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All Journal Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems
Elvira Nur
Universitas Negeri Makassar
Computer Science & IT Education

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Pengembangan E-Modul Academic Culture Orientation Sebagai Bahan Ajar Program Pengenalan Lingkungan Kampus Pada Mahasiswa Baru Mustari Lamada; Dwi Rezky Anandari Sulaiman; Elvira Nur
Information Technology Education Journal Vol. 3, No. 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

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Abstract

Penelitian ini bertujuan untuk mengetahui validitas, kepraktisan, dan keefektifan Pengembangan E-Modul Academic Culture Orientation Jurusan Teknik Informatika dan Komputer Fakultas Teknik Universitas Negeri Makassar. Penelitian ini menggunakan metode penelitian R&D (Research and Development), rancangan pengembangannya menggunakan model 4-D. Subjek pada penelitian ini adalah mahasiswa Jurusan Teknik Informatika dan Komputer yang telah mengikuti Academic Culture Orientation. Instrumen pengumpulan data dilakukan melalui lembar uji validasi, angket respon mahasiswa, dan instrumen penilaian hasil penggunaan e-modul. Teknik analisis data yang digunakan adalah analisis data deskriptif. Hasil penelitian menunjukkan bahwa e- modul Academic Culture Orientation Jurusan Teknik Informatika dan Komputer Fakultas Teknik Universitas Negeri Makassar, berada pada nilai persentase 96% dengan kategori sangat valid. E-Modul Academic Culture Orientation dinyatakan praktis dari hasil analisis respon mahasiswa dengan nilai persentase 90% dengan kategori sangat praktis. E-Modul Academic Culture Orientation dinyatakan efektif karena ditinjau dari hasil penggunaan mahasiswa mencapai ketuntasan 97% dengan kategori sangat baik sehingga dinyatakan efektif. Berdasarkan data tersebut dapat disimpulkan E-Modul Academic Culture Orientation dapat diterima untuk digunakan sebagai media dalam pelaksanaan Academic Culture Orientation Jurusan Teknik Informatika dan Komputer Fakultas Teknik Universitas Negeri Makassar.  
Classification of Students' Emotions from Facial Expressions Using CNN to Support Adaptive Learning Akmal Hidayat; Hera Ariska; Iren Kirana; Asmiyah Auliatna; Dian Sri Yuninda; Elvira Nur
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/b1rcm003

Abstract

The integration of affective aspects into adaptive learning systems remains limited, as most educational technologies primarily rely on cognitive performance indicators. However, students’ emotional conditions significantly influence engagement, motivation, and learning outcomes. This study aims to develop and evaluate a Convolutional Neural Network (CNN) model for classifying students’ emotions based on facial expressions to support adaptive learning environments. A quantitative experimental approach was employed. Facial expression image data were preprocessed through face detection, resizing, normalization, and data augmentation before being trained using a CNN architecture with the Adam optimizer and categorical cross-entropy loss function. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that the proposed CNN model achieved an overall accuracy of 90% with an average F1-score of 0.88 across four emotion categories (Happy, Sad, Neutral, and Angry). The confusion matrix indicates that most predictions fall within the correct classification range, although minor misclassifications occurred between low-intensity Sad and Neutral expressions. The stability of training and validation loss curves demonstrates good generalization ability without significant overfitting. These findings indicate that CNN-based facial emotion classification can serve as a reliable component in adaptive learning systems by providing real-time affective feedback. The study contributes to the development of artificial intelligence applications in education by integrating emotional recognition into adaptive instructional design
Co-Authors Akmal Hidayat Asmiyah Auliatna Dian Sri Yuninda Hera Ariska Iren Kirana Sulaiman, Dwi Rezky Anandari