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All Journal Jurnal Sains Dan Teknologi (SAINTEKBU) International Journal of Electrical Engineering, Mathematics and Computer Science
Jonathan Cristiano Rabika
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Computer Science & IT Control & Systems Engineering Engineering Library & Information Science Mathematics

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Evaluating UI/UX Online Courses : A Case Study Of Software Engineering Students’ Learning Experience Rasendriya, Achmad Syahmi; Najla Amelia Putri; Anatasya Wenita Putri; Aulia Anggraeni; Jonathan Cristiano Rabika; Muhammad Al Amin; Barus, Irma Rasita Gloria; Amata Fami
SAINTEKBU Vol. 16 No. 02 (2024): Vol. 16 No. 02 August 2024
Publisher : KH. A. Wahab Hasbullah University

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Abstract

With technology advancing quickly in today's world, education has become one of the significantly affected aspects. Technological progress facilitated access to educational materials, leading to the emergence of various new learning methods, including online courses. Online courses were widely favoured due to the customization and diverse variations of their learning materials. Visual design, user interface (UI), and user experience (UX) played a crucial role in attracting interest in learning, as evidenced by increased engagement among users of online courses with appealing designs. These critical UI/UX aspects provided a specific boost in understanding and exploring this field, as reflected in the provision of UI/UX materials on online course platforms. This study utilized the User Experience Questionnaire (UEQ) method to evaluate the satisfaction and experiences of students participating in UI/UX online courses across six aspects: Attractiveness, Efficiency, Perspicuity, Dependability, Stimulation, and Novelty. The study involved 41 students who took UI/UX online courses for one month. The results indicated a high level of satisfaction across these aspects, suggesting that UI/UX online courses could be considered effective and reliable methods for understanding and exploring this field.
Real-Time Facial Emotion Detection Application with Image Processing Based on Convolutional Neural Network (CNN) Hakim, Ghaeril Juniawan Parel; Simangunsong, Gandi Abetnego; Rangga Wasita Ningrat; Jonathan Cristiano Rabika; Muhammad Rafi' Rusafni; Endang Purnama Giri; Gema Parasti Mindara
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 1 No. 4 (2024): December : International Journal of Electrical Engineering, Mathematics and Com
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v1i4.123

Abstract

Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.
Co-Authors Anatasya Wenita Putri Aulia Anggraeni Endang Purnama Giri Fami, Amata Gema Parasti Mindara Hakim, Ghaeril Juniawan Parel Irma Rasita Gloria Barus Muhammad Al Amin Muhammad Rafi' Rusafni Najla Amelia Putri Rangga Wasita Ningrat Rasendriya, Achmad Syahmi Simangunsong, Gandi Abetnego