Background: Human face is a biometric feature. Artificial Intelligence (AI) called Artificial Neural Network (ANN) can be used in recognising such a biometric feature. In ANN, the learning process is divided into two: supervised and unsupervised learning. In supervised learning, a common method used is Backpropagation, while in the unsupervised learning, a common one is Kohonen Self Organizing Map (KSOM). However, the application of Backpropagation and KSOM need to be adjusted to improve the performance.Objective: In this study, Backpropagation and KSOM algorithms are rewritten to suit face image recognition, applied and compared to determine the effectiveness of each algorithm in solving face image recognition.Methods: In this study, the methods used and compared in the case of face image recognition are Backpropagation dan Kohonen Self Organizing Map (KSOM) Artificial Neural Network (ANN).Results: The smallest False Acceptance Rate (FAR) value of Backpropagation is 28%, and KSOM is 36%, out of 50 unregistered face images tested. While the smallest False Rejection Rate (FRR) value of Backpropagation is 22%, and KSOM is 30%, out of 50 registered face images. The fastest time for the training process using the backpropagation method is 7.14 seconds, and the fastest time for recognition is 0.71 seconds. While the fastest time for the training process using the KSOM method is 5.35 seconds, and the fastest time for recognition is 0.50 seconds.Conclusion: Backpropagation method is better in recognising face images than KSOM method, but the training process and the recognition process by KSOM method are faster than Backpropagation method due to the hidden layers. Keywords: Artificial Neural Network (ANN), Backpropagation, Kohonen Self Organizing Map (KSOM), Supervised learning, Unsupervised learning

This paper presents the development of a new algorithm in the field of image processing that enables the detection of flood disasters quickly and accurately, using the SONIC (Smart water indication optimizer) method. Concentrated detection in online real-time camera systems has been performed by several tests consisting of classifying camera objects, thermal cameras, and learning cameras. The introduction to the RTC web enables real-time and multiplatform data delivery systems on devices comprised of computers and android gadgets, on object classification using the SONIC algorithm. The object consists of humans, yellow balls, and green balls, with each sample having 50 points of view. The experiments showed test results up to 100% per age with real-time camera capture speeds.

Approach Light is an aircraft visual landing aid in a certain form of lighting to assist pilots when landing an aircraft in the dark or bad weather (below average visibility) in order to land safely. With the important role of the Approach Light in the aircraft landing process, the ON and OFF lighting condition of the Approach Light is necessary to be monitored. The design of this research uses artificial intelligence technology that can determine whether the lights on the Approach Light are in ON or OFF condition using camera's image capture. To find out whether the lights are on or not, Convolutional Neural Network is implemented in this monitoring technique to process image classification oh the lights. It can also send evidence in the form of captured images classified on the website as evidence of monitoring results that can be confirmed by technicians if any inappropriate classification results occurred. The results showed that the classification results for each brightness step obtained average values of 95% in accuracy, 90% in prediction precision, and 98% in prediction sensitivity. According to this good result of values, it is expected to give positive contribution for the technicians so that flight operations disruption can be minimized.

<span lang="EN-US">This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation function for the hidden layer, while the linear transfer function is the most appropriate activation function for the output layer. Based on the results of training and forecasting the USD against IDR currency, the Elman BPTT method is better than the Jordan BPTT method, with the best iteration being the 4000^th iteration for both. The lowest root mean square error (RMSE) values for training and forecasting produced by Elman BPTT were 0.073477 and 122.15 the following day, while the Jordan backpropagation RNN method yielded 0.130317 and 222.96 also the following day.</span><p> </p>