Sistem Informasi Simpan Pinjam Berbasis Web pada Koperasi Jasa Kawan Sejahtera Fakultas Teknik Universitas Pancasila

The process of collecting input data, processing it, storing it, analyzing it, and disseminating it is an information system. The purpose of this research is to develop a system that can support the data processing of cooperative members, member lists, and savings and loan processes so that accurate cooperative data and reports can be generated with data input processes and processed through a computerized database in the form of a web-based savings and loan cooperative processing information system. Faculty of Engineering at Pancasila University case studies on the Jasa Kawan Sejahtera cooperative, so that management activities are optimized. System development tools based on the UML (Unified Modeling Language) architecture are used to build this system, along with the PHP programming language, HTML, and MySQL as the database. Based on the findings of the information system that has been implemented, the top management of savings and loan cooperatives may simply and thoroughly monitor and oversee all management actions

Sistem Absensi Assisten Dosen Menggunakan Qr Code Scanner Berbasis Android Pada Program Studi Sistem Informasi Universitas Muria Kudus

Thesis report with the title "Assistant Lecturer Attendance System Using QR Scanner Codr Android Based on Information Systems Studies Program Muria Kudus University" has been carried out by analyzing the problems of absenteeism assistant professors are not computerized. Produce teaching assistant attendance system using a QR Code Scanner android based at the University Studies Program Information System Muria Kudus, so as to further facilitate the teaching assistant to confirm their attendance and also facilitate Laboran when doing monthly recap. The system is designed using UML modeling. While the programming language used is PHP and the MySQL database ANDROID. Results of this design is an Android and web-based applications for an Assistant Lecturer Information Systems Studies Program Faculty of Engineering University of Muria Kudus

Analisis Kinerja Pemodelan Data Star Schema Pada Data Perpustakaan

Lybrary information system whose data began to be organized from 2013 to the present, has recorded 5,477,618 library collections consisting of books, magazines, journals, CDs and so on. While the number of lending transactions continues to grow each year, although the development is not yet significant, it is recorded at around 3% every day. To help the process of analyzing library data patterns, a web-based system has been created using OLAP (Online Analytical Process) technology, with multidimensional data-based data modeling. To prove the simplicity of multidimensional data-based design, access time and join efficiency, in this study the selected data modeling is a star schema. The test results show that the star schema performance in library data is influenced by the amount of data, the cleaning process and the number of joins

User Interests Modeling Based on Multi-source Personal Information Fusion and Semantic Reasoning

Abstract. User interests are usually distributed in different systems on the Web. Traditional user interest modeling methods are not designed for integrating and analyzing interests from multiple sources, hence, they are not very effective for obtaining comparatively complete description of user interests in the distributed environment. In addition, previous studies concentrate on the text level analysis of user interests, while semantic relationships among interests are not fully investigated. This might cause incomplete and incorrect understanding of the discovered interests, especially when interests are from multiple sources. In this paper, we propose an approach of user interest modeling based on multi-source personal information fusion and semantic reasoning. We give different fusion strategies for interest data from multiple sources. Further more, we investigate the semantic relationship between users' explicit interests and implicit interests by reasoning through concept granularity. Semantic relatedness among interests are also briefly illustrated for information fusion. Illustrative examples based on multiple sources on the Web (e.g. microblog system Twitter, social network sites Facebook and LinkedIn, personal homepage, etc.) show that proposed approach is potentially effective

Social Learning Systems: The Design of Evolutionary, Highly Scalable, Socially Curated Knowledge Systems

In recent times, great strides have been made towards the advancement of automated reasoning and knowledge management applications, along with their associated methodologies. The introduction of the World Wide Web peaked academicians’ interest in harnessing the power of linked, online documents for the purpose of developing machine learning corpora, providing dynamical knowledge bases for question answering systems, fueling automated entity extraction applications, and performing graph analytic evaluations, such as uncovering the inherent structural semantics of linked pages. Even more recently, substantial attention in the wider computer science and information systems disciplines has been focused on the evolving study of social computing phenomena, primarily those associated with the use, development, and analysis of online social networks (OSN\u27s). This work followed an independent effort to develop an evolutionary knowledge management system, and outlines a model for integrating the wisdom of the crowd into the process of collecting, analyzing, and curating data for dynamical knowledge systems. Throughout, we examine how relational data modeling, automated reasoning, crowdsourcing, and social curation techniques have been exploited to extend the utility of web-based, transactional knowledge management systems, creating a new breed of knowledge-based system in the process: the Social Learning System (SLS). The key questions this work has explored by way of elucidating the SLS model include considerations for 1) how it is possible to unify Web and OSN mining techniques to conform to a versatile, structured, and computationally-efficient ontological framework, and 2) how large-scale knowledge projects may incorporate tiered collaborative editing systems in an effort to elicit knowledge contributions and curation activities from a diverse, participatory audience

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page