Activities on Unmanned Aerial Vehicle (UAV) have increased over the last years and there are many fields in which UAVs can be used. One of the basic applications is reconnaissance of a given area using multiple UAVs. To perform reconnaissance mission, there are two methods: (i) path planning to navigate the pre-determined route; and (ii) random mobility method to explore without prior knowledge. In this paper, we indicate the imbalance problem of existing random mobility models for reconnaissance and propose a new model considering reconnaissance balance based on the number of visits. We divide the scanning area into N zones and then select a zone stochastically in which the search is insufficient. We evaluated the performance of the model by focusing on the coverage rate and average inter-visiting time. The proposed model shows that the 90%-coverage reaching time is improved by about 25% and the average inter-visiting time is improved by up to 15% compared to the previous approach.

In this study, we address that foliar diseases of rice (Oryza sativa L.) pose a serious threat to agricultural productivity and propose an effective method for disease detection using Convolutional Neural Network (CNN). We use transfer learning on the MobilenetV3Large model to improve the model's performance. Our study involves a curated dataset containing images of infected rice leaves, followed by a careful preprocessing step. This dataset is then used to train a CNN model. The results show a commendable accuracy rate of over 90% and almost reaching 95% when the model is trained over 200 epochs. The model performance graph shows a consistent upward trend in accuracy coupled with decreasing loss during the training process. Furthermore, the classification results highlight the ability of the model to discriminate between different types of diseases affecting rice leaves. This study demonstrates the effectiveness of our proposed method and positions it as a valuable tool for leaf disease detection in rice. By providing faster and more accurate control measures, our approach has the potential to significantly improve agricultural productivity. The successful application of the CNN model using MobilenetV3Large highlights its adaptability and robust performance in addressing the pressing problem of rice leaf diseases and provides a promising path for future advances in precision agriculture.

This study delves into an analysis of the adoption patterns within the Jakarta Today e-government application (JAKI) through the dual lenses of the Government Adoption Model (GAM) and the Structural Equation Model (SEM). Encompassing JAKI users aged 17 years and above, the research encapsulates a substantial sample size of 384 individuals. The research findings underscore the pivotal role of key factors in driving e-Government adoption within the context of JAKI. Notably, Perceived Service Response, Perceived Trust, Perceived Uncertainty, Perceived Security, and Privacy collectively wield a significant and affirmative impact on the Adoption of e-Gov. However, intriguingly, factors including Perceived Awareness, Computer-self Efficacy, Availability of Resources, Perceived Ability to Use, Perceived Compatibility, Perceived Functional Benefit, Perceived Image, Perceived Information Quality, and Multilingual Option do not exert a notable influence on the Adoption of e-Gov. These insights proffer invaluable guidance for the Jakarta City Government, facilitating an enhanced understanding of user perceptions and needs. By meticulously addressing the determinative factors that engender a favorable adoption environment, the government stands poised to elevate the efficacy and reach of its e-government service, thus fostering greater citizen engagement and interaction with the JAKI application.

Today, simulator technology has been widely used as an important part of quadrotor development such as validation and testing. A good quadrotor simulator can simulate the quadrotor system as closely as possible to the real one. Therefore, in case of multi-quadrotor simulator, the simulator should not only can simulate a multi-quadrotor system, but also every quadrotor should be able to leverage their own resources. To solve this issues, in this paper, we present a hypervisor-based multi-quadrotor simulator. We used RT-Xen as hypervisor, a real-time Xen hypervisor. To ensure every quadrotor runs in real-time manner, we implemented quadrotor simulator in Litmus-RT which is a real-time extension of Linux. In this paper, we conducted some testing and performance evaluation for particular cases on our multi-quadrotor simulator: step-input responses, computation time, and response times. Based on the performance evaluation, our hypervisor-based multi-quadrotor simulator environment is proven to meet the real-time requirements. The results show that three important tasks in quadrotor system: Stability Controllability Augmented System (SCAS), Equation of Motion (EOM), and waypoint following task, are finished before their deadlines; in fact, 20 ms, 10 ms, and 40 ms before the deadlines for SCAS, EOM, and waypoint following, respectively.

In the era of rapidly developing information technology, the existence of smartphones has become an integral part of everyday life. Appearance and choice of dress code play a crucial role in a person's self-image. Therefore, this research aims to design a smartphone-based dress code selection recommendation system. This system will use clothing usage data, user preferences, and event context to provide relevant dress code recommendations. With this solution, it is hoped that users can easily and efficiently choose the appropriate dress code, increase self-confidence, and create a pleasant dressing experience. This research contributes to the development of smartphone-based applications to support users' lifestyle and personal appearance. This application not only provides dress code inspiration, but also makes it easier for users to make decisions regarding clothing choices. Model testing using Machine Learning with the K-Nearest Neighbor (KNN) algorithm shows satisfactory accuracy, precision and recall, namely 83.67%, 83.82% and 99.34%. This application has the potential to be a useful tool helping users live an informed fashion lifestyle and according to personal preferences, and also minimize the waste of time that would occur when choosing clothes.

Si Toya Wening application is an application designed to facilitate Surakarta PDAM customers in making water bill payments, making complaints, and other features. When research was conducted to 155 users, it showed that there were several indications of problems that usability in this application was not going well, such as a less intuitive interface, mismatch of items on the bottom navigation bar, and information blocked by other elements. In order to enhance the usability of this application, redesigning the interface is essential. As a measure of success, usability testing and system usability scale were used to compare the initial appearance and the appearance after being redesigned. The result of this research is a redesigned prototype with the design thinking method which is then tested using SUS to a sample of 83 respondents. SUS parameters are Acceptability ranges, adjective ratings, and grade scale. The final test results will be assessed in comparison to the initial test results. The test results show a significant improvement in application usability. The increase in the SUS score from 62.35 in the first test to 80.69 in the final test shows an improvement in usability. The acceptability range shifted from “MARGINAL LOW” to “ACCEPTABLE,” the adjective ratings improved from “OK” to “EXCELLENT,” and the grade scale rose from category “D” to “B.” This enhancement indicates that the application's usability has significantly improved.