Indonesia is a country with high rainfall. The rainy season can last for four months of the year. Development in urban areas in increasing resulting, resulting in reduced water absorption areas. People's habit of throwing garbage in waterways is also one of the factors that cause flooding. The current obstacle is the difficulty of monitoring water levels when heavy rains cause water to overflow onto roads and residents' houses. Checking the water level is still done manually, only looking at the measuring limit or ruler found on the river and not giving warning messages when the water level rises and it rains.  Devices are needed to monitor water levels during heavy rains and send alerts to officers when it rains and the water level has exceeded the limit. Researchers found the idea to make a water level monitor and rain detector using the Wemos D1 R2 Mini microcontroller, supported by the HC-SR04 sensor and Internet of Things (IoT) based rain sensor. The Blynk application receives data from Wemos to be forwarded as a notification to officers. This system provides real-time information about river water levels and conditions when it rains to help avoid or reduce losses due to flooding. The accuracy value generated by the ultrasonic sensor reaches 99.89%, while from the rain sensor, it reaches 100%.

LSB steganography and Vigenere chiper methods are integrated in used for data security validation in this study. This approach used Arithmetic Coding method for data compression and data decompression. To maintain the authenticity of the data file, a hash function (SHA 256) technique was added. This paper presents a prototype called Ste-Chy as a proof of concept of the combination of these techniques.This approach helps the user in terms of the exchange of confidential data through an online share in Android-based media. For the confidential authentication purpose, the confidential message is hidden togetherwith the target image. The quality of the original image and stego images in this work produce an image picture in an acceptable level for the user. The bigger the secret of the message, the compression will produce higher compression ratio. With this approach, security process of exchange of confidential message shared through the online share smartphone is considerably secured especially in an android environment.

Security to enter a system has a very important role because as the main entrance to access data sources. But often lack the attention of the owners and managers of information systems. To reduce these weaknesses, one method that is widely used today is to use One-Time password, which is where the password we have becomes dynamic, meaning that at a certain time the password is always changing, the positive side is that it makes it difficult for others to steal our passwords because besides representative passwords that are difficult to understand and passwords are always changing. This study discusses One-Time Password installed on a mobile device where the password is randomized using a combination of two algorithms, namely SHA256 and Time-based One Time Password. The development of this login method can reduce the level of theft of passwords owned by users who are entitled to access information sources.

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Clean water production has not been well considered between the balance of water use by the community and the production of clean water that is in accordance with the needs of the community. Prediction of water use in meeting the daily needs of the community is very necessary in order to be able to produce efficient water. This research can help PDAM Kota in Kalimantan to be able to produce clean water in accordance with the use of clean water by the community. The Backpropagation Neural Network method focuses on the recapitulation of water use by the community. For better prediction results, optimization is done with Particle Swarm Optimization (PSO). It is expected that the results in this study can predict community water use in daily activities. The test results showed that the Prediction results had RMSE of 0.040 with parameters for training cycle 600 values, learning rate 0.1 and momentum 0.2, and neuron size was 3 and in particle swarm optimization population size 8, max.of gene 100, inertia weight value 0.3, the value of local best weight 1.0 and global value of best weight 1.0