Total Web Quality

2021 ◽  
Author(s):  
Klaus M. Bernsau
Keyword(s):  
2021 ◽  
Vol 13 (2) ◽  
pp. 50
Author(s):  
Hamed Z. Jahromi ◽  
Declan Delaney ◽  
Andrew Hines

Content is a key influencing factor in Web Quality of Experience (QoE) estimation. A web user’s satisfaction can be influenced by how long it takes to render and visualize the visible parts of the web page in the browser. This is referred to as the Above-the-fold (ATF) time. SpeedIndex (SI) has been widely used to estimate perceived web page loading speed of ATF content and a proxy metric for Web QoE estimation. Web application developers have been actively introducing innovative interactive features, such as animated and multimedia content, aiming to capture the users’ attention and improve the functionality and utility of the web applications. However, the literature shows that, for the websites with animated content, the estimated ATF time using the state-of-the-art metrics may not accurately match completed ATF time as perceived by users. This study introduces a new metric, Plausibly Complete Time (PCT), that estimates ATF time for a user’s perception of websites with and without animations. PCT can be integrated with SI and web QoE models. The accuracy of the proposed metric is evaluated based on two publicly available datasets. The proposed metric holds a high positive Spearman’s correlation (rs=0.89) with the Perceived ATF reported by the users for websites with and without animated content. This study demonstrates that using PCT as a KPI in QoE estimation models can improve the robustness of QoE estimation in comparison to using the state-of-the-art ATF time metric. Furthermore, experimental result showed that the estimation of SI using PCT improves the robustness of SI for websites with animated content. The PCT estimation allows web application designers to identify where poor design has significantly increased ATF time and refactor their implementation before it impacts end-user experience.


Teknologi ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 10
Author(s):  
Fajar Indra Kurniawan ◽  
Ronny Makhfuddin Akbar

Monitoring siswa merupakan bentuk proses pengawasan sekolah terhadap kegiatan belajar mengajar siswa, salah satunya di tingkat Sekolah Menengah Atas Negeri atau SMAN Mojoagung, Jombang, Jawa Timur, Indonesia. Monitoring siswa tersebut meliputi jurnal kelas, presensi siswa dan capaian kompetensi dasar siswa. Proses pengolahan monitoring yang berjalan selama ini memiliki beberapa kendala, diantaranya 1) Belum tersedianya rekapitulasi absensi per mata pelajaran, sehingga guru mata pelajaran kesulitan memonitor kehadiran siswa ketika mata pelajaran sedang berlangsung; dan 2) Tidak terdokumentasi dengan baik capaian kompetensi dasar yang telah diajarkan pada tiap mata pelajaran, yang akhirnya mempengaruhi kualitas proses belajar mengajar. Berdasarkan kendala yang dialami, perlu dibangun sebuah sistem informasi yang mampu mengakomodasi pengelolaan proses aktivitas belajar mengajar meliputi pelaporan jurnal kelas, rekapitulasi absesi kehadiran siswa per mata pelajaran, dan laporan capaian kompetensi dasar. Pengembangan sistem informasi pada penelitian ini menggunakan Waterfall. Sistem monitoring ini menggunakan tiga aspek pengujian, diantaranya efficiency, reliability, dan usability berdasarkan Web Quality Evaluation Method (WebQEM). Hasilnya menunjukkan rata-rata loadtime 2,78 detik dan rata-rata pagesize 190,52 KB pada kecepatan internet 20,98 Mbps, serta persentase session, pages dan hits dengan nilai 99% dengan 25 data pada pengujian efficiency. Pengujian reliability menunjukkan performa yang baik karena mampu mengakomodasi 600 user yang menangani 1.085 request dan throughput 227,098/menit dengan 0% error dari total 5.322 data yang tersimpan. Sedangkan pengujian usability yang melibatkan 322 responden memiliki persentase 76,72% yang masuk kategori baik. Dari hasil pengujian tersebut aplikasi mampu menangani permintaan data dalam jumlah besar dan memastikan kehandalan server mengakomodasi akses seluruh siswa SMAN Mojoagung. Student monitoring is the form of school's supervision process on the teaching and learning activities at SMAN Mojoagung, Jombang, East Java, Indonesia, which  includes the class journals, student's attendance, and students' achievement of basic competencies. However, the recent monitoring process has had some obstacles, including 1) The absence of recapitulation of students' attendance in each lesson which makes it difficult for the teacher to monitor the students' attendance in the teaching and learning process; and 2) poor documentation of the students' achievements of basic competencies in each lesson, which ultimately affects the quality of the teaching and learning process. Based on the obstacles, it is crucial to build a web-based information system which is capable to accommodate the supervision process of the teaching and learning activity involving class journal report, recapitulation of the students' attendance in each lesson and the report of basic-competence achievement. The approach of this current research in this information system development used Waterfall method. The approach of this current research in this information system development used Waterfall method. The developed monitoring system used three aspects of testing, including efficiency, reliability, and usability based on the Web Quality Evaluation Method (WebQEM). The results showed an average loadtime of 2.78 seconds and an average pagesize of 190.52 KB at 20.98 Mbps internet speed as well as a percentage of sessions, pages, and hits of 99% in the efficiency testing on 25 data. The reliability testing showed proper performance since it could accommodate 600 users which handle 1,085 Request and 227.098/minute Throughput with 0% Error from the total of 5,322 saved data. Meanwhile, the usability testing involving 322 respondents showed a percentage of 76.72% and were categorized as good. Based the test results, the application was able to handle a large amount of data demand and ensured the reliability of the server in accommodating the access to all students of SMAN Mojoagung.


2017 ◽  
Vol 107 ◽  
pp. 1-10 ◽  
Author(s):  
Juan Francisco Ortega-Morán ◽  
J. Blas Pagador ◽  
Luisa Fernanda Sánchez-Peralta ◽  
Patricia Sánchez-González ◽  
José Noguera ◽  
...  

2006 ◽  
pp. 109-142 ◽  
Author(s):  
Luis Olsina ◽  
Guillermo Covella ◽  
Gustavo Rossi
Keyword(s):  

Author(s):  
Alexis Huet ◽  
Zied Ben Houidi ◽  
Shengming Cai ◽  
Hao Shi ◽  
Jinchun Xu ◽  
...  

Author(s):  
Alemnew Sheferaw Asrese ◽  
Ermias Andargie Walelgne ◽  
Vaibhav Bajpai ◽  
Andra Lutu ◽  
Özgü Alay ◽  
...  

Author(s):  
Athula Balachandran ◽  
Vaneet Aggarwal ◽  
Emir Halepovic ◽  
Jeffrey Pang ◽  
Srinivasan Seshan ◽  
...  

2020 ◽  
Vol 37 (3) ◽  
pp. 401-416 ◽  
Author(s):  
Tyler Tucker ◽  
Donata Giglio ◽  
Megan Scanderbeg ◽  
Samuel S. P. Shen

AbstractSince the mid-2000s, the Argo oceanographic observational network has provided near-real-time four-dimensional data for the global ocean for the first time in history. Internet (i.e., the “web”) applications that handle the more than two million Argo profiles of ocean temperature, salinity, and pressure are an active area of development. This paper introduces a new and efficient interactive Argo data visualization and delivery web application named Argovis that is built on a classic three-tier design consisting of a front end, back end, and database. Together these components allow users to navigate 4D data on a world map of Argo floats, with the option to select a custom region, depth range, and time period. Argovis’s back end sends data to users in a simple format, and the front end quickly renders web-quality figures. More advanced applications query Argovis from other programming environments, such as Python, R, and MATLAB. Our Argovis architecture allows expert data users to build their own functionality for specific applications, such as the creation of spatially gridded data for a given time and advanced time–frequency analysis for a space–time selection. Argovis is aimed to both scientists and the public, with tutorials and examples available on the website, describing how to use the Argovis data delivery system—for example, how to plot profiles in a region over time or to monitor profile metadata.


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