Perancangan dan Implementasi Sistem Pemesanan dan Monitoring Transaksi di Restoran Berbasis Smartphone

Penelitian ini didasari oleh alasan untuk meningkatkan kualitas layanan terhadap para pelanggan sebuah restoran pada saat menampilkan menu dan melakukan pesanan. Sistem yang akan dibangun adalah sebuah sistem yang terdiri dari tiga bagian yaitu E-menu, aplikasi bagian dapur dan aplikasi monitoring transaksi. E-menu adalah aplikasi yang digunakan untuk melihat menu dan melakukan pemesanan, aplikasi bagian dapur diperuntukkan bagi staf dapur yang mampu menampilkan pesanan yang telah dilakukan dan mengubah status pesanan yang sudah selesai disiapkan. Sedangkan aplikasi monitoring transaksi adalah aplikasi yang mampu memantau transaksi yang sudah dilakukan pada aplikasi E-menu. Pengujian aplikasi ini dilakukan dengan metoda black box dan mengacu pada skenario use case yang telah dirancang sebelumnya. Hasil dari pengujian yang dilakukan, aplikasi yang dibangun mampu berfungsi dengan baik sesuai dengan apa yang direncanakan dengan tingkat keberhasilan 100%

Enhancing travel experience with the combination of information visualization, situation awareness, and distributed cognition

With the new forms of travel introduced by new technologies of transportation and communication, a satisfied travel experience could be affected by various factors before and during a trip. Especially for road trips, traveling by car provides freedom on time control while leading to more possibilities of rescheduling initial plans made under time constraints. When overwhelmed with the need for changed travel context to avoid unexpected events that will require a serious change of initial plans, travelers need to find and access helpful contextual information quickly. This is a context-related decision making process that requires amplifying human situation awareness and supporting distributed cognition, since travel information offers multiple choices. To solve this problem, I applied information visualization as the main design solution. When comparing it with a traditional representation of lists, information visualization displays the advantages of visual representation of abstract data to clarify and depict the information and amplify cognition while improving travel experience intuitively in the domain of user experience design. Therefore in this thesis I will address the approach of implementing recontextualized situation awareness, distributed cognition, and information visualization in a travel-aid system. By using both theoretical and practical design perspectives, I will discuss how to enhance travel experience with represented contextual information that users desire or expect before and during a road trip. I will also explore the new values of this design with strategic business support. Additionally, after conducting research and analysis on existing interaction design parts, I selected a smartphone app to serve as a proper platform with connected multifunctions. Briefly, I begin the thesis with a review of previous theories and aspects of travel planning, information visualization as it relates to travel, situation awareness, and distributed cognition in the design context and related smartphone apps. Then I discuss the process of identifying the specific issues to be solved or improved with a preliminary research of empirical study, followed by an interview, online survey, insights synthesis, and business model design. After a visual-system design was developed, heuristic evaluation was employed to assess the outcome. Lastly, a new round of refined design results is introduced based on outcomes of the evaluation

Marketing Intelligence and Big Data: Digital Marketing Techniques on their Way to Becoming Social Engineering Techniques in Marketing

This contribution reviews the vast scope of digital application areas, which shape the digital marketing landscape and coin the present term “marketing intelligence” from a marketing technique point of view. Additionally, marketing intelligence as social engineering techniques are described. The review ranges from digital IT- and big data marketing until marketing 5.0 as digitalized trust marketing. The multiplicity of applications and interdependencies of the digital and social techniques reviewed should show that big data and marketing intelligence have already become a marketing reality. It becomes clear that marketing is witnessing a methodological, technical and cultural paradigm shift that augments and amplifies traditional outbound marketing with inbound marketing

InnoJam: A Web 2.0 discussion platform featuring a recommender system

In this Master Thesis we have designed, implemented and evaluated a Web 2.0 platform for massive online-discussion, inspired by Innovation Jams. Innovation Jams, the original initiative from IBM, has proven to be successful at bringing together vast amounts of people, capturing their untapped knowledge and, while the participants are discussing, gather useful insights for a companyʼs innovation strategy [Spangler et al. 2006, Bjelland and Chapman Wood 2008]. Our approach, based in an open-source forum system, features visualization techniques and a recommender system in order to provide the participants in the Jam with useful insights and interesting discussion recommendations for an improved participation. A theoretical introduction and a state-of-the-art survey in recommender systems has been gathered in order to frame and support the design of the hybrid recommender system [Burke 2002], composed by a content-based and a collaborative filtering recommenders, developed for InnoJam

Service Robots in Catering Applications: A Review and Future Challenges.

“Hello, I’m the TERMINATOR, and I’ll be your server today”. Diners might soon be feeling this greeting, with Optimus Prime in the kitchen and Wall-E then sending your order to C-3PO. In our daily lives, a version of that future is already showing up. Robotics companies are designing robots to handle tasks, including serving, interacting, collaborating, and helping. These service robots are intended to coexist with humans and engage in relationships that lead them to a better quality of life in our society. Their constant evolution and the arising of new challenges lead to an update of the existing systems. This update provides a generic vision of two questions: the advance of service robots, and more importantly, how these robots are applied in society (professional and personal) based on the market application. In this update, a new category is proposed: catering robotics. This proposal is based on the technological advances that generate new multidisciplinary application fields and challenges. Waiter robots is an example of the catering robotics. These robotic platforms might have social capacities to interact with the consumer and other robots, and at the same time, might have physical skills to perform complex tasks in professional environments such as restaurants. This paper explains the guidelines to develop a waiter robot, considering aspects such as architecture, interaction, planning, and executionpost-print13305 K

Information-seeking on the Web with Trusted Social Networks - from Theory to Systems

This research investigates how synergies between the Web and social networks can enhance the process of obtaining relevant and trustworthy information. A review of literature on personalised search, social search, recommender systems, social networks and trust propagation reveals limitations of existing technology in areas such as relevance, collaboration, task-adaptivity and trust. In response to these limitations I present a Web-based approach to information-seeking using social networks. This approach takes a source-centric perspective on the information-seeking process, aiming to identify trustworthy sources of relevant information from within the user's social network. An empirical study of source-selection decisions in information- and recommendation-seeking identified five factors that influence the choice of source, and its perceived trustworthiness. The priority given to each of these factors was found to vary according to the criticality and subjectivity of the task. A series of algorithms have been developed that operationalise three of these factors (expertise, experience, affinity) and generate from various data sources a number of trust metrics for use in social network-based information seeking. The most significant of these data sources is Revyu.com, a reviewing and rating Web site implemented as part of this research, that takes input from regular users and makes it available on the Semantic Web for easy re-use by the implemented algorithms. Output of the algorithms is used in Hoonoh.com, a Semantic Web-based system that has been developed to support users in identifying relevant and trustworthy information sources within their social networks. Evaluation of this system's ability to predict source selections showed more promising results for the experience factor than for expertise or affinity. This may be attributed to the greater demands these two factors place in terms of input data. Limitations of the work and opportunities for future research are discussed

Design and evaluation issues for user-centric online product search

Nowadays more and more people are looking for products online, and a massive amount of products are being sold through e-commerce systems. It is crucial to develop effective online product search tools to assist users to find their desired products and to make sound purchase decisions. Currently, most existing online product search tools are not very effective in helping users because they ignore the fact that users only have limited knowledge and computational capacity to process the product information. For example, a search tool may ask users to fill in a form with too many detailed questions, and the search results may either be too minimal or too vast to consider. Such system-centric designs of online product search tools may cause some serious problems to end-users. Most of the time users are unable to state all their preferences at one time, so the search results may not be very accurate. In addition, users can either be impatient to view too much product information, or feel lost when no product appears in the search results during the interaction process. User-centric online product search tools can be developed to solve these problems and to help users make buying decisions effectively. The search tool should have the ability to recommend suitable products to meet the user's various preferences. In addition, it should help the user navigate the product space and reach the final target product without too much effort. Furthermore, according to behavior decision theory, users are likely to construct their preferences during the decision process, so the tool should be designed in an interactive way to elicit users' preferences gradually. Moreover, it should be decision supportive for users to make accurate purchasing decisions even if they don't have detail domain knowledge of the specific products. To develop effective user-centric online product search tools, one important task is to evaluate their performance so that system designers can obtain prompt feedback. Another crucial task is to design new algorithms and new user interfaces of the tools so that they can help users find the desired products more efficiently. In this thesis, we first consider the evaluation issue by developing a simulation environment to analyze the performance of generic product search tools. Compared to earlier evaluation methods that are mainly based on real-user studies, this simulation environment is faster and less expensive. Then we implement the CritiqueShop system, an online product search tool based on the well-known critiquing technique with two aspects of novelties: a user-centric compound critiquing generation algorithm which generates search results efficiently, and a visual user interface for enhancing user's satisfaction degree. Both the algorithm and the user interface are validated by large-scale comparative real-user studies. Moreover, the collaborative filtering approach is widely used to help people find low-risk products in domains such as movies or books. Here we further propose a recursive collaborative filtering approach that is able to generate search results more accurately without requiring additional effort from the users