Koreksi Spasial Data Batimetri Nasional (BATNAS) di Perairan Dangkal Menggunakan SVR RBF dan GPS Geodetik, Studi Kasus: Pulau Pari

Adithya Kresna Sumaamijaya; Fazel Karly; Amanda Puspita Putri; Andy Wibawa Nurrohman

doi:10.22146/jgise.108955

Koreksi Spasial Data Batimetri Nasional (BATNAS) di Perairan Dangkal Menggunakan SVR RBF dan GPS Geodetik, Studi Kasus: Pulau Pari

https://doi.org/10.22146/jgise.108955

Adithya Kresna Sumaamijaya^(1*), Fazel Karly⁽²⁾, Amanda Puspita Putri⁽³⁾, Andy Wibawa Nurrohman⁽⁴⁾

(1) Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia
(2) Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia
(3) Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia
(4) Program Studi Sains Informasi Geografi, Universitas Pendidikan Indonesia
(*) Corresponding Author

Abstract

Akurasi data batimetri nasional (BATNAS) di perairan dangkal, seperti di sekitar Pulau Pari (Kepulauan Seribu), masih terbatas. Padahal, data akurat sangat krusial untuk perencanaan wilayah pesisir, keselamatan pelayaran, dan pengelolaan sumber daya laut. Penelitian ini bertujuan meningkatkan akurasi data BATNAS melalui metode koreksi berbasis machine learning. Data GPS Geodetik digunakan sebagai acuan kebenaran dalam proses koreksi. Tiga metode yang dievaluasi adalah Simple Linear Regression, Support Vector Regression (SVR) dengan kernel linier, serta SVR dengan kernel radial basis function (RBF). Hasil menunjukkan bahwa model SVR RBF memiliki kinerja terbaik dengan nilai koefisien determinasi (R²) sebesar 0,6799, RMSE 0,1904 m, dan MAE 0,1128 m. Analisis residual menunjukkan bahwa SVR RBF memiliki kesalahan rata-rata mendekati nol (-0,0046 m) dan standar deviasi residual terkecil sebesar 0,1909 m. Visualisasi spasial dan profil memanjang memperkuat keunggulan SVR RBF dalam menghasilkan kontur dasar perairan kompleks secara akurat. Temuan ini menunjukkan bahwa SVR RBF efektif dalam meningkatkan akurasi data BATNAS di perairan dangkal dan berpotensi diterapkan untuk perbaikan data batimetri di wilayah serupa.

Keywords

koreksi batimetri, machine learning, data BATNAS, validasi GPS geodetik, perairan dangkal, support vector regression

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DOI: https://doi.org/10.22146/jgise.108955