Sistem Isyarat Bahasa Indonesia Research Articles

The Comparison of K-Nearest Neighbors and Random Forest Algorithm to Recognize Indonesian Sign Language in a Real-Time

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).

The development of a wearable device for Sign Language Gesture Recognition (SLGR) by using Conceptual Design Approach (CDA)

In recent years, there has been a significant focus on researching and developing Sign Language Gesture Recognition (SLGR) for people with hearing and speaking impairments. This is especially important in Indonesia, where there are approximately two million people with these disabilities. However, current research and developments on SLGR devices are specifically designed to understand only particular sign language systems, such as Korean Standard Sign Language (KSDSL) and American Sign Language (ASL), each of which has its unique gestures and models. As a result, no device has been developed to recognize the gestures of the Indonesian sign language system, known as Sistem Isyarat Bahasa Indonesia (SIBI). Therefore, this study aims to develop an SLGR device that can recognize and translate SIBI gestures into output images via text and speech. The development of the SLGR device in this study is conducted by using the Conceptual Design Approach (CDA) methodology. Where in this case, previous research on SLGR devices is first observed as a benchmark for comparison. Furthermore, the benchmark is used as the basis for the function, sub-function, and specification of the proposed design. Based on these stages of benchmarking and concept development, the study concluded that the final design of the proposed SLGR device is constructed by using 5 flex and Gyroscope sensors connected wirelessly to the Raspberry microcontroller. The device is equipped with a voice system and LCR RPi as the output system for translation. Based on the combination of these sensors, the device is able to identify any particular gestures that correspond to words and phrases in SIBI and translate them into speech via the designated speaker and text display on the LCD screen. To fully understand the performance of the device, experimental tests are conducted by analyzing the input of 26 alphabets in the SIBI system. As a result, the device demonstrated an average of 92% accuracy to convert sign language into voice and text, which demonstrates the usefulness of the proposed device

Optimization of Modified Hidden Markov Model for Vision-Based Indonesian Sign Languages Recognition

Sign language is one of the most popular language which is used as a communication bridge that depends on hand movement. As it is used worldwide, the variety of sign languages is very large and most of the people doesn’t understand it which makes the communication between deaf and normal people interrupted. This makes sign language recognition popular as it makes people doesn’t need to understand the sign but still understand the meaning of the sign. But sign language itself has many problems such as the possibility of different dataset has the same movement but different meaning, the method used of each dataset could be ineffective in other dataset, and many other else which makes it difficult to be implemented. Other than that, vision-based recognition is not as popular as sensor-based recognition because of the difference in feature accuracy even though it could give more area of improvement. That’s why the aim of this paper is to presents the combination of methods used to recognize vision-based Indonesian Sign Language and enhance the method using optimization technique. The methodology used in this study follows four steps framework of sign language recognition which is dataset collection, preprocessing, feature extraction, and recognition. Each method is improved in order to be compared and checked what is the method combination and optimization method is the best for sign language recognition. For dataset collection, the dataset that is used is formal Indonesian Sign Language which is called Sistem Isyarat Bahasa Indonesia (SIBI) with some constraint in order to make sure the quality of the dataset is good. The preprocessing methods are differentiated into two categories which is image enhancement and hand detection. Image enhancement methods include no image enhancement, Gaussian blur, Bilateral blur, and Contrast Limited Adaptive Histogram Equalization and hand detection methods include skin detection using YCbCr color space and edge detection using Canny algorithm. Feature that is used in this study is pair of left and right-side movement for each frame that is extracted by calculating the average value of each pixels from left and right-side image that could be a representative value of each frame. Lastly the recognition system that is used is Gaussian Hidden Markov Model and its’ state optimization which includes no optimization, Latin Hypercube optimization, Hill Climbing optimization, and Combination of Latin Hypercube and Hill Climbing optimization. The experiment result of the proposed method could recognize up to 82% accuracy rate. The improvement of this research could be implemented on educational studies or game development that need vision-based sign language recognition system.

User Interface Modelling for SIBI (Sistem Isyarat Bahasa Indonesia/Indonesian Sign Language System) learning applications using the User-Centered Design Method

Sign Language is a type of language used to help deaf children to communicate. The Indonesian government has created their standardization system for sign language called SIBI (Sistem Isyarat Bahasa Indonesia). Deaf children have characteristics such as lack of memory ability. Therefore they need media for repetition in learning. The actual mobile-based learning could support recurrence in the subject is already available, but not all the designs on mobile technology are suitable for children’s needs, proven by testing the existing application using USE Questionnaire which obtained a usability average of 58%, which mean that the app could not provide convenience and satisfaction for users. Therefore, to create a user interface that suits the needs of the user, the User-Centered Design (UCD) method is used. The usability testing conducted for the prototype application and the result is 90% with an excellent category. If the result of the usability is an excellent value, then the app could be used very easy and satisfied for user.

Bisindo Sign Language Transliteration Using Automata

Sign Language is a language which prioritizes manual communication, body language and lip movement for communication. People who usesign language for communication aredeaf and mute people. Sign language in Indonesia is Sistem Isyarat Bahasa Indonesia (SIBI) and Bahasa Isyarat Indonesia (BISINDO). BISINDO has a unique clause pattern that isSOPA (Subject, Object, Predicate, Adverbial). The clause pattern of BISINDO is just for deaf and mute people who know and understand it. Because there are some different structures between Indonesian language and BISINDO, itis neededlearning media which can translate from Indonesian language to BISINDO. Finie State Automata (FSA) is a method which is able to recognize pattern sentence classification in Indonesian language. FSA method is NSA (Non-DetermisticFinie State Automata) that is otomata with characteristics as a state. NFA is used to give introducing structure pattern sentence in Indonesian language. Sentence pattern which is produced from NFA produces eight sentences pattern which are arranged as requisites ofBISINDO translation. The result of research which is experimented by sign language researchersas many as 90% can translate well and appropriate with BISINDO sentence pattern, 10% explain that it is not appropriate with translation of sentence pattern and sign language model. Based on the data, it can be known that media of BISINDO sign language learning based on web using method of translation automata can be used for learning media.

Real Time SIBI Sign Language Recognition Based on K-Nearest Neighbor

Persons with disabilities also have the right to communicate with each other, both with normal people and people with other disabilities. People with disabilities will be difficult to communicate with other people. They use 'sign language' to communicate. That's why other normal people will be difficult to communicate with them. Because there are not many normal people that can understand the 'sign language'. System which can help to communicate with disabilities people are needed. In this paper, we proposed sign language recognition for Sistem Isyarat Bahasa Indonesia (SIBI) using leap motion based on K-Nearest Neighbor. Technology of leap motion controller will generate the existence of coordinate points on each bone in hand. As an input, we used the value of distance between the coordinates of each bone distal to the position of the palm, which were measured using Euclidean Distance. This feature of distance will be used for training and testing data on K-Nearest Neighbor method. The experiment result shows that the best accuracy is 0,78 and error 0,22 with proposed parameter of K = 5.

The Development Learning Video of Cube And Cuboid for Deaf Students in the 3RD Grade of Middle School

This research was aims to develop video instructional media of cube andcuboid for deaf students. The background of doing this research instructionalmedia is not only required by means of the normal students, but it is neededby the students who have special needs like deaf students also. Observationresult addresses that the deaf students are inclined to be slow in catching thematerials are given due to the lack of media, difficulty in imagining cube andcuboid, and forget the materials are given. In accordance with the students'characteristics and needs, it requires the visual-based media. This researchtypically employed research and development method. The result ofvalidation based on the assessment of media practitioner with the scalestarting from 0 to 5 was 4.42, and material practitioner from 0 to 5 was 4. Theresult of validator's assessment demonstrated that this instructional media canbe available to use. The result of students' response in using this instructionalmedia was 74.5 %. It revealed that students' response to this instructionalmedia was good. This research has added the Sistem Isyarat BahasaIndonesia (SIBI), and accordance with the characteristics of deaf students andalso has added animations from the Cabri3D software to help studentsobserve the shape and characteristics of the building blocks and cubes

Sistem Pengenalan Bahasa Isyarat Indonesia dengan Menggunakan Metode Fuzzy K-Nearest Neighbor

The Indonesian Natural Sign System (SIBI) is one of the most natural languages of communication, especially for deaf and speech impaired. Deaf and speech impaired can understand and communicate with each other by using sign language, but some normal people will have difficulty understanding sign language with deaf and speech impunity to say. To overcome these problems need develop a system that is able to recognize the Indonesian Sign System (SIBI) which is expected capable of learning media in communicating between the deaf and normal humans. The introduction of the Indonesian Sign System (SIBI) will consists of three main stages: image acquisition, preprocessing and recognition. In this research the classification method used is Fuzzy KNearest Neighbor (FKNN) method. Based on the results of experiments conducted with the classification using the method Fuzzy K-Nearest Neighbor (FKNN) obtained an accuracy of 88%.
 Index Term— Fuzzy K-Nearest Neighbor, Sistem Isyarat Bahasa Indonesia (SIBI).

Implementation of Real-Time Static Hand Gesture Recognition Using Artificial Neural Network

This paper implements static hand gesture recognition in recognizing the alphabetical sign from “A” to “Z”, number from “0” to “9”, and additional punctuation mark such as “Period”, “Question Mark”, and “Space” in Sistem Isyarat Bahasa Indonesia (SIBI). Hand gestures are obtained by evaluating the contourrepresentation from image segmentation of the glove wore by user. Then, it is classified using Artificial Neural Network (ANN) based on the training model previously built from 100 images for each gesture. The accuracy rate of hand gesture translation is calculated to be 90%. Moreover, speech translation recognizes NATO phonetic letter as the speech input for translation.

Aplikasi Virtual Kata Untuk Komunikasi Penyandang Tunarungu Berbasis Android

Deaf people are people who lose either their partial or complete hearing ability which will make the sense of hearing unusable. The problem encountered when meeting a deaf person is unable to communicate well. To solve this problem, it is necessary to build a virtual word application that is android based to help communicate with the deaf people. This app uses 3D virtual characters as a voice-translating medium into a sign language. The sign language is converted to 3D animation to make it easier to understand and attractive. The sign language system used in this application is the SIBI system (Sistem Isyarat Bahasa Indonesia). This application is built by using Unity 3D software with C # and Java programming language, while to create 3D animation is using Blender software. From the test results of this application, obviously it can facilitate the communication with the deaf people.   Keywords: word virtual application, deaf communication, 3D sign language animation, SIBI, android.