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Bali is a renowned tourist destination that attracts visitors from around the world, particularly for its natural beauty, rich culture, and delicious cuisine. The increasing number of tourists in Bali has driven rapid growth in the culinary industry. In Denpasar City, selecting the right location is a key factor for the success of culinary businesses, as each location has different characteristics and potentials. This study employs the Multiple Attribute Decision Making (MADM) model, combining the Simple Additive Weighting (SAW) and Technique for Orders Preference by Similarity to Ideal Solution (TOPSIS) methods, to determine the optimal location for culinary businesses in Denpasar City. Data were collected through surveys of 154 culinary business owners, considering eight criteria: Accessibility, Visibility, Traffic, Facilities, Expansion, Environment, Competition, and Regulations. The study's findings indicate that both SAW and TOPSIS methods identify high population density areas as the best choice. The SAW and TOPSIS method provides the highest preference value of 0,8815 and 0.7082 respectively, making it the more effective method for recommending optimal culinary locations in Denpasar City.

Information systems now play a crucial role in various fields, including education. Yayasan Aldiana Nusantara (YAN), which supports students from low-income backgrounds, faces challenges in selecting scholarship recipients. To streamline this process, this research designs a web-based Decision Support System (DSS) using the Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The system considers criteria such as class, KPS/KIP/KKS recipients, parents' income, report card grades, and extracurricular activities. The DSS is developed using the Waterfall model, PHP, and MySQL, with four types of access rights: Administrator, Judge, Student, and Foundation Leader. Sensitivity testing shows that the SAW method is more responsive to changes in criteria compared to TOPSIS, with a sensitivity value of 2.98 for SAW and 0.022 for TOPSIS. These results indicate that SAW is more optimal in assisting YAN in effectively and efficiently selecting scholarship recipients.

Background: Additive manufacturing (AM) requires reliable and efficient defect detection mechanisms to ensure structural integrity and product quality, yet conventional inspection approaches remain time-consuming and often unsuitable for real-time industrial deployment. Objective: This study aims to develop and experimentally validate an artificial intelligence based vision inspection system capable of accurately detecting surface defects in AM components. Methods: A Convolutional Neural Network (CNN) architecture utilizing pretrained backbones (ResNet and EfficientNet) was implemented with a transfer learning strategy and data augmentation techniques. High-resolution AM surface images representing porosity, cracks, and layer misalignment were used for training and evaluation. Model performance was assessed using Accuracy, Precision, Recall, F1-score, and mean Average Precision (mAP), and comparative benchmarking was conducted against traditional machine learning models such as Support Vector Machine and Random Forest. Results: The proposed CNN-based models significantly outperformed conventional approaches, achieving up to 95.1% Accuracy and 92.8% mAP. The EfficientNet backbone demonstrated superior generalization capability, particularly in balancing Precision and Recall, indicating robust defect detection performance across multiple categories. These findings confirm that AI-driven inspection frameworks provide scalable and reliable quality assurance solutions for advanced manufacturing environments.

Autonomous vehicles (AVs) are revolutionizing transportation by relying on advanced AI techniques like deep learning and reinforcement learning for decision-making and navigation. However, concerns about the opacity of traditional AI models in safety-critical applications such as autonomous driving raise issues related to safety, accountability, and trust. This study explores the integration of Explainable AI (XAI) techniques in AV systems to enhance transparency and interpretability while maintaining high prediction accuracy. XAI methods, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive ExPlanations), provide understandable justifications for AI-driven decisions, addressing biases, fairness, and accountability. These techniques also support regulatory compliance and foster public trust in AVs. A mixed-methods approach, combining experimental simulations and user surveys, was employed to integrate XAI into AV systems and test its performance in urban traffic and highway driving scenarios. Feedback from users, collected through questionnaires and in-depth interviews, revealed that XAI-enhanced systems significantly improved the interpretability of AV decisions, leading to higher user trust and satisfaction. The study highlights the importance of balancing model complexity with interpretability, demonstrating that XAI techniques are crucial for building trust and ensuring accountability in autonomous driving systems.

Artificial food additives pose significant health risks to Gulf Cooperation Council (GCC) citizens despite regional authorities' extensive medical, legislative, and technological efforts. Literature highlights the detrimental impacts of these additives, including malnutrition, digestive disorders, respiratory problems, skin issues, hives, nausea, diarrhea, shortness of breath, allergic reactions, high blood pressure, and tumors. The research project at hand aims at becoming the first official and comprehensive mobile application of its own in the GCC region that manages the calculation and demonstration of an up-to-date health and legal knowledge base of the impacts of artificial additives, enhances the awareness, automatically recognizes the artificial additives, and provides alternative solutions, for both android and IOS mobile platforms. This research project introduces "Weqaya," a pioneering mobile application designed to manage, educate, and raise awareness about the effects of artificial additives. Weqaya provides real-time health and legal information, identifies additives, and suggests alternative solutions for Android and iOS platforms. The project employs an Agile-based SDLC model to explore, develop, and evaluate the food additive phenomena in Weqaya. The application's usability evaluation scores a promising 95.21%, indicating its potential utility for GCC health ministries, dietitians, academics, researchers, and food producers in enhancing knowledge and promoting non-artificial food options.