Human face recognition is a vital biometric sign that has remained owing to its many levels of applications in society. This study is complex for free faces globally because human faces may vary significantly due to lighting, emotion, and facial stance. This study developed a mobile application for face recognition and implemented one of the convolutional neural network (CNN) architectures, namely the Siamese CNN for face recognition. Siamese CNN can learn the similarity between two object representations. Siamese CNN is one of the most common techniques for one-shot learning tasks. Our participation in this study determined the efficiency of the Siamese CNN architecture with the enormous quantity of face data employed. The findings demonstrated that the suggested strategy is both practical and accurate. The method with augmentation produces the best results with a total data set of 9000 face images, a buffer size of 10000, and epochs of 5, producing the minimum loss of 0.002, recall of 0.996, the precision of 0.999, and F1-score of 0.672. The proposed method gets the best accuracy of 98% with test data. The Siamese CNN model is successfully implemented in Python, and a user interface and executables are built using the Kivy framework.

<span lang="EN-US">Indonesia is a major exporter and producer of coffee, and coffee cultivation adds to the nation's economy. Despite this, coffee remains vulnerable to several plant diseases that may result in significant financial losses for the agricultural industry. Traditionally, plant diseases are detected by expert observation with the naked eye. Traditional methods for managing such diseases are arduous, time-consuming, and costly, especially when dealing with expansive territories. Using a model based on transfer learning and deep learning model, we present in this study a technique for classifying Robusta coffee leaf disease photos into healthy and unhealthy classes. The MobileNetV2 network serves as the model since its network design is simple. Therefore, it is likely that the suggested approach will be deployed further on mobile devices. In addition, the transfer learning and experimental learning paradigms. Because it is such a lightweight net, the MobileNetV2 system serves as the foundational model. Results on Robusta coffee leaf disease datasets indicate that the suggested technique can achieve a high level of accuracy, up to 99.93%. The accuracy of other architectures besides MobileNetV2 such as DenseNet169 is 99.74%, ResNet50 architecture is 99.41%, and InceptionResNetV2 architecture is 99.09%.</span>

Challenges in food production in the future are certainly more complex in developing countries like Indonesia. The Agricultural Research and Development Agency developed an Integrated Planting Calendar (Katam) information system to decide the water discharge for rice planting time using rainfall-runoff modeling called GR4J (Genie Rural a four parameters Journalier). This study aims to improve the accuracy of the GR4J model for determining water discharge in an area. The study areas in this study were the Progo Watershed and the Wuryantoro Watershed. The GR4J model is measured based on four free parameters in the form of Maximum Capacity Production Store (X1), Coefficient Changes in Groundwater (X2), Maximum Capacity Routing Store (X3), and Peak Time Ordinate unit hydrograph (X4). The four parameters are optimized using Particle Swarm Optimization (PSO). This study shows that the parameter optimization of the GR4J model with PSO was successfully carried out. As a determinant of the model's success, the Nash-Sutcliffe Efficiency (NSE) equation and the Root Mean Square Error (RMSE) is used. We have found that NSE is increasing and RMSE decreases in RMSE in each watershed after using PSO optimization. The area affects the accuracy of the GR4J model. The smaller the area, the more it can show the characteristics of a watershed. Using the PSO algorithm in the GR4J model will be more effective than using manual or trial methods based on expert knowledge because it can achieve optimization quickly.

In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research, 5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation, and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf diseases, as shown by the system's implementation outcomes.

The farmer’s term of trade (NTP) is used by the Central Bureau of Statistics (BPS) as one of the indicators for measuring the level of welfare or purchasing power of farmers. To prepare preventive measures when the NTP index falls from the previous period, the relevant parties need to predict NTP for the coming period. The purpose of this study is to measure the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting NTP of West Sumatra Province in the coming month. The data used are monthly NTP data of West Sumatra Province obtained from the BPS website www.sumbar.bps.go.id from 2013 to 2016 for the network training process. Model evaluation was done by comparing the test results with actual data in 2017. Forecasting systems are divided into two types, namely time series model and the multivariate model. The test results show that the time series model has the smallest RMSE of 0.3430 for the training set and the RMSE value is 1.4570 for the testing set.