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An electrostimulator is a medical device designed to deliver controlled electrical stimulation to nerves and muscles, supporting rehabilitation and therapy for patients with neuromuscular disorders. This study focuses on designing and developing a portable electrostimulator that offers three distinct waveform modes: continuous wave, discontinuous wave, and dense-disperse wave, providing versatility for different therapeutic needs. The device is powered and controlled by an Arduino Mega 2560 microcontroller, coupled with a Nextion touchscreen LCD interface that allows users to adjust waveform type, frequency, and stimulation intensity with ease. Waveforms are generated through an NE555 IC circuit, with amplitude adjusted via a potentiometer and subsequently amplified using a step-up transformer to achieve therapeutic voltage levels. Functionality and performance tests were conducted using an oscilloscope, and the device was benchmarked against a commercial KWD-808 electrostimulator. Results demonstrate that the developed electrostimulator reliably produces the intended waveforms, achieving peak voltages up to 32V and frequencies ranging from 33.3 Hz to 66.6 Hz, confirming its effectiveness and feasibility for non-clinical nerve and muscle therapy applications.

Public Fuel Filling Stations (SPBU) are important facilities that provide various types of fuel such as gasoline, diesel, and Pertamax to meet the needs of motorized vehicles. The existence of SPBU greatly helps the public in obtaining fuel at a more economical price compared to purchasing retail. However, the transaction system at SPBU generally still uses conventional methods, such as cash payments or the use of debit/credit cards that have not been fully integrated with an efficient digital system. The use of RFID (Radio Frequency Identification) technology has been implemented as a non-cash transaction method at several SPBUs, but this system still has various weaknesses, such as limited device compatibility and delays in transaction processing. This prompted the author to develop the concept of an independent SPBU based on modern technology that is more efficient and secure. The proposed innovation includes the use of contactless smart cards and coin acceptors for the payment system, allowing users to make self-service transactions without operator involvement. In addition, the author also added several supporting components such as proximity sensors, which function to detect the presence of vehicles or people around the SPBU area. These sensors can help in saving electrical energy by activating the system only when needed. Another component is a vibration sensor, which plays a crucial role in detecting excessive vibrations that could potentially cause leaks. If excessive vibration is detected, the system automatically closes the solenoid on the pump to prevent the risk of fire or damage. By integrating this technology, the autonomous gas station system is expected to improve operational efficiency, user convenience, and safety during the automatic refueling process. This development is expected to be an innovative solution for modernizing the gas station system in Indonesia.

Unmanned Surface Vehicle (USV) is an unmanned vehicle that is currently being developed in Indonesian waters for the purposes of depth surveys in waters that combine autonomous navigation frames, environmental sensors, multibeam echosounders to collect topography, temperature, and other data that are utilized for the world of shipping. Researchers tested an unmanned ship tool that can move automatically and can determine the direction to a predetermined location. Output. The test results show that the unmanned ship can walk to the planned route. This unmanned ship is equipped with a Micro Air M10G GPS sensor, LoRa Sensor, Arduino Mega 2560 Pro Sensor... In testing as many as 15x experiments were carried out including 10x experiments carried out to turn right and 5x experiments with turning left. The experiments carried out were very effective and according to what had been planned, but there were 4x experiments that had differences from 11x experiments due to water waves and winds that were too large so that the ship was unstable.

The increase in online buying and selling due to government appeals during the Covid-19 pandemic has created a new habit in society, which also serves to reduce physical contact and prevent the spread of the virus. However, with the increase in delivery of goods, challenges arise in maintaining the cleanliness and safety of received packages. To address this, a smart package receiving system was developed that features automatic sterilization using ultraviolet (UV) lights and disinfectant spraying on incoming packages. This system aims to prevent the transmission of viruses that may be attached to the package. In addition, the system helps monitor and secure packages, especially when the owner is not at home. The system uses a microcontroller as the control center, equipped with an ESP32-CAM camera to document photos of the sender, as well as an ultrasonic sensor to detect whether the package is in the box or not. The process starts when the courier presses the “there is a package” button, which sends an OTP code to the receiver's Telegram. The courier enters the OTP code, places the package in the box, and closes it. After that, the UV lamp turns on for sterilization, the ESP32-CAM photographs the package, and the ultrasonic sensor detects the package status. If the package is detected, a photo notification of the sender is sent to Telegram, informing the recipient that the package is ready for pickup.  

Coffee is an agricultural product highly favored by the majority of the Indonesian population. Coffee comes in various types, each with a distinct aroma. Currently, differentiating coffee types relies on conventional methods, using the human nose, which is shubjective and dependent on individual conditions. This approach is less effective. To address this, researcher have developed a machine learning based system using Arduino Mega 2560 as the microcontroller, gas sensor MQ4, MQ7, MQ135, as the aroma detection tools, and the Linear Discriminant Analysis (LDA) algorithm as the classification method. The results of this system can classify 5 types of coffee with an accuracy rate of 90%.