Vocational high school students majoring in engineering, who are the successors to technology developers in society, need hard skills to enter the workforce. However, the students of SMK Negeri 2 Bangkalan have not received knowledge about embedded systems. In supporting this need, Master in Applied Informatics and Computer Engineering Program at Politeknik Elektronika Negeri Surabaya held a community service program by holding practice-based training on Arduino Uno-based embedded systems. This program is aimed at vocational high school students so that they can improve their abilities and skills in hardware programming and embedded. The method used in this activity is workshop-based training. The workshop includes a basic explanation of recognizing Arduino and embedded systems, a simple Arduino circuit practicum, and questions and answers between tutors and trainees. The results achieved from this activity are indicated by the results of the training evaluation questionnaire. In general, the training material is considered to be in accordance with the need to add insight, knowledge, skills, and expertise. In addition, the practice-based training on Arduino Uno-based embedded systems has provided benefits for students to use technology in their daily lives.

The Social Force Model (SFM) is a popular navigation technique for mobile robots that is primarily used to simulate pedestrian movement. The SFM method's drawback is that several parameter values, such as gain, k, and impact range, σ, must be determined manually. The reaction of the SFM is frequently inappropriate for certain environmental circumstances as a result of this manual determination. In this paper, we propose employing the Fuzzy Inference System (FIS), whose rules are optimized using a Genetic Algorithm (GA) to manage the value of the gain, k, parameter adaptive. The relative distance, d, and relative angle, α, concerning the robot's obstacle are the inputs for the FIS. The test results using a 3-D realistic CoppeliaSim demonstrated that the learning outcomes of FIS rules could provide adaptive parameter values suitable for each environmental circumstance, allowing the robot to travel smoothly is represented using the robot’s heading deviation which decreasing by and reaching the goal 1.6 sec faster from the starting point to the goal, compared to the SFM with the fixed parameter value. So that the proposed method is more effective and promising when deploying on the real robot implementation.

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