Memprediksi Ketinggian Tsunami Menggunakan Random Forest Regressor

  • Novantri Prasetya Putra Institut Teknologi Telkom Purwokerto
  • Jerry Lasama Institut Teknologi Telkom Purwokerto
  • Andre Pradika E.P. Institut Teknologi Telkom Purwokerto
  • Agi Prasetiadi Institut Teknologi Telkom Purwokerto
Keywords: ensemble, machine learning, prediksi ketinggian tsunami

Abstract

Tsunami memiliki ketinggian yang berbeda-beda, semakin tinggi maka semakin banyak persiapanmenghadapi tsunami untuk menekan jumlah korban. Machine learning dapat digunakan untuk menentukan tinggi dari tsunami. Ketika tsunami datang, ketinggian tsunami dapat diprediksi untuk mengetahui titik aman pada bibir pantai. Hal ini akan sangat membantu dalam proses evakuasi sehingga dapat menekan jumlah korban jiwa. Metode yang digunakan yaitu Random Forest Regressor untuk dapat menganalisa peubah latitude, longtitude, tahun, bulan, hari, kekuatan gempa, negara, kode negara,dan kode penyebab gempa dengan ketinggian maximum tsunami. Dengan menggunakan metode tersebut, didapatkan negatif mean absolut error sebesar -3,93. Setelah Mean Absolute Error didapatkan, akan ditemukan feature yang memiliki importance terbesar untuk dapat dijadikan predictor.

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Published
2020-03-16
How to Cite
Putra, N., Lasama, J., E.P., A., & Prasetiadi, A. (2020). Memprediksi Ketinggian Tsunami Menggunakan Random Forest Regressor. Conference on Electrical Engineering, Telematics, Industrial Technology, and Creative Media (CENTIVE), 2(1), 48-52. Retrieved from http://conferences.ittelkom-pwt.ac.id/index.php/centive/article/view/86
Section
Articles