Bimantoro Aulia Rizky, - (2023) REMOTE SENSING UNTUK MEMPREDIKSI KASUS DEMAM BERDARAH MENGGUNAKAN RANDOM FOREST. S1 thesis, Universitas Pendidikan Indonesia.
![[img]](http://repository.upi.edu/style/images/fileicons/text.png) |
Text
S_KOM_1900303_Title.pdf Download (358kB) |
|
Text
S_KOM_1900303_Chapter1.pdf Download (90kB) |
|
Text
S_KOM_1900303_Chapter2.pdf Restricted to Staf Perpustakaan Download (783kB) |
|
Text
S_KOM_1900303_Chapter3.pdf Download (242kB) |
|
Text
S_KOM_1900303_Chapter4.pdf Restricted to Staf Perpustakaan Download (2MB) |
|
Text
S_KOM_1900303_Chapter5.pdf Download (52kB) |
|
Text
S_KOM_1900303_Appendix.pdf Restricted to Staf Perpustakaan Download (552kB) |
Abstract
Penelitian ini mengkaji penggunaan teknologi remote sensing dan algoritma random forest dalam upaya memprediksi kasus demam berdarah. Demam berdarah adalah masalah kesehatan global yang memerlukan pemantauan dan tindakan cepat. Penggunaan data citra satelit, seperti Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), data suhu permukaan, kecepatan angin, curah hujan, kelembaban, suhu, dan populasi, digunakan untuk mengidentifikasi daerah-daerah yang berpotensi tinggi terhadap penularan penyakit tersebut. Algoritma random forest digunakan untuk mengintegrasikan berbagai parameter lingkungan dan membangun model prediksi. Informasi data citra satelit diekstraksi menggunakan aplikasi google earth engine, menghasilkan data yang digunakan memprediksi demam berdarah menggunakan random forest. Hasil penelitian menunjukkan bahwa skenario terbaik menghasilkan nilai RMSE sebesar 57.912128. This research examines the use of remote sensing technology and random forest algorithm in an effort to predict dengue fever cases. Dengue is a global health problem that requires monitoring and rapid action. The use of satellite image data, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), surface temperature, wind speed, rainfall, humidity, temperature, and population data, are used to identify areas with high potential for transmission of the disease. A random forest algorithm was used to integrate the various environmental parameters and build a prediction model. Satellite image data information was extracted using google earth engine application, resulting in data used to predict dengue fever using random forest. The results showed that the best scenario resulted in an RMSE value of 57.912128.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | ID SINTA Pembimbing: Lala Septem Riza : 5975668 Yaya Wihardi : 5994413 |
| Uncontrolled Keywords: | demam berdarah, remote sensing, machine learning, random forest, google earth engine dengue fever, remote sensing, machine learning, random forest, google earth engine |
| Subjects: | L Education > L Education (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Ilmu Komputer |
| Depositing User: | Bimantoro Aulia Rizky |
| Date Deposited: | 02 Jan 2024 06:02 |
| Last Modified: | 02 Jan 2024 06:02 |
| URI: | http://repository.upi.edu/id/eprint/113985 |
Actions (login required)
 |
View Item |