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Andree E Widjaja
Department of Information System, Universitas Pelita Harapan, Tangerang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Automated Class Attendance Management System using Face Recognition: An Application of Viola-Jones Method Andree E Widjaja; Nathanael Joshua Harjono; Hery Hery; Aditya Rama Mitra; Calandra Alencia Haryani
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.133

Abstract

Over the past few years, face recognition has been widely used to help human activities in various sectors, including the education sector. By using facial recognition, the class attendance system at universities can be significantly improved. For example, students are no longer asked to sign attendance sheets manually, but attendance can be taken, recorded, and managed automatically through an integrated class attendance management system using facial recognition. The recorded data can then be further analysed to produce useful information for instructors and administrators. In turn, this arrangement will assist them in making decisions about matters relating to student attendance. The main objective of this research is to develop an automatic class attendance management system using facial recognition. In particular, the system we propose was developed using a prototyping software development approach and was modelled using UML version 2.0. As a choice of methods and tools, we used the Viola-Jones method as a face detection algorithm, Python and PHP as programming languages, OpenCV as the computer vision library, and MySQL as the DBMS. The results obtained from a number of black box tests carried out were a fully functional automatic class attendance system prototype using facial recognition.
Text Mining Application With K-Means Clustering to Identify Sentiments and Popular Topics: A Case Study of The Three Largest Online Marketplaces in Indonesia Andree E Widjaja; Andy Fransisko; Calandra Alencia Haryani; Hery Hery
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.134

Abstract

The number of internets and social media users, which continues to increase at a very fast rate, has resulted in the emergence of new business opportunities in Indonesia. One of those indications is the emergence of marketplace companies in Indonesia. The presence of these online marketplaces provides people with more online marketplace choices according to their preferences. One of the factors that became the basis for this election was reading comments or reviews from consumers on the marketplace posted on social media. This research was conducted using text mining method with k-means clustering algorithm to systematically identify the sentiments and topics that are widely discussed by online marketplace consumers in Indonesia. The data was processed by the k-means algorithm in the form of comments or reviews from three online marketplaces (Tokopedia, Shopee and Bukalapak) on Twitter. The amount of data for each marketplace referred to was 1500 data tweets. The results showed that the three online marketplaces were associated to different topics, even though they are in the same industry. These differences arise due to the fact that most consumers discuss the topics of programs held by their respective online marketplaces. The main topics related to Tokopedia are “belanja” (“shopping”) and “terimakasih” (“thank you”); while for Shopee “pilih” (“choose”) and “jongho”, and for Bukalapak “pra-kerja” (“pre-employment”). In addition, the sentiment analysis carried out shows that the sentiment of the three online marketplaces is predominantly neutral.
Co-Authors Aditya Rama Mitra Andy Fransisko Calandra Alencia Haryani Hery Hery Nathanael Joshua Harjono