Perancangan Sistem Pendeteksi Emosional Siswa Menggunakan Algoritma CNN untuk Mengukur Tingkat Pengelolaan Kelas
DOI:
https://doi.org/10.57255/intellect.v3i02.295Keywords:
Emosional, Pengelolaan Kelas, CNNAbstract
This Papers is motivated by a problem at SMAN 5 Bukittinggi shows that teachers often have difficulty analyzing the emotional state of their students. This is based on the difficulty of teachers to control and classify the emotional state of each student while learning, which has an impact on the effectiveness of classroom management. The developed system is designed to automatically detect students' emotions, so that teachers can understand students' emotional conditions and adjust teaching methods. The research method used is Research and Development (R&D) which includes the stages of needs identification, design, development, testing, implementation, and evaluation. The data used are 28,700 facial images taken from the Kaggle platform and used to train CNN models in classifying seven emotions: angry, disgusted, afraid, happy, sad, surprised, and neutral. The system was tested at SMA Negeri 5 Bukittinggi involving teachers and students. The results showed that the developed system was valid (score 0.88), practical (score 0.62), and effective (score 0.83) in detecting students' emotions and helping classroom management. The contributions of this research include innovating the use of AI in education, providing technological solutions for teachers to better understand students' emotional states, and improving the interaction and quality of classroom management. Overall, the system is expected to help improve the quality of learning by providing teachers with more sophisticated and responsive tools to support effective classroom management.
Abstrak
Artikel ini dilatar belakangi dari suatu permasalahan di SMAN 5 Bukittinggi menunjukkan bahwa guru sering mengalami kesulitan mengenai analisa emosional dari siswanya. ini didasari oleh guru susah untuk mengontrol dan mengklasisifikasikan emosional dari setiap siswa saat belajar, yang berdampak pada efektivitas pengelolaan kelas. Sistem yang dikembangkan dirancang untuk mendeteksi emosi siswa secara otomatis, sehingga guru dapat memahami kondisi emosional siswa dan menyesuaikan metode pengajaran. Metode penelitian yang digunakan adalah Research and Development (R&D) yang meliputi tahapan identifikasi kebutuhan, desain, pengembangan, pengujian, implementasi, dan evaluasi. Data yang digunakan berupa 28.700 citra wajah yang diambil dari platform Kaggle dan digunakan untuk melatih model CNN dalam mengklasifikasikan tujuh emosi: marah, jijik, takut, senang, sedih, terkejut, dan netral. Sistem diuji di SMA Negeri 5 Bukittinggi dengan melibatkan guru dan siswa. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan valid (skor 0,88), praktis (skor 0,62), dan efektif (skor 0,83) dalam mendeteksi emosi siswa dan membantu pengelolaan kelas. Kontribusi penelitian ini mencakup inovasi penggunaan AI dalam pendidikan, memberikan solusi teknologi bagi guru untuk lebih memahami kondisi emosional siswa, serta meningkatkan interaksi dan kualitas pengelolaan kelas. Secara keseluruhan, sistem ini diharapkan dapat membantu meningkatkan kualitas pembelajaran dengan memberikan guru alat yang lebih canggih dan responsif untuk mendukung pengelolaan kelas yang efektif.
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