Penerapan Metode Random Forest dalam Klasifikasi Huruf BISINDO dengan Menggunakan Ekstraksi Fitur Warna dan Bentuk - Indra, Dolly; Hayati, Lilis Nur; Daris, Mega Asfirawati; As'ad, Ihwana; Mansyur, Umar (Computer Engineering Departement, Universitas Komputer Indonesia, 2024)
Digital image processing is a field of study that focuses on how an image can be formed, processed, and analyzed to generate useful information for humans. In this research, the utilization of digital images is implemented to classify BISINDO (Indonesian Sign Language) letters from A to Z using the Random Forest classification method. The initial stage in the classification of BISINDO letter images involves pre-processing, which includes converting RGB images to grayscale and performing segmentation through three stages: thresholding, morphology, and edge detection using the Prewitt operator. Subsequently, features such as HSV color extraction and metric shape features, as well as eccentricity, are extracted. These extracted feature values are then utilized in the classification stage of BISINDO letter images from A to Z using the Random Forest method. In this study, three data comparison scenarios were employed for testing purposes. The first scenario involved an 80:20 data ratio, which achieved a testing accuracy of 94.2%. The second scenario with a 70:30 data ratio achieved a testing accuracy of 93.6%, while the third scenario with a 60:40 data ratio had a lower accuracy of only 77.9%. Based on the results of our testing, the system developed is capable of effectively classifying BISINDO letters from A to Z using color and shape feature extraction, along with the Random Forest classification method. The best results were obtained in the data comparison scenario of 80:20, achieving an accuracy rate of 94.2%. Keywords – BISINDO, HSV, Metric, Eccentricity, Random Forest.