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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 research presents the design and testing of an automatic color detection system using TCS3200 color sensor integrated with Arduino Uno microcontroller. The system was developed and tested using Wokwi virtual simulation platform before physical implementation. The TCS3200 sensor converts RGB light intensity reflected from objects into frequency signals, which are processed by Arduino Uno to classify colors into red, green, and blue categories. The system incorporates audio feedback using DFPlayer Mini module to provide sound notifications for detected colors. Testing results show that the system can accurately detect and classify primary colors with frequency-based thresholds: red (R<48 &R>37 & G<95 & G>85), blue (G<75 & G>65 & B<33 & B>23), and green (R<55 & R>40 & B<25 & B>5). The simulation validation demonstrates stable performance with consistent color recognition capabilities, making it suitable for industrial sorting applications and assistive technology for visually impaired individuals.

Sleep disorders such as insomnia are health problems that have a serious impact on quality of life. This research aims to design and build a brain wave stimulator based on Audio Visual Stimulation (AVS) as a non-invasive solution to help improve sleep quality. It utilizes a synchronized combination of light and sound, controlled by an Arduino NANO microcontroller, and supported by a DFPlayer Mini module, speakers, and blue LEDs as a stimulation medium. The research methods used are applied research with observation stages, literature studies, system design, tool implementation, and function testing. Functional tests were performed five times in a span of 15 minutes after the device was activated. The results of the voltage measurement on each component also show that the performance of the tool is within normal limits according to the datasheet. Thus, this tool successfully functions as designed and has the potential to be a viable solution to improve sleep quality.

The Internet of Things (IoT)-based automatic bell system is designed to enhance the efficiency of bell operations in educational institutions by utilizing modern technology. This research aims to develop a system that can control the bell automatically according to the class schedule, while also enabling remote control via a mobile application using the Blynk platform. The system is built using an ESP8266 as the main microcontroller, a DFPlayer Mini module for audio playback, and an RTC DS1307 for time management. The results show that the system functions as expected, both in automatic mode based on the schedule and in manual mode through the mobile application. Testing and debugging demonstrated that integration with WiFi networks allows for flexible and effective bell control. For further development, it is recommended to add a power backup feature, web interface, and push notifications to improve system reliability and flexibility. This system provides an efficient and practical IoT solution for automating bell operations in educational environments.

A musical instrument is a device created to produce sound. The price of musical instruments themselves varies depending on the type of instrument and the quality of the material used to make the instrument. At NU 07 Brangsong Middle School, there were no instruments except tambourines. That is because there is no funds to buy a musical instrument. The school allocates more funds to buildings or school buildings and scholarship assistance for underprivileged children and intelligent children. Thus students at the school cannot practice or hone their skills in music. From these problems can actually be solved by an arduino-based Piano, Harp, Marching Bell Digital instrument using laser light and LDR. Because this tool can be made at a low cost and can function more than one musical instrument. The working system of this tool is that there are several lasers that will emit light directly on the LDR sensor (in pairs). And when a pair of laser beams and LDR number one are blocked by an object or hand it will emit a piano sound with a "DO" motif. Furthermore, when a pair of laser beams and LDR number two are blocked, it will emit piano sound with the "RE" orientation and so on. This tool is also equipped with buttons that function as a selector of the type of sound that will be generated and the desired octave.