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Gesture  recognition  technology  is  used  to  detect  movements  through  image processing,   enabling  computers  or digital devices to understand and interpret human  body  movements  as  input  or  commands.   This  technology  has  great potential  to bridge communication between the deaf community and individuals without   hearing   impairments,    enhancing  interaction  and  enriching  mutual understanding between the two.  However,  the accuracy ofgesture recognition is often  affected  by variations in the distance between hand landmarks.  Based on this problem,  this research proposes a methodfor stabilizing the measurement of distances between landmark points  in gesture recognition through a polynomial regression  approach.   Specifically,   the  distance  between  hand  landmarks  is calculated and stabilized using polynomial  regression to improve the accuracy of gesture recognition.  This method is implemented using the MediaPipeframework to detect and track hands in real-time,  and the OpenCV library to manage video. The  research  results  show  that  this  approach  can  significantly  improve  the stability  and accuracy  of gesture detection.   The developed system successfully detects gestures for  letters A  through F with a high accuracy  rate,  averaging above 98,3%.  The use ofpolynomial regression helps enhance detection accuracy by reducing noise in the landmark data.

Teaching early grade students to read the Quran requires methods appropriate to their developmental characteristics. This study aims to analyze the effectiveness of the right-brain-based Wafa method in teaching Quran reading to first-grade students at SDIT Al-Fatih Pangkajene. This study used a descriptive qualitative approach with a case study design. Data were obtained through observation, interviews, and documentation involving the principal, teachers, homeroom teachers, and students. Data analysis was conducted through data collection, data condensation, data presentation, and drawing conclusions. Data validity was tested through triangulation of sources and methods.The results showed that the Wafa method was implemented through enjoyable, systematic, and multisensory learning. The teacher used songs, hand gestures, colorful visual media, letter cards, audio murottal (recitation of Quranic verses), repetition, and positive reinforcement to help students recognize the hijaiyah letters, recite them, and build reading confidence. Observations showed that most students were enthusiastic, happy, and active, and were able to master the basics of the hijaiyah letters. This method also strengthens students' learning motivation, self-confidence, and emotional connection with the Quran. Thus, the right-brain-based Wafa method has proven to be relevant as an effective, enjoyable, and appropriate Quranic learning strategy for early elementary school students.

Indonesian Sign Language (Bahasa Isyarat Indonesia, BISINDO) is often misunderstood as merely a gestural or simplified form of communication, undermining its linguistic sophistication and contributing to barriers to deaf inclusion in Indonesia. Approximately 2.6 million deaf individuals in the country rely on BISINDO as their primary language. This study explores the linguistic complexity of BISINDO, documenting its phonological, morphological, syntactic, and semantic systems. Using a mixed-methods approach, we analyzed 150 hours of BISINDO conversations from 80 deaf signers, conducted experimental studies on phonological distinctions and syntactic complexity, and performed ethnographic fieldwork in deaf communities and schools. Comparative analysis with American Sign Language (ASL) was also conducted. Our findings demonstrate that BISINDO is a fully developed natural language, with 45 handshapes, 18 locations, 23 movements, and 4 orientations in its phonological system. It shows productive morphological processes, flexible word order, and rich semantics, including idioms and metaphors. However, there are significant barriers to inclusion: 68% of deaf students lack qualified BISINDO teachers, and 84% of public services lack sign language interpreters. Furthermore, 72% of hearing Indonesians still do not recognize BISINDO as a legitimate language. Deaf community organizations, however, report BISINDO as central to their identity. The study highlights the need for policy reforms, including BISINDO recognition as an official language, qualified interpreters, bilingual deaf education, and public awareness campaigns to combat misconceptions.    

Communication is a fundamental aspect of human life. However, individuals with hearing and speech impairments often face barriers in communicating with the general public. The Indonesian Sign System (SIBI) serves as a communication solution for the deaf and speech-impaired community in Indonesia, yet public understanding of SIBI remains limited. To address this issue, this study aims to develop an automatic translation model from SIBI sign language into Indonesian text by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to effectively recognize visual patterns, making it suitable for processing hand gesture images in sign language. This research involved collecting and classifying a dataset of hand images based on the alphabet or words in SIBI, which were then used to train the CNN model. The designed CNN model was built to accurately classify hand signs and translate them into Indonesian text. The results of this study have the potential to serve as a supportive solution for inclusive communication between the deaf community and the wider public, and can be further developed for contextual sentence translation. Keywords: Indonesian Sign System (SIBI), CNN, Deep Learning, Automatic Translation, Inclusive Communication

Communication is a fundamental human need for conveying information and ideas. However, individuals who are deaf and mute face difficulties in communicating with the broader community that does not understand sign language. This study aims to design and implement a real-time static sign language translator into speech using five flex sensors, an MPU6050 sensor, a Raspberry Pi Pico, an ADS1115 ADC module, and a DFPlayer Mini module as the audio output medium. Testing results show that the device successfully recognizes finger movements and hand orientation. The system is capable of playing audio output corresponding to recognized gestures, with the shortest latency recorded at 1.1 seconds and the longest at 2.8 seconds, achieving a detection accuracy rate of 75% based on 60 tests across 12 sign words. This device supports the translation of 12 simple sign words. The implementation demonstrates potential as an assistive communication tool, although further development is needed to improve accuracy, expand vocabulary, and conduct trials directly with deaf or mute users.

Indonesian Sign Language (BISINDO) serves as a primary communication medium for the deaf community; however, limited public understanding often creates barriers during daily interactions. This study aims to develop a real-time BISINDO word-level translation system using hand landmark extraction and temporal modeling with Long Short-Term Memory (LSTM). The system employs MediaPipe Hands to detect 21 hand landmarks per frame, which are then processed as sequential motion patterns to classify five BISINDO words: saya, terima kasih, maaf, nama, and kamu. A total of 250 gesture samples were recorded under controlled lighting conditions as the primary dataset. The processed sequences were used to train the LSTM model, which was subsequently integrated with an ESP32 microcontroller and a DFPlayer Mini module to produce direct audio output. Experimental results show that the model achieved an average accuracy of 86%, with precision and recall values ranging from 0.81 to 0.94. The confusion matrix analysis indicates that most gestures were correctly classified, although some errors occurred in gestures with similar initial motion trajectories. Integration testing demonstrated an average system latency of 3.8 seconds and an audio output success rate of 85%. These findings indicate that the proposed system is capable of translating BISINDO word-level gestures accurately, responsively, and consistently in real-time conditions. This study provides a strong foundation for the broader development of sign language translation systems, with potential enhancements in vocabulary expansion, multi-user datasets, and hardware optimization for deployment in real-world environments.

This study explores the meaning of love and courage in the lyrics of Nadin Amizah's song "Taruh". Music serves as a cultural medium that reflects the construction of social reality, and song lyrics often become a space for expressing personal and collective experiences. The song "Taruh" illustrates the emotional complexity present in human relationships, particularly in interpreting the feelings of love intertwined with acts of bravery. This research employs Ferdinand de Saussure's semiotic theory by examining the syntagmatic and paradigmatic relations within the song's lyrics. The descriptive qualitative method is applied, with data obtained through non-participant observation and literature study. The findings indicate that the lyrics of "Taruh" do not portray an idealized version of love; instead, they express a more realistic view of love as something that includes pain, vulnerability, and imperfection. Interestingly, within those imperfections, there is a profound sense of courage—a willingness to endure, to remain, and to accept wounds as a part of emotional life. The syntagmatic analysis highlights how the arrangement of words in each stanza reflects stages of emotional struggle, longing, acceptance, and resilience. On the other hand, the paradigmatic analysis reveals that while alternative words could have been chosen, the selected diction intensifies the emotional nuance without shifting the central message of the narrative. Overall, the song constructs a layered narrative of love, where courage is found not in grand gestures but in silent endurance. This study shows how popular music, through its poetic structure, can communicate complex emotional truths and contribute to deeper cultural understandings of human emotion.

This study aims to develop an intuitive and efficient smart home control system by utilizing hand tracking and speech recognition technologies. These technologies employ the OpenCV, Mediapipe, PyAudio, and Speech Recognition libraries to recognize hand gestures and voice commands in real-time. The system is developed using a Raspberry Pi connected to a webcam and microphone as input devices, and a relay to control electronic appliances. The results show a high accuracy rate at optimal light intensity for hand tracking and a specific distance for speech recognition. This system is implemented in an IoT environment to control devices such as lights and door locks. The research is expected to contribute to the development of smarter and more user-friendly smart homes.

The workshop "To Be A Powerful Public Speaker" is designed to improve the public speaking skills of SMA Negeri 14 Samarinda students, especially new members of the Student Council. This activity aims to increase confidence, mastery of communication techniques, and interaction with the audience. The training method includes theory, practice, and evaluation, involving speakers with various topics, such as the basics of public speaking, speaking techniques in broadcasting, and tips for becoming a professional MC. The training results showed that 85% of participants felt more confident in public speaking, increased mastery of intonation and gestures, and the ability to build better relationships with the audience. The hands-on and practical approach to this training ensures that participants understand the theory and can apply it in real life, particularly in their leadership roles. These workshops positively impact students' communication skills, supporting their personal and professional development.

The storytelling training held during the Language Month at SMK Negeri 3 Palembang aimed to enhance students’ storytelling skills as competition participants. This program employed discussion and hands-on practice to train techniques such as intonation, facial expressions, and gestures relevant to story flow. The results showed significant improvements in participants’ confidence and communication skills. However, challenges such as tight school schedules and limited mentors were identified. Evaluation suggests better planning, extended training duration, and involving more mentors to improve future program effectiveness.

The development of technology with digital image processing is often utilized to solve various problems in image processing, such as facial recognition, object detection, and interaction between users. In this study, we developed an interactive hand gesture-based game titled "Slap Mosquito" that utilizes image processing techniques to control the game through hand gestures. Using Rapid Application Development (RAD), Python, OpenCV, and Pygame methodologies, this game allows users to slap mosquitoes virtually in real-time through hand gesture recognition that is read by the camera and translated into in-game actions. RAD allows rapid development iterations and improvements based on user feedback, which is essential for improving system responsiveness and accuracy. This study focuses on detection precision, system responsiveness, and the impact of lighting on game performance, as measured using frames per second (FPS) and user gameplay results. The test results show that optimal lighting meets high detection accuracy, while low lighting conditions have a negative impact on accuracy and responsiveness. The results of this study provide insights for further development of gesture-based applications, especially regarding the importance of optimizing technical parameters and RAD methodology in improving user experience.    

This study aims to examine the role of body language in building trust in first interactions among teenagers in Bandung City. Through an ethnomethodological approach, this study analyzes various nonverbal elements such as facial expressions, hand gestures, body posture, and proxemics that influence perceptions of trust. Data was collected through participatory observation, in-depth interviews, and video analysis.  The results show that body language plays a crucial role in establishing trust at the first interaction. A genuine smile and consistent eye contact are key indicators used by adolescents to assess trust. Open hand gestures and upright posture also contribute positively to perceptions of trust.Appropriate interpersonal distance helps create a sense of comfort and trust, while a warm tone of voice and a moderate pace of speech increase perceptions of sincerity.  Local cultural norms and the influence of global trends affect the way adolescents use body language in their interactions. Gender differences were also found, with adolescent girls being more expressive in hand gestures and facial expressions, while adolescent boys relied more on body posture and tone of voice. The study concludes that understanding and effective use of body language is key in building trust in first interactions. Practical implications of the findings include developing nonverbal communication skills in adolescent education and increasing cultural awareness in social interactions.      

The purpose of this study is to explain and describe the movement structure of Sayak Dance in Air Batu Village, Ranah Pemberap District, Merangin Regency, Jambi Province. This type of research is qualitative research with descriptive methods. Research instruments are researchers themselves and assisted by supporting instruments such as stationery and cameras. Data was collected through observation, interviews, and documentation. The steps of analyzing data are data collection, data reduction, data presentation and conclusions. The results showed that the relationship of basic elements, Sayak Dance has basic elements related to the attitude of the movement of body parts, starting from the head, body, hands, feet. The attitudes and movements contained in the Sayak Dance consist of: 1) attitudes and movements seen in a) the attitude of the head is upright, upright motion on the head does not exist. 2) Posture and gestures are seen in: a) upright, upright and leaning forward posture b) Motion on the body is absent 3) Posture and hand gestures are seen in: a) attitude on the hand lift sambah b) motion on the hand open straight sambah, open right sambah, pat me up, pat the right side of the leaf, pat the bottom slap, pat the back 4) attitude and motion on the feet seen in: a) attitude of the right foot, squat, middle stance, right lift, lift left, and bend. The grammatical hierarchical relationship system contained in Sayak Dance consists of 15 motifs, 8 phrases, 4 sentences and 1 cluster. Sayak dance includes syntagmatic relationships, relationships that are like links that cannot be separated or exchanged between the sati and others.

The innovation of the robot cars today is moving very fast. The robot car is capable of being driven without using manual remote controls. One form of technological development in robot car control is by using hand gestures. The microcontrolle placed in the palm of the hand will read the direction of movement of our hands, then the instructions are transmitted to the robot car.  The transmission system used in the microcontrolled circuit in the palm of the hand and the receiver circuit inside the robot car must be properly attached. This is influenced by the quality of programming language levels and the arduino mini pro and MPU-6050 circuits. Hand gesture directional readings are able to be easily readable and directly transmitted by the NRF24L01 module via radio waves. The response power of the robot car in receiving hand-move instructions can be received as far as 45 m. In addition to using a good arduino mini pro, the addition of antenna modules will also increase the quality and distance of the connection is getting farther..