AKURASI PENGGUNAAN METODE SUPPORT VECTOR MACHINE (SVM) DALAM PREDIKSI KAPASITAS DUKUNG FONDASI TIANG

Raden Harya Dananjaya, Sutrisno Sutrisno, Nanda Milenia Dwi Rahmawati

Abstract

Kapasitas dukung merupakan hal penting yang harus diperhatikan pada saat merancang sebuah fondasi. Artificial intelligence dapat digunakan sebagai salah satu metode untuk memprediksi daya dukung fondasi. Salah satu metode di dalam artificial intelligence yang sering digunakan adalah support vector machine (SVM). Keuntungan dari penggunaan metode SVM adalah pengurangan asumsi yang digunakan untuk memperoleh hubungan antara variabel bebas dengan variabel terikat pada proses prediksi. Penelitian ini bertujuan untuk mengetahui akurasi SVM dalam memprediksi kapasitas dukung fondasi dengan menggunakan data cone penetration test (CPT). Features atau variabel bebas yang digunakan sebagai input adalah diameter tiang (D), panjang tiang (L), pile material, pile type, installation method, nilai qc (tahanan conus) dan fs (tahanan gesek). Untuk melakukan kalibrasi dan validasi, digunakan data loading test. Proses validasi dilakukan dengan menggunakan metode k-fold cross validation yang pada penelitian ini menggunakan 10-folds cross validation. Hasil penelitian menunjukkan bahwa model SVM yang menghasilkan akurasi terbaik adalah dengan menggunakan kernel polynomial, C sebesar 0,5 dan g sebesar 0,1. Proporsi data training dan testing yang menghasilkan akurasi tertinggi adalah 90% : 10%, yang mana memiliki nilai R2 dan RMSE pada proses testing sebesar 0,9287 dan 637,1087 kN, secara berturut-turut.    

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DOI: https://doi.org/10.20961/mateksi.v11i2.65101

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