Cloud Computing Technology in the Development of Digital Libraries to Increase Literacy in Indonesia

Problem Statement: Cloud computing has the potential to increase literacy in Indonesia by enabling wider and more efficient access to sources of information through the creation of digital libraries that can be accessed anywhere and anytime. Purpose: In this analysis we can understand how Indonesian digital libraries are doing right now and outlines the difficulties they confront in giving their users access to information and resources. Method: This research is a descriptive qualitative research. Researchers chose to use this method because this research focuses on complex social phenomena, namely increasing literacy through the use of digital libraries supported by cloud processing technology. Result: The advantages of cloud computing technology for digital libraries, such as improved accessibility, improved cooperation, and increased efficiency, are also covered in the article. Conclusion: At the article's suggestions are made for using and implementing cloud computing technologies to increase literacy rates in Indonesia.    Keywords: Literacy; Digital  Libraries; Cloud Computing   Abstrak Permasalahan: Komputasi awan memiliki potensi untuk meningkatkan literasi di Indonesia dengan memungkinkan akses yang lebih luas dan efisien ke sumber informasi melalui pembuatan perpustakaan digital yang dapat diakses di mana saja dan kapan saja. Tujuan: Dalam analisis ini kita dapat memahami bagaimana perpustakaan digital Indonesia saat ini dan menguraikan kesulitan yang mereka hadapi dalam memberikan akses informasi dan sumber daya kepada penggunanya. Metode: Penelitian ini merupakan penelitian kualitatif deskriptif. Peneliti memilih menggunakan metode ini karena penelitian ini berfokus pada fenomena sosial yang kompleks, yaitu peningkatan literasi melalui penggunaan perpustakaan digital yang didukung oleh teknologi pemrosesan awan. Hasil: Keuntungan teknologi komputasi awan untuk perpustakaan digital ini seperti, peningkatan aksesibilitas, peningkatan kerja sama, dan peningkatan efisiensi. Kesimpulan: Pada artikel tersebut diusulkan penggunaan dan implementasi teknologi komputasi awan untuk meningkatkan angka melek huruf di Indonesia.  Kata kunci: Literasi; Perpustakaan Digital; Komputasi Awa

Linux Kernel Functions for an Embedded Target Platform

In the earliest years of computer systems revolution in the 1930-40s, the computers were extremely expensive and huge, and they were dedicated to performing a single task or a collection of targeted tasks. Nowadays, the tendency of computer systems development is towards some small, fast, and very powerful tools, gadgets and equipment which have become part of our everyday life. These systems are called embedded systems. Although they were used only to control electromagnetically telephone switches at the beginning, their capabilities have improved gradually over the past decade. Obviously, this is a vital requirement for embedded systems to be able to connect to some networks in order to send and receive data. It could increase the level of complexity in embedded systems. Hence, they are required to have more memory and interfaces, as well as the services of an operating system to do memory management, network management, file systems and etc. Although there are many different kinds of embedded operating systems, the Linux OS is chosen in our case. Now the question is how the Linux operating system could be integrated into the embedded system hardware platform and make it compatible with the user applications. If the target platform is one of the platforms already supported by the Linux, the porting procedures could be accomplished easily by using the codes and files provided by the Linux kernel. Otherwise, it is required to start coding from scratch. The target embedded system which is used in this thesis is called COFFEE Core. It is a RISC-based embedded processor that has been designed at Tampere University of Technology. COFFEE Core is considered as a general-purpose platform which is mainly designed for embedded systems. Since the COFFEE Core is not developed in the Linux kernel tree, it is required to integrate some pieces of code which should be written exclusively for COFFEE Core in Linux kernel tree. Accordingly, some modification in the hardware-independent sections is required. Therefore, the main goal of this thesis is to illustrate what it means to porting Linux OS to a newly designed architecture. It provides a comprehensive programming paradigm of the process of porting and explains how and in which order the porting could be fulfilled. Moreover, the architecture of Linux itself is presented and its different components will be reviewed

Optimizing E-Management Using Web Data Mining

Today, one of the biggest challenges that E-management systems face is the explosive growth of operating data and to use this data to enhance services. Web usage mining has emerged as an important technique to provide useful management information from user's Web data. One of the areas where such information is needed is the Web-based academic digital libraries. A digital library (D-library) is an information resource system to store resources in digital format and provide access to users through the network. Academic libraries offer a huge amount of information resources, these information resources overwhelm students and makes it difficult for them to access to relevant information. Proposed solutions to alleviate this issue emphasize the need to build Web recommender systems that make it possible to offer each student with a list of resources that they would be interested in. Collaborative filtering is the most successful technique used to offer recommendations to users. Collaborative filtering provides recommendations according to the user relevance feedback that tells the system their preferences. Most recent work on D-library recommender systems uses explicit feedback. Explicit feedback requires students to rate resources which make the recommendation process not realistic because few students are willing to provide their interests explicitly. Thus, collaborative filtering suffers from “data sparsity” problem. In response to this problem, the study proposed a Web usage mining framework to alleviate the sparsity problem. The framework incorporates clustering mining technique and usage data in the recommendation process. Students perform different actions on D-library, in this study five different actions are identified, including printing, downloading, bookmarking, reading, and viewing the abstract. These actions provide the system with large quantities of implicit feedback data. The proposed framework also utilizes clustering data mining approach to reduce the sparsity problem. Furthermore, generating recommendations based on clusters produce better results because students belonging to the same cluster usually have similar interests. The proposed framework is divided into two main components: off-line and online components. The off-line component is comprised of two stages: data pre-processing and the derivation of student clusters. The online component is comprised of two stages: building student's profile and generating recommendations. The second stage consists of three steps, in the first step the target student profile is classified to the closest cluster profile using the cosine similarity measure. In the second phase, the Pearson correlation coefficient method is used to select the most similar students to the target student from the chosen cluster to serve as a source of prediction. Finally, a top-list of resources is presented. Using the Book-Crossing dataset the effectiveness of the proposed framework was evaluated based on sparsity level, and Mean Absolute Error (MAE) regarding accuracy. The proposed framework reduced the sparsity level between (0.07% and 26.71%) in the sub-matrices, whereas the sparsity level is between 99.79% and 78.81% using the proposed framework, and 99.86% (for the original matrix) before applying the proposed framework. The experimental results indicated that by using the proposed framework the performance is as much as 13.12% better than clustering-only explicit feedback data, and 21.14% better than the standard K Nearest Neighbours method. The overall results show that the proposed framework can alleviate the Sparsity problem resulting in improving the accuracy of the recommendations