Klasifikasi Jenis Tanah Berbasis Deep Learning Menggunakan Algoritma CNN

Eka Nurul Sabrina; Firahmi Rizky

doi:10.56211/hello_world.v5i2.1320

Authors

Eka Nurul Sabrina Universitas Muhammadiyah Sumatera Utara
Firahmi Rizky Universitas Muhammadiyah Sumatera Utara

DOI:

https://doi.org/10.56211/hello_world.v5i2.1320

Keywords:

Klasifikasi Tanah; CNN; Aluvial; Deep Learning

Abstract

Klasifikasi jenis tanah seperti aluvial, inceptisol, dan entisol memiliki peranan penting dalam perencanaan penggunaan lahan, pertanian, serta pengembangan infrastruktur. Proses identifikasi jenis tanah secara manual melalui observasi lapangan dan analisis laboratorium seringkali memakan waktu, biaya besar, serta memerlukan keahlian khusus. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis terhadap jenis tanah aluvial, inceptisol, dan entisol menggunakan metode deep learning dengan algoritma Convolutional Neural Network (CNN). Dataset berupa citra tanah diperoleh dari sumber primer (pengambilan langsung) dan sekunder (pangkalan data online). Proses penelitian mencakup preprocessing data seperti normalisasi, augmentasi, dan perubahan ukuran citra, dilanjutkan dengan pelabelan dan pelatihan model CNN. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan ketiga jenis tanah tersebut dengan tingkat akurasi yang tinggi, sehingga pendekatan ini efektif untuk mempercepat proses klasifikasi dan mengurangi ketergantungan pada metode manual. Temuan ini diharapkan dapat menjadi solusi inovatif dalam mendukung pengambilan keputusan berbasis teknologi di bidang geoteknik dan lingkungan.

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