Dian Nugraha

The study addresses persistent communication barriers faced by students with disabilities, particularly the deaf, by exploring challenges, presenting breakthroughs, and introducing an innovative solution-a sign language translator (SLT) using motion capture technology. This groundbreaking technology, deployed through the ADDIE model and validated with user acceptance testing (UAT), successfully integrates into the learning management system (LMS) at SLB Bina Insani Depok, demonstrating its efficacy in bridging communication gaps. The results suggest a notable increase in efficiency for tasks such as t2, t3, and t5, highlighting the system’s improved ability to direct users to the LMS homepage, the SLT page, and translate words into sign language, respectively. The study suggests further development in advanced animation to enhance the learning experience for deaf students and recommends progressing toward the total communication (KOMTAL) system for comprehensive communication preparation, ultimately aiming to create an inclusive and dynamic learning platform for the holistic development of deaf students.

This article outlines the creation of a health detection system designed to identify rest tremors in Parkinson's disease (PD). The system leverages internet of things (IoT) technology to measure frequencies derived from human activities, excluding other symptoms such as heartbeat and voice recording. The core components include the Arduino Nano microcontroller and the Node ESP MCU 8266 V3 for data processing. The system employs an accelerometer sensor positioned at the body's center axis to gauge the frequency of motor symptoms associated with resting tremors, particularly when the hands are at rest in the lap. The findings indicate that 9 samples displayed symptoms of rest tremor. The recorded p-value, standing at 0.884, signifies a robust correlation between the two variables at a significant threshold of 0.01 or 1%.

Frequency modulated continuous wave (FMCW) multiple input multiple output (MIMO) radar systems employing Doppler division multiplexing (DDM) require calibrating the phase shifters to maintain optimal performance and minimize spurs all along the radar system's life cycle. Existing methods rely on a dedicated time slot to perform the phase shifters' calibration. Ideally, a method allowing for online estimation of the phase shifter imbalances without interrupting the radar operation would be desirable. In this work, a particular subset of the DDM coding scheme is analyzed, enabling such an online estimation. The proposed coding scheme and associated constellation imbalance estimator are detailed in this work and tested on the road, showing the robustness of the estimator.

The Sars-CoV-2 pandemic resulted in limited space for the public to interact and carry out their daily routines. The limited space available forces everyone to be more creative in meeting their daily needs. With currently developing technology, new information ecosystems have been developed to address problems especially in critical fields such as health, the economy and education. Artificial intelligence is now very commonly implemented in various fields, Decision Support Systems (DSS) are AI which are often found in most systems that are widely used. Many studies have been conducted using DSS. However, only a few are focused on overcoming the support system for the pandemic that has occurred since late 2019. Most of these DSS methods use outranking methods for multi-criteria parameters owned by users who require wise decisions to be decided. The outranking method has been developed in this article with the aim of obtaining a self-diagnosis system for patients with covid types of Alpha (B.1.17), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1 529) this detection system was developed using Electre and Promethee methods. Both methods utilize outranking to obtain detection results based on multi-criteria decisions of the symptoms experienced by the patient. From the system that has been developed, it is obtained data that is quite promising for the implementation of this outranking system, namely for both systems to obtain an accuracy of approximately 90% and more. This data indicates that the role of artificial intelligence, especially the decision system, is a method that is reliable enough to be implemented in MCDM (Multi-Criteria Decision Making).

In this COVID-19 pandemic season, there are still many people who tend to lack awareness in complying with health protocols, especially in terms of crowding and maintaining hand hygiene. Many innovations for hand washing facilities have been made in tourist places, restaurant toilets, malls, and even places of worship. However, during the current Covid-19 pandemic season, have these placements and tools been able to break the ropes of spreading the virus? The answer is that it is still not enough, because the hand washing place is still touched by many hands. Even though some have used automatic or touchless hand washing stations, the purchase of these tools is quite expensive among the surrounding community. Therefore, in this project we intend to break the rope of the spread of the coronavirus, by designing an economical touchless hand washing sink. Then, after the tool was completed, the grant process to the party in need, namely SMK Global Prima in Tambun Utara, Bekasi Regency, was carried out. Although on a small scale, we hope that this effort can be useful for the students involved, the partners of SMK Global Prima, and the community in general.