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The Gulf Cooperation Council (GCC) region has experienced tremendous growth in the higher education sector with many public but internationally recognized universities, colleges, and programs. Annually, within and outside of the GCC zone, high school students and guardians undergo repeatable and time-consuming experiences for inhomogeneous university, college, and program admission and tracking processes. This research project aims to provide a unified management tool to manage the admission and tracking process to multiple accredited GCC universities, colleges, and programs through one unified central mobile-based application. The research project aimed to achieve two objectives: exploring the GCC university admission process phenomena and developing and evaluating a unified mobile-based university admission application (denoted by HEIM) for higher education institutes' mobile applications. An Agile-SDLC methodology was employed, entailing two phases; the Exploratory phase to the phenomena of the university admission process and the development and evaluation phase of the potential mobile application. The 1st phase was based on an interview with the Ministry of Education (MOE), employing SLR and CA techniques for articles and mobile applications collection and analysis, and 1st questionnaire to collect user requirements. The 2nd phase employed several design techniques, including DFD, ERD, etc., a 2nd questionnaire to collect system requirements and coding by .NET MAUI, C#, and XAML, and a 3rd questionnaire to evaluate the usability of HEIM based on Nielsen heuristics at which the empirical findings revealed 97.7% of usability.

Plant disease is one of the problems in the world of agriculture. Early identification of plant diseases can reduce the risk of loss, so automation is needed to speed up identification. This study proposes a custom-designed convolutional neural network (CNN) model for plant disease recognition. The proposed CNN model is not complex and lightweight, so it can be implemented in model applications. The proposed CNN model consists of 12 CNN layers, which consist of eight layers for feature extraction and four layers as classifiers. Based on the experimental results of a plant disease dataset consisting of 38 classes with a total of 87,867 image records. The proposed model can get high performance and not overfitting, with 97%, 98%, 97% and 97%, respectively, for accuracy, precision, recall and f1-score. The performance of the proposed model is also better than some popular pre-trained models, such as InceptionV3 and MobileNetV2. The proposed model can also work well when implemented in mobile applications.

The beginning of 2020 was a nightmare for Indonesia, the Covid-19 pandemic that hit had an extraordinary impact. Tourism, which is the main sector in the process of national development, is not spared from the impacts it presents. This pandemic has hit the tourism industry so that tens of thousands of workers have lost their jobs within 1 month. As time goes by, Indonesia does not continue to fall into misery. The new normal order has made the tourism sector carry out various kinds of innovations to get back up from the downturn caused by the pandemic. One of the innovations made is by maximizing digital marketing for tourist destinations. This study uses a literature study research method using various written sources such as articles, journals, and documents that are relevant to the study in this study. The results of the study show that the use of digital marketing in the tourism sector has several advantages, namely (1) Spending less money and targeting a wider market, (2) Retaining customers by establishing communication in a smooth way, (3) Easy to choose specific targets (4) Its effectiveness is easy to measure. Digital marketing for tourist destinations can utilize and use various online platforms such as websites, social media, mobile applications, and online advertising.

The smart city action plan for the city of Denpasar Bali includes: 1) human resources 2) nfrastructure, 3) applications, and 4) smart city literacy. Meanwhile, in building a smart city there are six pillars, namely smart governance, smart society, smart living, smart economy, smart environment, and smart branding. In 2020 Denpasar City ranks 1st Smart City with Surakarta in the framework of the movement program towards 100 Smart Cities Indonesia.The 2021 Electronic-Based Government System Index is 3.19 which is in the good category and is in the Top 5 RKCI that year.  The Denpasar Bali City Communication, Information and Statistics Service (Kominfos) has taken several steps to implement e-government in realizing a smart city. Some of these steps include building an e-gov portal, increasing accessibility, using the right technology, establishing partnerships with the private sector, developing mobile applications. By implementing e-gov and developing a smart city, the Denpasar-Bali City Communication and Informatics Agency is able to improve the quality of public services, facilitate public access to information and public services, and increase the efficiency and effectiveness of the performance of the Denpasar city government.