Analisis Fitur Pada Citra Gestur Tangan Sistem Isyarat Bahasa Indonesia
DOI:
https://doi.org/10.22441/format.2023.v12.i2.010Keywords:
pengenalan gestur tangan, ekstraksi fitur, SIBI, WEKAAbstract
Implementasi pengenalan gestur tangan telah banyak digunakan pada interaksi manusia dan komputer dekade terakhir ini. Implementasi pengenalan gestur tangan dapat meliputi banyak bidang, mulai dari bidang hiburan hingga bidang kesehatan atau medis. Untuk menghasilkan pengenalan gestur tangan yang baik, tentunya dibutuhkan penggunaan fitur yang tepat. Penelitian ini bertujuan menganalisis fitur dari citra statik gestur tangan agar dapat diimplemetasikan pada perangkat gawai tanpa menggunakan perangkat khusus. Pada penelitian sebelumnya fitur yang digunakan pada pengenalan gestur tangan berupa arah jari, panjang jari, posisi sendi, jarak ujung jari terhadap telapak tangan, sudut antar sendi-sendi jari berdekatan, serta sudut antara telapak tangan, pangkal jari, dan ujung jari. Berdasarkan penelitian terdahulu tersebut, diusulkan metode adalah dengan menggunakan fitur jarak, sudut, dan kuadran. Dataset yang digunakan berupa 528 data citra gestur tangan alfabet SIBI. Pengujian dilakukan dengan perangkat lunak WEKA menggunakan algoritme Naive Bayes, K-Nearest Neighbor(KNN), Neural Network, Support-Vector Machine (SVM), dan C4.5. Algoritme-algoritme tersebut dipilih karena dinilai memiliki karakteristik serta kebutuhan dataset yang berbeda sehingga pengujian fitur yang diusulkan dapat dilakukan secara menyeluruh. Metode K-fold cross validation digunakan pada pengujian untuk mengetahui akurasi terbaik. Hasil yang didapat adalah gabungan dari fitur jarak, sudut, dan kuadran dinilai palin baik diterapkan dengan akurasi tertinggi sebesar 60%.
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