Lithium-ion battery management is crucial as their use grows in devices and electric vehicles. A key aspect is State of Charge (SoC) estimation, which indicates the battery's charge level at any given time. This research aims to develop a method that can provide accurate SoC estimates for Li-ion batteries using the Particle Swarm Optimization (PSO) method. In this research, a 12V 8.4 Ah Lithium-Ion battery was used as a test subject, utilizing a voltage sensor, ACS712 sensor, and LM35 temperature sensor to measure key parameters such as voltage, current, and temperature. The PSO approach was chosen because of its ability to find optimal solutions in complex search spaces, such as SoC estimation in batteries. Through a combination of the PSO algorithm and data generated from sensors, it is hoped that the SoC estimates produced can improve battery usage efficiency, extend service life, and increase the performance of systems that depend on batteries. PSO can provide more accurate predictions with smaller errors, both in terms of the RMSE value of 0.0391 and the MAPE value of 12.028%. The high accuracy of 87.972% of PSO also shows that this method is reliable for applications that require precise SoC predictions. It is hoped that the results of this research can become a basis for further research in the field of battery management and metaheuristic algorithm optimization. After all, this research aims to enhance battery management systems and deepen understanding of PSO-based SoC estimation.

This paper examines the performance of photovoltaic panels integrated into the electrical grid in a load-sharing scheme at Darool Ehsan Muhammadiyah Senior High School, Sragen. The study is part of Universitas Muhammadiyah Surakarta's community service program focused on renewable energy development in educational environments. Unlike previous studies relying on simulated data, this research uses real-time primary data from direct measurements of photovoltaic power production and grid electricity consumption over a specific period. The study’s innovation lies in analyzing the photovoltaic system within a tropical climate-based load-sharing scheme and comparing energy usage when connected to photovoltaic panels versus when disconnected. It also evaluates the impact of reducing solar energy consumption from the grid. The collected data reveal an estimated daily load of 49.89 kWh, with PLN-supplied energy consistently exceeding inverter-supplied energy. Integration of photovoltaic panels into the grid reduces PLN electricity consumption by up to 30% during optimal sunlight periods, achieving an average system efficiency of 15%. This research offers valuable insights into the potential and challenges of implementing photovoltaic panels in educational settings, emphasizing the importance of climate considerations in system design and optimization. Future studies will focus on techno-economic evaluations of solar power installations and harmonic distortion (THD) analysis for larger-scale implementations.

A monitoring system for fuel availability at Base Transceiver Stations (BTS) is an absolute necessity. The fuel availability in the generator set must be guaranteed because it ensures the energy supply and continuous operation. This research purpose is to create a robust and effective sensor that can be monitored in real-time so that industry players can estimate the consumption of fuel at the BTS in a certain period. The novelty of this research is that instead of looking at the amount of fuel consumption/reduction like the previous research, this study focuses on how much diesel availability is in the generator tank. So, calculating fuel requirements and predicting actual consumption are done directly. The sensor is a reed-switch oil level sensor and is connected to ESP32 as the microcontroller. It's installed horizontally and uses an optimum distance between sensors of 2.5 mm to improve the sensor reading's accuracy. The test results of converting voltage values into ADC signals on sensor readings produced a determination coefficient of 0.9987, which showed the reliability of the sensor-level design. The sensor precision test results produced an RSD value of 0.17% and the accuracy test produced an error range of -3% to 2% with an average error value of 1.59%. These results indicate that the designed level sensor has good precision and accuracy. Through this research, the provider can predict and plan their fuel consumption needs for BTS power supply and increase the Domestic Component Level in the sensor industry.

The large-scale implementation of analog switch-off for television broadcasting in Indonesia has led to blank spots in several regions. To address this issue, an optimal propagation model is needed. Proper selection and analysis of the channel model can enhance transmitter coverage, increase coverage percentage, improve energy efficiency, and boost field strength due to optimal transmit power. Previous studies have explored various DVB-T2 propagation models, notably the ITU-R P.1812-4 and Longley-Rice models, which are sophisticated and consider various environmental parameters, making them suitable for diverse broadcasting conditions. This research introduces a novel approach by specifically focusing on the urban context of Semarang City, Indonesia, to reduce blank spots by applying the ITU-R P.1812-4 and Longley-Rice propagation models. This study uniquely compares the two models to determine the most effective one for this urban area. Results indicate that the ITU-R P.1812-4 model provides a higher field strength value than the Longley-Rice model, with average field strengths of 108.3425 dBμV/m and 108.2325 dBμV/m, respectively. The difference in average field strength of 0.11 dBμV/m, despite having the same free space loss value of 100.9025 dB, indicates that one model has a slightly stronger signal. This stronger signal can improve coverage by reaching further distances and penetrating obstacles better. Additionally, a stronger signal means less power is needed to maintain the same coverage area, thus improving energy efficiency. This research not only offers empirical data specific to Semarang City but also provides insights that can guide future digital broadcasting optimizations in similar urban environments.

The grounding system is one of the security systems in substations to channel excess voltage caused by lightning strikes that occur at substations, as well as electrical equipment. To design a grounding system, there are several factors that must be considered, including the type of soil, the configuration of the grounding system, the resistivity of the soil, and the condition of the surrounding environment. The aim of this research is to determine the safe limits in the substation grid grounding system which is influenced by the type of soil and influences the touch voltage and step voltage, which can be dangerous to humans. The method used is simulation with the Finite Element Method (FEM) in the ETAP application. Finite Element Method (FEM) is a method that uses image mediation by assuming that the grounding system is an equipotential structure. The test results were deliberately carried out with larger substation area parameters and different soil types, so that the differences between the influence and appearance of touch voltage and step voltage can be seen clearly.

An electric motor is an electromagnetic machine commonly utilized across various industries and automotive products. One prevalent type of electric motor employed in electric vehicles is the Permanent Magnet Brushless DC Motor (PM-BLDC), a brushless motor employing permanent magnets. However, despite its efficiency, permanent magnet motors often experience vibrations that can disrupt their performance. This research aims to optimize the existing BLDC motor design, with a specific focus on reducing the existing cogging torque. Initially, the existing design exhibited a cogging torque level of 0.21482 Nm. The optimization process involved modifications to several key design parameters, such as air gap, magnet thickness, magnet type, and slot opening width. In previous research, only comparisons were made between stator slot designs, which proved to be less effective as significant differences were not evident in the results of the comparative analysis of BLDC motor designs. So, in this research, the Taguchi method was utilized for the optimization process due to several advantages it offers. Through an analysis of means and variance, the optimization process successfully achieved a significant reduction in cogging torque by 0.099744 and an increase in efficiency by 0.6%. The results of the optimized permanent magnet BLDC design indicated a cogging torque value of 0.115072 Nm and an efficiency of 86.64% at an operational motor speed of 1500 rpm. This research provides a substantial contribution to the development of more efficient electric motors suitable for various applications.

In the process industry, a control system is important to ensure the process runs smoothly and keeps the product under predetermined specifications. Oscillations in process variables can affect the decreasing profitability of the plant. It is important to detect the oscillation before it becomes a problem for profitability. Various methods have been developed; however, the methods still need to improve when implemented online for multi-oscillation. Therefore, this research uses a machine learning-based method with the K-Nearest Neighbour (KNN) algorithm to detect multi-oscillation in the control loop, and the detection methods are made to carry out online detection from real plants. The developed method simulated the Tennessee Eastman Process (TEP), and it used Python programming to create a KNN model and extract time series data into the frequency domain. The Message Queuing Telemetry Transport (MQTT) communication protocol has been used to implement as an online system. The result of the implementation showed that two KNN models were made with different window size variations to get the best performance model. The best model for multi-oscillation detection was obtained with an F1 score of 76% for detection.

One of the most favorable frequency bands for 5G technology is the 27.5 - 28.5 GHz band which has been used by Fixed Satellite System (FSS) service in the uplink direction. This potentially causes interference between the two systems. This study aims to analyze the interference that occurs between satellite earth stations and 5G access points (AP), and between 5G AP and satellite sky stations. The analysis is carried out based on simulations using the Spectrum Engineering Advanced Monte Carlo Analysis Tool (SEAMCAT) software, with two scenarios. The first scenario is to analyze interference between the 5G AP and the FSS sky station. With the C/I interference criterion of 40.2 dB, the simulation results show that the 5G AP will not interfere with the FSS sky station. The second scenario is the interference simulation between FSS earth station and 5G AP. The simulation is carried out by varying the distance between the earth station and the 5G AP, the height of the earth station, and the height of the 5G AP. Based on the simulations, it is shown that the FSS earth station can interfere with 5G AP with a probability of up to 60%, so it is necessary to adjust the distance between systems and the height of the antenna to minimize the interference. The shortest distance needed to minimize interference is 36 km with an earth station height of 5 meters.

Quadruped, a robot that resembles four-legged animals, is developed for many purposes, such as surveillance and rescue. Such a caveat requires the robot to have the capability to overcome various terrain and obstacles. When moving across such a landscape, it is essential to maintain the robot's orientation steadily. Inclined terrains such as stairs have posed another challenge to the control strategy as the robot is unstable while climbing. Therefore, the contribution of this work is to address the need for heading control because of the relatively longer stairs used for the current competition compared to the past. The proposed control system simultaneously maintains the heading while keeping the body stable. The inertial measurement unit sensor carried by the robot would provide the pose needed for heading control calculations. The robot's heading becomes the base for the PD controller calculation. The final pose that stabilizes the robot while tackling heading error is a combination of correction from the PD controller and the stabilization part of the control strategy. Then, the leg servo angle determination deployed the inverse kinematics calculation from the suitable robot pose. The proposed method enabled the designed robot to maintain its heading with a 4.4-degree margin of error and stabilize the body. The quadruped also completes the stair climbing at the shortest time of 20 seconds with a speed of up to 5.5 centimeters per second.