including the Americas. This study aims to evaluate the effectiveness and accuracy of the ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables) method in modeling and predicting the number of dengue fever cases in the Americas with three representative countries based on clusters, namely Brazil, Mexico, and Saint Kitts and Nevis. The data used consists of time series data on the number of dengue fever cases from 1980 to 2022, as well as socio-economic factors relevant to the spread of the disease. Socio-economic factors as exogenous variables in this ARIMAX are reduced into several components using Principal Component Analysis. The forecasting results using the ARIMAX method are evaluated and compared with the actual data on the number of dengue fever cases to assess its accuracy and effectiveness. The analysis shows that the ARIMAX model is capable of providing relatively accurate forecasts for the number of dengue fever cases in Brazil, Mexico, and Saint Kitts and Nevis. However, there are some differences in the performance of the model among countries, with the model for Mexico showing better performance compared to the other two countries. These findings indicate that socioeconomic factors can have a significant impact on the spread of dengue fever. This research contributes significantly to understanding the epidemiological dynamics of dengue fever and can be used as a basis for designing more effective intervention strategies for controlling this disease in the future. Additionally, this study highlights the importance of comprehensive and accurate data collection and long-term monitoring of dengue fever to support better forecasting.

Item Type: Thesis (Bachelor)
Creators:
CreatorsNIMEmail
Gabriela, ArnettaNIM01112200012arnettagabriela28@gmail.com
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorMargaretha, HelenaNIDN0312057504helena.margaretha@uph.edu
Thesis advisorWidjaja, PetrusNIDN0314095901petrus.widjaja@uph.edu
Uncontrolled Keywords: time series forecasting; arimax; principal component analysis; cluster; multiple linear regression; regresi linear berganda.
Subjects: Q Science > QA Mathematics
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Mathematics
Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Mathematics
Depositing User: Arnetta Gabriela
Date Deposited: 24 Jul 2024 06:17
Last Modified: 24 Jul 2024 06:17
URI: http://repository.uph.edu/id/eprint/64245

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