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All Journal Scientific Journal of Informatics Jurnal Teknik Informatika dan Elektro (JURTIE)
Goenawan, Aaqila Dhiyaanisafa
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Identifikasi Warna Pada Objek Citra Digital Secara Real Time Menggunakan Pengolahan Model Warna HSV Goenawan, Aaqila Dhiyaanisafa; Rachman, M. Bakhara Alief; Pulungan, Mutiara Persada
Jurnal Teknik Informatika dan Elektro Vol 4 No 1 (2022): Januari: Jurnal Teknik Informatika dan Elektro
Publisher : Universitas Gajah Putih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55542/jurtie.v4i1.430

Abstract

Buta warna merupakan kondisi di mana penderitanya mengalami kesulitan membedakan warna tertentu (buta warna sebagian) atau bahkan keseluruhan warna (buta warna total). Berdasarkan data dari Colour Blind Awareness, setidaknya 300 juta orang di seluruh dunia mengidap buta warna. Buta warna menyebabkan penderita tidak dapat membedakan sebuah warna dengan warna lainnya. Namun dalam beberapa pekerjaan, dibutuhkan kemampuan untuk mengidentifikasi sebuah warna dengan akurat. Oleh karena itu, penelitian ini bertujuan untuk melakukan identifikasi warna pada sebuah objek yang merupakan citra digital. Identifikasi warna dilakukan secara real time menggunakan pengolahan model warna HSV.
The Comparison of K-Nearest Neighbors and Random Forest Algorithm to Recognize Indonesian Sign Language in a Real-Time Goenawan, Aaqila Dhiyaanisafa; Hartati, Sri
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48475

Abstract

Purpose: Comparing 2 models or prototype programs which can recognize Indonesian Sign Language System or Sistem Isyarat Bahasa Indonesia (SIBI) fonts from hand gesture and translate it’s into writing Messages in real-time.Methods: After selecting datasets and reprocessed by the researcher into 1 dataset, which are a combination of several sign image datasets of the SIBI letters images available on the Kaggle website, the dataset is converted into landmarks. The landmarks are divided into 26 sign classes and preprocessed to a total of 19,826 rows of data, and then divided into 67% training data and 33% test data. Next, both K-NN and Random Forest algorithm are implemented into different program and get tested into 2 different tests, model evaluation and real-time. At the end, the result is compared to see the increase of accuracy level of both K-Nearest Neighbors (K-NN) and Random Forest algorithm.Result: The constructed and trained model is then evaluated and the results of Precision, Recall, Accuracy, and F1-Score are 99.88% using the Random Forest algorithm. The results of real-time program testing with the K-Nearest Neighbors algorithm get higher results, where the average accuracy value reaches 99%.Novelty: From the result shows that the model built with the Random Forest algorithm is superior, but the K-Nearest Neighbors algorithm is better in real-time testing. Therefore, image data and its diversity should be increased, in order to improve recognition accuracy. The program could be enhanced by adding a function where the program can recognize hand gesture, not only one or two hands but also can recognize a hand gesture with movements so the program can recognize static and dynamic letter (required hands movement).
Co-Authors Pulungan, Mutiara Persada Rachman, M. Bakhara Alief Sri Hartati