Augmentasi Data pada Prasasti Logam untuk Deteksi Aksara Kawi
DOI:
https://doi.org/10.37859/jf.v14i1.6952
Abstract
Deep learning telah diterapkan di berbagai bidang seperti pembacaan aksara pada prasasti. Beberapa penelitian telah menerapkan deep learning dan menciptakan model dengan akurasi yang tinggi untuk melakukan deteksi dan pengenalan aksara pada prasasti. Seperti diketahui deep learning memerlukan data dalam jumlah yang besar dan bervariasi untuk menghasilkan model dengan performa yang tinggi. Namun sayangnya, tidak semua data memiliki jumlah yang cukup besar untuk mendukung proses pelatihan dengan deep learning. Data Prasasti merupakan salah satu contoh data yang memiliki jumlah terbatas karena susah untuk ditemukan. Untuk mendukung ketersediaan data dalam jumlah besar serta bervariasi, penelitian ini menerapkan augmentasi pada data aksara di prasasti untuk memenuhi kebutuhan dataset pelatihan deep learning. Teknik augmentasi data yang digunakan telah disesuaikan dengan data berjenis aksara, seperti rotasi, translasi, zoom, blur serta kombinasi antara teknik tersebut untuk menciptakan variasi baru. Penelitian ini menggunakan 3 (tiga) sampel data gambar yang diambil dari salah satu lempeng Prasasti Waruṅgahan. Menerapkan 19 teknik augmentasi data, penelitian ini menghasilkan data baru dengan jumlah yang besar serta bervariasi. Sebelum augmentasi, data gambar hanya sebanyak 3 (tiga) data dengan total 285 label. Setelah augmentasi dilakukan, jumlah data meningkat menjadi 60 data gambar lempeng dengan total 4975 label. Hasil augmentasi data ini digunakan sebagai dataset pelatihan untuk YOLO object detection. Hasil pelatihan yang dilakukan pada penelitian lain menunjukkan bahwa penambahan jumlah serta variasi data meningkatkan akurasi pendeteksian aksara khususnya pada Prasasti Waruṅgahan.
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