No abstract

The development of software in a wide range of remote locations by large developers generates problems, in which a number of bugs or errors appear after deployment. Therefore, the solution is developing a monitoring application based on secure software development and risk assessment concept on Software Development Life Cycle (SDLC). The developed concept is based on mapping result of touchpoints for secure software, and NIST SP 800-30 for risk management in each stage of SDLC. The measures particularly support developer teams in remote distributed environments. The focus is on the risk assessment performed during the stage of initialization to implementation. In addition, according to the mapping results as well as business process analysis, there are five main functions related to this study, including creating projects, designing process, developing process, testing process, and deployment. Additionally, a web-based monitoring application is implemented to secure the software development process based on security control procedures at each stage, and developed using PHP programming languages and MySQL for a database. Moreover, the application is triggered by five parameters: software type development, tools, database structures, module names, and errorrelated problems. From these parameters, the risks could be discovered and subsequently categorized into four types of risk, such as low, medium, high, and critical, based on the impact of each risk. The results signify that the number of supports significantly decreased by 80%. Correspondingly, this application is expected to support secure software development as well as provide efficient treatment for possible errors and security risks.

No abstract

TRAINING ON THE USE OF AGRICULTURAL MOBILE APPLICATIONS FOR FARMERS IN PANYOCOKAN VILLAGE. The prospect of using smartphones is increasingly expanding, including for agricultural businesses. This study shows how important it is to carry out skills training for agricultural business actors in using smartphones that are directed at the use of special applications to support agricultural businesses. This needs to be done as an effort to support the improvement of skills and knowledge in agriculture in Panyocokan Village, Ciwidey District, Bandung Regency. The target participants in this activity are farmers, extension workers, and agricultural business actors who have difficulty in obtaining the information they need about agriculture. Information that will be provided in the application includes varieties, cultivation methods, fertilization, plant-disturbing organisms, pesticides, pest control, pesticide spraying, post-harvest, crop price information, and consultation with experts. The training method is carried out by online learning which begins with data collection, situation analysis on the potential of the agricultural and plantation sectors, preparation of training scenarios, training implementation, and evaluation of training results. This research resulted in an increase in skills in using smartphones to support agricultural businesses with the support of training materials and application manuals.

This paper contains the development of a smart power device designed to collect load power data from industrial manufacturing machines, predict future variations in load power data, and detect abnormal data in advance by applying a machine learningbased prediction algorithm. The proposed load power data prediction model is implemented using a Long Short-Term Memory (LSTM) algorithm with high accuracy and relatively low complexity. The Flask and REST API are used to provide prediction results to users in a graphical interface. In addition, we present the results of experiments conducted to evaluate the performance of the proposed approach, which show that our model exhibited the highest accuracy compared with Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM) models. Moreover, we expect our method's accuracy could be improved by further optimizing the hyperparameter values and training the model for a longer period of time using a larger amount of data.