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All Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Computers, Informatics, and Vocational Education
Asmiyah Auliatna
Universitas Negeri Makassar
Computer Science & IT Education Engineering Social Sciences Other

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Pengembangan E-Modul Mata Kuliah Komunikasi Data di Prodi PTIK Universitas Negeri Makassar Asmiyah Auliatna; Zulhajji; Abdul Wahid
Journal of Computers, Informatics, and Vocational Education Volume 1 Issue 3, November (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian yang dilakukan di Jurusan Teknik Informatika dan Komputer, Prodi Pendidikan Teknik Informatika dan Komputer Fakultas Teknik Universitas Negeri Makassar ini bertujuan untuk mengetahui : (1) Hasil pengembangan e-modul pembelajaran komunikasi data di Program Studi Pendidikan Teknik Informatika dan Komputer Fakultas Teknik Universitas Negeri Makassar layak digunakan sebagai media pembelajaran. (2) Tanggapan mahasiswa terhadap penggunaan e-modul pada mata kuliah komunikasi data di Program Studi Pendidikan Teknik Informatika dan Komputer Fakultas Teknik Universitas Negeri Makassar. (3) Hasil pengembangan e-modul pembelajaran komunikasi data di Program Studi Pendidikan Teknik Informatika dan Komputer Fakultas Teknik Universitas Negeri Makassar telah memenuhi kriteria efektif. Jenis penelitian yang digunakan adalah jenis penelitian pengembangan (Research and Development) dengan mengacu pada model pengembangan ADDIE. Adapun instrumen yang digunakan untuk penilaian kelayakan e-modul yaitu meliputi lembar penilaian kelayakan oleh ahli materi, lembar penilaian kelayakan oleh ahli media, dan angket respon mahasiswa terhadap penggunaan e-modul. Hasil pengujian berada pada kategori yang sangat layak dan dinyatakan layak digunakan sebagai bahan ajar dengan perolehan keseluruhan aspek oleh ahli materi dan ahli media sebesar 86,25% dan 91,07% dan respon mahasiswa sebesar 89,08% sehingga kelayakan e-modul yang dikembangkan dalam kategori “Sangat Layak”. Kemudian berdasarkan rekaptulasi data hasil uji efektifitas penelitian didapatkan nilai rata-rata 72,01 yang berada di antara 56 – 75 dengan tafsiran efektivitas N-Gain yaitu cukup efektif.
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 Abdul Wahid Akmal Hidayat Dian Sri Yuninda Elvira Nur Hera Ariska Iren Kirana Zulhajji