ANALISIS DATA DAN PREDIKSI BANJIR DI JAKARTA MENGGUNAKAN METODE MACHINE LEARNING

RAYHAN, MUHAMMAD (2024) ANALISIS DATA DAN PREDIKSI BANJIR DI JAKARTA MENGGUNAKAN METODE MACHINE LEARNING. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Machine learning methods are used in this research to carry out data analysis and flood predictions in Jakarta. The aim of this research is that analysis of flood impact data helps in identifying areas' vulnerability to flooding. Development of machine learning based algorithms such as Linear Regression and Decision Tree. Machine learning is some of the methods that can be used. To increase the coverage of flood predictions, the steps taken are analyzing flood data, determining objectives, then continuing with collecting the necessary data, after the data is obtained, data analysis can be carried out and results will be obtained. The results of accuracy testing on the linear regression algorithm with MAE parameters obtained results of 0.59, MSE 0.65 and RMSE 0.80, for testing the decission tree algorithm with MAE parameters obtained results of 3.35, MSE 12.5 and RMSE 3.54. The results of this research conclude that the Decision Tree and Linear Regression algorithms have better quality in making predictions. It is hoped that this research can make considerations about which algorithm is most appropriate to apply in processing flood data and the results of flood data processing can be used to determine prediction strategies and the public will know the level of flood levels in Jakarta. Keywords: Machine learning, Decision Tree, Linear Regression, prediction Metode pembelajaran mesin digunakan dalam penelitian ini untuk melakukan analisis data dan prediksi banjir di Jakarta. Tujuan dari penelitian ini adalah analisis data dampak banjir membantu dalam mengidentifikasi kerentanan wilayah terhadap banjir. Pengembangan algoritma berbasis pembelajaran mesin seperti Regresi Linear dan Decision Tree. Machine learning adalah beberapa metode yang dapat digunakan. Untuk meningkatkan ketepatan prediksi banjir, Tahapan yang dilakukan yaitu dengan menganalisa data banjir, menentukan tujuan, lalu dilanjut dengan mengumpulkan data yang diperlukan, setelah data didapat makan analisa data bisa dilakukan lalu hasil akan didapatkan. Hasil pengujian akurasi pada algoritma regresi linear dengan parameter MAE mendapatkan 0.59, MSE 0.65 dan RMSE 0.80, untuk pengujian pada algoritma decission tree dengan parameter MAE mendapatkan hasil 3.35, MSE 12.5 dan RMSE 3.54. Hasil penelitian ini menyimpulkan bahwa Algoritma Decision tree dan Regresi Linear mempunyai kualitas yang lebih baik dalam membentuk prediksi. Penelitian ini diharapkan dapat membuat pertimbangan algoritma mana yang paling tepat diterapkan dalam pengolahan data banjir dan hasil pengolahan data banjir dapat dimanfaatkan untuk menentukan strategi prediksi dan masyarakat menjadi mengetahui tingkat tingkat banjir di jakarta. Kata Kunci : Machine learning, Decision Tree, Regresi Linear, prediksi

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 069
Call Number:	SIK/15/24/056
NIM/NIDN Creators:	41519010146
Uncontrolled Keywords:	Machine learning, Decision Tree, Regresi Linear, prediksi
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem > 003.7 Kinds of Systems/Macam-macam Sistem > 003.74 Linear Systems/Sistem Linear 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	20 Mar 2024 03:07
Last Modified:	20 Mar 2024 03:07
URI:	http://repository.mercubuana.ac.id/id/eprint/87298

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