This study examines the application of the Lagrange method and Particle Swarm Optimization (PSO) in solving the Economic Dispatch problem at the Tanjung Jati B Steam Power Plant (PLTU). The main objective is to compare the effectiveness of both methods in optimizing load distribution among generation units and to evaluate potential operational cost savings compared to actual operating conditions. The optimization model minimizes a quadratic generation cost function, subject to system load demand and the power output limits of each unit. Simulation data includes daily load distribution and fuel-related parameters such as calorific value and fuel price. The results show that the Lagrange method yields an average power output of 2,367 MW, equal to the real condition, while PSO produces 1,966 MW, which is 16.94% lower. In terms of cost savings, PSO achieves an average saving of IDR 938,501, while the Lagrange method and real operation yield average savings of IDR 1,078,064 and IDR 1,073,076 respectively. In percentage terms, Lagrange shows 12.95% higher savings than PSO and 0.46% higher than the actual condition. This study highlights the importance of integrating optimization methods in power plant operations to improve efficiency and reliability of electricity supply.

As a resource supporting public health services, management of electromedical equipment must be carried out quickly, accurately and integratedly so that function, safety, security and benefits can be optimized. The management of electromedical equipment is regulated in the Republic of Indonesia Minister of Health Regulation Number 65 of 2016 concerning Electromedical Service Standards. The expected result of this research is an expert system that can accurately detect damage to sterile electromedical equipment, especially autoclaves. This expert system can then assist electromedical technicians in finding damage and as an assessment in making decisions for appropriate and validated actions to be taken. From the results of testing and analysis of the expert system for detecting damage to Autoclaves using the fuzzy logic method, the following conclusions were obtained. The damage detection expert system application in the Autoclave has been proven to be able to provide 100% diagnosis results. This system can assess the degree of damage of 11.6235981%. based on the input symptoms provided, thus providing decisions that are close to the actual conditions. The expert system built is able to speed up the damage detection process compared to manual methods. This helps in taking quicker action to prevent further damage to the autoclave.

Based on statistical data, every year there is an increase in population. This also happened to the increasing number of Catholics. The increase member in Saint Aloysius Church of Mojosongo isn’t accompanied by an increase of seating facilities, to solved this, a device was designed which will show seats that have been used or still empty through a viewer that will be placed near the entrance. The research method used in this study is a quantitative method. The number of seats used by in one block is 25 seats, the seat sample is 9 seats. Three (3) PIR sensors are placed above the row to the seat 2, where the direction of the PIR sensor is drived by a stepper motor with an angle is 22.61°. Detection alternated and sequentially starting from row A, row B and row C. In general, the seat detection system based on ATMEGA16 works according to the design. People can find an empty seat position by looking at the viewer (LED) and looking for LED lights that are not lit.