Floods cause economic losses, even cause loss of life. To anticipate floods and the impacts, flood prediction including early warning systems should be developed using appropriate techniques. The aim of this research is to apply the back propagation neural network algorithm for water level prediction and produce web-based flood prediction information system. The system is built using a back propagation neural network algorithm. This algorithm has 3 stages in the training process, which are forward feed, calculation, and back propagation. The used data is derived from the physics laboratory of Diponegoro University. This study concludes that the application of back propagation neural network algorithm for flood prediction can produce an accurate prediction. Therefore, this can be a reference for predicting floods significantly based on water levels in certain places. In this study obtained MSE at the first iteration of 0.0142, the smallest MSE that meets the limit of threshold of 0.000002420 and data accuracy of 98.66%. This means that generally, the back propagation neural networks application produce accurate water level prediction, which is close to the actual data.

Synchronous motors working at a capacitive power factor of 0.8 are obtained by providing a 0ver excitation, meaning operation at a capacitive power factor to improve the power factor of the system when connected to an inductive load, as is the case with an induction motor. A synchronous motor that is over-excitation will act like a capacitor and suck up the current that lead the voltage. A synchronous motor that works without a load that is given over excitation will function as a synchronous compensator whose capacitive value can be adjusted. The condition of this synchronous compensator is installed on a cage rotor induction motor as a power factor improvement. The results of this study showed that the power factor value of the induction motor before installing the synchronous compensator was cos φ = 0.88 and the reactive power was 132,84 VAR, after installing the synchronous compen sator, cos φ = 0.91 to 0.99 and the reactive power was 68 VAR to 28,2 VARs

The aim of this study is to see the health level of BRI (Bank Rakyat Indonesia) of 2018 and 2019 by CAMEL and RGEC ratio. This study is quantitative study. Collection data technique of this study are interview and documentation while analyzing data technique is through descriptive method where CAMEL is consisted of capital, asset quality, management quality, feasibility and liquidity factor while RGEC is consisted of risk profile, good corporate governance, and earning factor. Based on the result of this study, CAMEL of 2018 and 2019 are 99,41 and 99,23 categorized as “health”. RGEC of 2018 and 2019 are 96,67 percent categorized as “health”.   Keywords: Health level, CAMEL, RGEC

No abstract