Pendeteksi Mood Mahasiswa Menggunakan Face Emotion Recognation Dengan Algoritma Haar Cascade

Filinia Gusti; Ahmaddul Hadi; Sandi Rahmadika

doi:10.61132/merkurius.v3i2.724

Authors

Filinia Gusti Universitas Negeri Padang
Ahmaddul Hadi Universitas Negeri Padang
Sandi Rahmadika Universitas Negeri Padang

DOI:

https://doi.org/10.61132/merkurius.v3i2.724

Keywords:

Haar Cascade, Face Emotion Recognation, Python

Abstract

Facial expressions are primary indicators of human emotions, often providing deeper insights than words (Oliver & Alcover, 2020). This study focuses on Mood as a natural response to past experiences, emphasizing its importance for psychological well-being. Over the past five years, the Covid-19 pandemic has triggered a significant global mental health crisis, affecting facial expressions and leading to varying degrees of depression and anxiety. Generation Z, particularly students, have been severely impacted, experiencing declines in academic abilities such as attention and memory (Qorik et al., 2020; Uswatun et al., 2020).In the educational context, the online learning systems adopted during the pandemic have introduced new challenges for students, including technical issues, lack of direct interaction, and less effective delivery of material, all of which contribute to increased academic stress (Rahmayinita, 2020; Utami et al., 2020). Enhancing the affective aspects of online education is crucial to better simulate face-to-face interactions (Bloom et al., 1984; Marzano & Kendall, 2007). This research aims to detect the Mood of students in online learning environments using advanced technology. The system is designed to assist educators in recognizing the academic stress levels of their students, enabling them to develop more creative and responsive teaching strategies (Zulfikri et al., 2023).

References

Abdillah, A., Rachman, M. M., & Wibisono, S. A. (2022). Comparison of face recognition algorithms: A study on convolutional neural network, local binary pattern histogram, and principal component analysis. Journal of Artificial Intelligence Research, 12(4), 134–147.

Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1984). Taxonomy of educational objectives: The classification of educational goals. Longmans, Green and Co.

Choi, S., Kim, J., & Park, Y. (2022). Advancements in facial recognition technology: A review of recent developments. International Journal of Computer Vision and Pattern Recognition, 25(3), 345–360.

Faisal, R., & Mustika, S. (2022). Applications of convolutional neural networks in face recognition. Journal of Artificial Intelligence and Computer Science, 19(3), 98-107.

Herman, H., & Yuliana, T. (2021). Evolving trends in facial recognition technology. Journal of Computer Science and Engineering, 18(2), 122-130.

Lingling, Z., Gu, H., & Wang, J. (2020). Facial emotion recognition with convolutional neural networks: Applications and challenges. International Journal of Computer Science and Network Security, 20(11), 72–78.

Malhotra, R., Sharma, A., & Kaur, H. (2021). Efficient face detection using Haar cascade classifier in varying illumination conditions. International Journal of Computer Applications, 183(6), 15–20.

Marzano, R. J., & Kendall, J. S. (2007). The new taxonomy of educational objectives. Corwin Press.

Oliver, P., & Alcover, C. (2020). The role of facial expressions in communication and emotional intelligence. Journal of Emotional and Behavioral Disorders, 28(2), 121–135.

Qorik, A., Safitri, R., & Nugroho, A. (2020). Impact of COVID-19 on mental health: An overview of depression and anxiety levels. Journal of Mental Health and Well-being, 9(1), 45–60.

Rahmayinita, D. (2020). Challenges and adaptations in online learning during the COVID-19 pandemic: Perspectives from students and teachers. Journal of Education and Learning Research, 8(3), 123–136.

Tej Chinimilli, A., Adinarayana, A., & Srikanth, M. (2020). Performance analysis of face recognition systems using LBPH algorithm. International Journal of Computer Applications Technology and Research, 9(2), 89–93.

Thary Al-Ghrairi, H., Al-Mohammad, H. S., & Abdullatif, H. (2022). Principal component analysis for efficient face recognition. Journal of Computer Science and Applications, 30(2), 223–230.

Uswatun, K., Nurjanah, N., & Purnama, W. (2020). Generation Z: Psychological impact and academic challenges during COVID-19 pandemic. Journal of Educational Psychology, 15(4), 329–341.

Utami, S., Rachmawati, R., & Purwanto, A. (2020). The rise of academic stress among students in online learning: A study during the pandemic. Journal of Educational and Developmental Psychology, 10(2), 111–119.

Zulfikri, M., Novita, A., & Fathurahman, F. (2023). Detection of academic stress levels using Haar cascade algorithm in online learning environments. Journal of Educational Technology and Innovation, 17(1), 199–210.