The increasing economic growth of the Indonesian people has implications for the number of vehicle ownership, especially cars. However, this increase is not matched by public understanding of the need for maintenance of the engine cooling system. One way that can be done is to use radiator coolant to maintain the cooling system so that engine performance remains in optimal condition. The purpose of this study was to determine and compare the working temperature of the engine when using several types of radiator coolant. This research uses an experimental approach research method. The test was carried out at 750 RPM, 1500 RPM, 3000 RPM and using a coolant containing a mixture of water with ethylene glycol 0%, 30%, 50% and 80%. In the calculation, it is found that the working temperature of the Toyota Avanza 1.5 M/T car engine is very good using a mixture of water and ethylene glycol 50%, namely with an average engine working temperature of 94.8℃, then a mixture of water and ethylene glycol 30%, namely with an average temperature engine working 96.3℃, a mixture of water and ethylene glycol 80%, with an average working temperature of 98.6℃ engine and a mixture of water and ethylene glycol 0%, with an average working temperature of 101.5℃

This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.This research designs a machine learning model using an Artificial Neural Network (ANN) algorithm to predict the tensile strength of aluminum. This research produces a machine learning model that has 8 (eight) input data variables consisting of the percentage of aluminum chemical composition such as Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and 1 output (output), namely aluminum tensile strength. This study makes changes to several variations of parameters, such as variations in the number of split data, training cycles, learning rates, and hidden neurons. This Artificial Neural Network (ANN) modeling produces an RMSE value of 15,383 with the best parameters being split into 60 training and 40 testing data, training cycle of 100, learning rate of 0.08, momentum 0.9, and hidden neuron 7.

This research aims to determine the market potential and business opportunities for wall decorations made of metal with CNC laser in the industry by determining the market size, evaluating competition, determining product trends, and identifying business opportunities. The quality management method of Deming's Wheel or PDCA (Plan-Do-Check-Act) is used to analyze the market potential and competition of metal wall decoration products in various marketplaces. The research results show that Shopee is the e-commerce platform that sells the most metal wall decorations in Indonesia. The sales of metal wall decorations on Shopee reached 17,830 units, followed by Lazada with 2,794 units and Tokopedia with 789 units. The most popular motif or design among consumers is the leaf motif, with a total sales of 14,600 units on Shopee. The average monthly sales on Shopee are quite high, where store A is able to sell 160 units of wall decoration. The factors that influence high sales are quick chat replies, high ratings, positive testimonials from customers, and having their own online store website. Having their own online store provides advantages for store owners, such as advertising.

The problem of Coronavirus disease (COVID-19) has not stopped. It was necessary to implement the Covid-19 protocol to avoid spreading, one of which is to wash hand regularly using and circulating water. Based on these reasons, it was still necessary to develop automatic hand washing equipment which is currently a primary need in public places, offices, and educational facilities. This study aims to modify the automatic sink using the Arduino Uno R3 microcontroller as a data processor and Ultrasonic HC-SR04 as a sensor; testing the function of the tool, namely automatic soap and water faucet, hand dryer, piezoelectric buzzer as an air level alarm, by utilizing the results of AC condensation as an water source. The results of this automatic sink modification can meet Covid-19 standards and can be used by all groups, especially schools and offices that have started to be active, so that with this tool it can reduce the spread of Covid-19. In addition, it is that the source of water from the condensation can be used as an alternative to water to save the current increasing use of PDAM water.