SISTEM INFORMASI TRACER STUDY ALUMNI PADA PRODI TEKNIK INFORMATIKA UNIVERSITAS MUHAMMADIYAH GRESIK BERBASIS WEB

The name of the Higher Education Institution will be big, because of the alumni. However, the Informatics Engineering University of Muhammadiyah Gresik (UMG) continues to be required to always improve the quality of its educational process accompanied by efforts to increase its relevance in the context of global competition. Efforts to make improvements both in the academic and infrastructure fields have been carried out. The management of alumni data at Muhammadiyah Gresik University especially Information Engineering has not been overlooked and is still done manually. Alumni data is only stored in piles of paper in the administrative space, this can cause data damage and even data loss. The UMG Informatics Engineering tracer study activity provides an increase in education in teaching and learning activities so that it can improve the subsequent learning process. Therefore, to get information on the indications of lack of implementation of the study program the success of professionalism (career, status, and income) of alumni is needed. The Web-based Tracer Study Information System in Informatics Engineering Muhammadiyah Gresik University will be made easier for study programs to get information on relevant knowledge and expertise (the relationship between knowledge and expertise with work requirements, work scope, professional position) of the alumni. The value of the convenience obtained from the application questionnaire by users is 75.95% rounded up to 76%

Suggested Framework for Big Data Analysis of Enterprise Websites. A Case Study for Web Intelligence Network

[EN] Big Data gives an opportunity for the researchers and scholars to make surveys in various domains. In this paper we will concentrate on websites as a data source which can be used to provide lots of valuable information for enterprise statistics. In this field, Big Data allows to get various information, including the type of the enterprise (e-commerce etc.), whether the enterprise is present in social media, the frequency of updating the website etc. The main goal of the paper is to present what Big Data methods are the most efficient in acquiring and processing the information from websites. The discussion shows different variants of conducting the work, based on the case studies conducted as experimental statistics at European Union level over the last 6 years. This paper is based on the experience in processing the data from websites in ESSnet grants on Big Data I (2016-2018), Big Data II (2018-2020) and Web Intelligence Network (2021-2025). The process of getting enterprise data from websites can be divided into the following steps: (1) Defining the population of enterprise websites; (2) Web scraping; (3) Data processing (extracting); (4) Data validation (de-duplication, quality indicators); (5) Data analysis; (6) Data dissemination. Each of the steps needs additional validation, especially the first step in this process have an impact on the final results that may not be comparable to the official statistical data. The essential part is also the way the data will be extracted to find the interesting data. In this sense, we need to choose between text mining methods, e.g. machine learning and regular expressions, that gives different results according to the information which should be provided. The paper shows how the use of appropriate methods can increase the overall value of the analysis.Maślankowski, J.; Nowak, D. (2022). Suggested Framework for Big Data Analysis of Enterprise Websites. A Case Study for Web Intelligence Network. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 270-270. http://hdl.handle.net/10251/18971027027

The Role of Eye-Movements in the Representation of Value in a Reinforcement Learning Context: A Web-Based Study

The present thesis experimentally set out to try to answer if there was a correlation between reinforcement learning and eye-movements and what the implications of such a correlation might be. An important experimental factor here was the decision to do this online, to see if it was possible to get valid and reliable results, and furthermore perhaps reach out to a more diverse group of people than a typical in-lab study would, making the results more generalizable. 38 people were recruited via the website prolific.co. The participants then performed a learning test, where they were shown two symbols on the screen, and the objective was to find the symbol with the highest value out of the pair, with three different symbol pairs. After each presentation of a pair, the participant had to choose one symbol, which were then followed by a rewarding or non-rewarding feedback. Each symbol pair had a different ratio of positive relative to negative feedback. The participants eye-movements were tracked via their web-camera, to see if they fixated more on the most rewarding symbol. The results showed that the participants reliably learned to choose the higher value symbol, comparable in validity to that of in-lab studies. We also found a statistically significant correlation between learning and fixating on the most rewarding symbol, although the quality of the eye-tracking was of too poor quality to draw any conclusions about this correlation. The present experiment reached a diverse group of people from all over the world and proved that it is possible to perform a reinforcement learning experiment online, although the technology of eye-tracking cannot match an in-lab study. Further online research is needed in many areas to determine what type of experiments can produce valid and reliable data, which is especially relevant to the generalizability of research, and the present situation of a global pandemic which limits the in-lab approach

Multi User Decision Support System For Teachers Sertification With HTTPS

Education quality is a key condition to realize the life of a developed nation, modern dan Prosperous. (Fasli Jalal 2008) Teacher quality greatly affect the effectiveness of learning (Suherman, 2007, Rink, 2002). Teacher certification program is a way to improve the quality of teachers so that the quality of education will be increased dan in turn affect students' achievement (Siedentop & Tannehill, 2000). The purpose of this paper is to discuss the design of decision support systems passing teacher certification so as to facilitate the certification team dan assessors in the decision making sertikasi teacher graduation. In this paper we use a qualitative method based on system development life cycle (SDLC), including planning, analysis, design, dan implementation. Design of decision support systems passing teacher certification can provide information about passing the certification of teachers, as information analysis dan control in teacher certification assessment conducted by the assessor dan teacher certification of graduation information. Keywords : Teachers Certification, System Development Life Cycle (SDLC), Qualitative Methods, Method of Black Box

A machine learning-based framework for preventing video freezes in HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) represents the dominant technology to deliver videos over the Internet, due to its ability to adapt the video quality to the available bandwidth. Despite that, HAS clients can still suffer from freezes in the video playout, the main factor influencing users' Quality of Experience (QoE). To reduce video freezes, we propose a network-based framework, where a network controller prioritizes the delivery of particular video segments to prevent freezes at the clients. This framework is based on OpenFlow, a widely adopted protocol to implement the software-defined networking principle. The main element of the controller is a Machine Learning (ML) engine based on the random undersampling boosting algorithm and fuzzy logic, which can detect when a client is close to a freeze and drive the network prioritization to avoid it. This decision is based on measurements collected from the network nodes only, without any knowledge on the streamed videos or on the clients' characteristics. In this paper, we detail the design of the proposed ML-based framework and compare its performance with other benchmarking HAS solutions, under various video streaming scenarios. Particularly, we show through extensive experimentation that the proposed approach can reduce video freezes and freeze time with about 65% and 45% respectively, when compared to benchmarking algorithms. These results represent a major improvement for the QoE of the users watching multimedia content online

Sorting Through and Sorting Out: The State of Content Sharing in the E-Learning

On 22-24 September 2002, a group of 22 education and information technology specialists gathered on the campus of the University of California at Irvine (UCI), for a symposium on the state of educational "content sharing." (See participant list.) The meeting was sponsored by the William and Flora Hewlett Foundation Education Program and the UCI Distance Learning Center. This paper summarizes the themes that emerged from that gathering. Most papers can be characterized as collaborative, but this one is particularly deserving of that adjective. The presentation here is an attempt to synthesize the ideas of all the participants, expressed in numerous conversational and written exchanges pre-, during and post-meeting. While every effort has been made to present the range of views, surely not all participants would agree with the emphases and interpretations herein.This report includes a hyper-linked bibliography and footnotes for additional web-based material on e-learning topics. Links are provided for the reader's convenience only, and represent neither an endorsement nor a guarantee of the accuracy of the content of the associated sites. Comments and questions about this document are welcomed, however, and should be directed to the author or the meeting sponsors

Information Outlook, April 2007

Volume 11, Issue 4https://scholarworks.sjsu.edu/sla_io_2007/1003/thumbnail.jp