As a maritime country, shrimp commodity production in Indonesia is very high and continues to increase. However, because shrimp is a perishable food, we need a detection device. This is because conventional methods that are widely used by the community in detecting freshness of shrimp are only based on the smell. Of course, this is a problem when shrimp are packed in closed containers. In this paper, a method for detecting shrimp is proposed using the Melastoma malabathricum L. - based label indicator. The high content of flavonoids in the extracts allows the changing the colour of the label from red to grey due to the interaction between the label with the OH- group that arises from the shrimp spoilage process. The colour that appears on the label indicator will correlate with the level of shrimp freshness. By increasing detection effectiveness, the classification is performed using the nearest-neighbours algorithm, which is equipped with an image processing mechanism in the form of colour quantization. There are four classifications used to express the quality of shrimp, namely "acceptable," "just acceptable," "unacceptable," and "more unacceptable." The accuracy of applying this method is 71.9%, with the majority of detection errors occurring in the "acceptable" class. Based on these results, it can be stated that the label indicators prepared in this study are very promising to be developed into intelligent packaging components.

The use of solar energy to satisfy renewable energy in Indonesia has great potential. To improve the efficiency of solar collectors, some researchers proposed solar collectors that equipped by nanofluids as working fluids. CuO-based nanofluids are known as nanofluids that have excellent conductivity. The purpose of this study is to synthesise CuO nanofluids through egg white mediation methods, and it will be applied to simple distillation systems. Generally, the synthesising process is arranged based on the modification of the sol-gel method, and by the calcination, it will leave metal oxides, namely CuO-nanoparticles. The calcination temperature variation used is 150℃; 300℃; 500℃ and 700℃. According to Debye-Scherrer equations, it is known that, in optimal condition, we obtain CuO crystallite by the size is 42 nm. The use of CuO nanofluids can increase the conductivity of fluids and can increase the effectiveness of ethanol distillation by 81%.

The national need for advanced materials is spread across several fields, such as industry, transportation, information systems and military defense systems. In the field of transportation, the government continues to innovate by shifting fuel-based vehicles (BBM) towards environmentally friendly cars in the form of electric cars. However, the main challenges in developing this electric car are the electric energy charge storage system which is durable, has high storage capacity, and short charging time. In this study, carbon was activated gradually using a 7% H3PO4 solution followed by heating at a temperature of 700oC for 3 hours under nitrogen gas (N2). The results of the FTIR analysis showed that the activated carbon N2 gas had a strong and sharp C=C group along the absorption band 1500 cm-1 – 1400 cm-1 which indicated that the activated carbon had high quality and purity. This result was strengthened based on the EDS spectra which showed a carbon content of 90.56%. In addition, based on SEM micrographs, it can be observed that the morphology of the carbon produced has pores scattered on the surface. The presence of these pores will provide space for the transfer of charge or ions to the electrode. The best electrode was obtained on activated carbon with N2 gas activation treatment with a conductivity of 3.92 x 10-2 S/m and a capacitance of 1.44 x 10-3 F, so it was concluded that activated carbon muntok white paper can be used as an electric charge storage material.

The main weakness in shrimp marketing is the perishable food nature of shrimp. Generally, people identify the freshness of shrimp by direct observation. However, it will be difficult to detect the freshness of shrimp if it is marketed in a closed container. In this study, a label indicator of purple sweet potato will be made to detect the freshness of shrimp. The increase in the efficiency of indicator readings is carried out using a neural network algorithm. The results of the sensitivity test showed that the label indicator of purple sweet potato extract was sensitive to the presence of ammonia.Through a comparison between the storage time of shrimp and the organoleptic quality of shrimp, it is known that the quality of shrimp is divided into four classes, namely: (i) "Very fresh" marked with a solid red color (ii) "Fresh marked with a deep blue color (iii) "not fresh marked with a dark red color. gray and (iv) “very unrefreshing marked with a faded brown color. Through label indicator image classification using a neural network algorithm, from 73 training data obtained an accuracy rate of 95.89% and a precision of 92%.