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The rapid growth of online transportation services has significantly transformed urban mobility patterns, including in Abepura District, Jayapura City. This study is grounded in the concept of smart mobility, which emphasizes technological integration, efficiency, and accessibility within the smart city framework. The theoretical foundation draws on consumer preference theory and the Customer Satisfaction Index (CSI) model. A quantitative approach was applied through questionnaires distributed to 100 respondents, supported by secondary data on digital infrastructure and local transport regulations. The analytical methods included conjoint analysis to identify user preferences, CSI analysis to assess smart mobility readiness, and spatial analysis to map infrastructure support. The findings indicate that fare and safety are the most influential attributes shaping user preferences, followed by application usability, transport mode, and travel time. Maxim emerged as the most widely used application, followed by Grab and Gojek. The CSI score reached 77.60%, categorized as “highly ready,” though gaps remain in intermodal integration and waiting time efficiency. Spatial analysis confirmed that the coverage of 16 BTS towers in Abepura adequately supports online transportation operations. In conclusion, online transportation services in Abepura District demonstrate strong readiness to support the implementation of smart mobility, yet further improvements are needed in modal integration and operational efficiency to ensure sustainable and inclusive urban mobility.

The rapid development of digital technology has significantly transformed commercial activities, particularly through the emergence of marketplaces as platforms for online transactions. The vast number of products available in a marketplace often creates difficulties for users in finding items that suit their needs and preferences. To address this challenge, a recommendation system is required to provide personalized and relevant product suggestions. This study discusses the implementation of a product recommendation system in a marketplace using the Collaborative Filtering method. This method works by leveraging information from users’ previous behavior, such as purchase history, ratings, and similarity of preferences with other users, to generate more accurate product recommendations. The Collaborative Filtering approach has proven effective in identifying user preference patterns based on relationships between users as well as between items. This study employs user interaction data such as ratings and shopping activities as the processing foundation. The process involves data collection, preprocessing, calculation of similarity between users or products, and generating recommendation lists. The results indicate that this method enhances the shopping experience by providing relevant product suggestions tailored to user interests, thereby increasing customer satisfaction and potentially improving sales performance in the marketplace. Thus, the application of a Collaborative Filtering-based recommendation system not only simplifies product discovery for users but also offers strategic advantages for marketplace operators in digital business competition

Web-Based Augmented Reality (AR) is an emerging interactive technology that is increasingly adopted in the fields of visual communication design and digital education. This study aims to analyze trends in the use of Web-Based AR within interactive design contexts and to identify user preferences regarding its features. The research employs a literature review and secondary data analysis, drawing from academic publications, industry reports, and online surveys conducted between 2019 and 2024. The findings reveal a significant increase in the adoption of Web-Based AR both globally and in Indonesia. Key factors driving adoption include interactivity without installation, cross-device compatibility, and rapid browser-based access. Trend diagrams, comparative tables of AR platforms (such as 8thWall, ZapWorks, and WebXR API), and user preference visualizations are presented to support the analysis. The study discusses technological challenges, adoption gaps, and design implications, particularly in education and marketing. Limitations include reliance on secondary data and limited geographic scope. The study’s outcomes are expected to serve as a reference for interactive content developers, educators, and researchers in designing inclusive and adaptive AR-based user experiences.

This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian inference to generate relevant POI suggestions. As users begin to interact and provide ratings, the system progressively shifts to a collaborative filtering mechanism, leveraging cosine similarity to identify similar users within comparable contexts. The recommender system focuses on three categories of POIs: restaurants, hotels, and landmarks. These locations are retrieved through the Google Maps API, and only mapped locations are considered. The system was implemented on Android devices and evaluated through a user study involving 25 participants from diverse backgrounds, including software developers, IT students, and general users. Evaluation metrics such as normalized Discounted Cumulative Gain (nDCG) and classification accuracy were used to assess recommendation quality. Results demonstrate that the system performs better than traditional methods, with nDCG improvements reaching up to 83 percent. Users reported high satisfaction regarding the recommendations' accuracy, ease of use, and contextual relevance. While the system offers significant improvements, it also has certain limitations. Its dependency on Google Maps data may restrict its scope, and using only four contextual factors limits the system’s adaptability to more complex user preferences. Future enhancements could include additional dynamic contexts such as weather, POI popularity, and time-related trends, as well as integrating more advanced models to increase personalization and flexibility in real-world applications.