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All Journal Jurnal Pengabdian kepada Masyarakat Nusantara JUTEKOM - Jurnal Teknologi dan Ilmu Komputer
Nasution, Muhammad Rafiq
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Facial Emotion Recognition Based on Convolutional Neural Network Using FER2013 Dataset Syabil, Muhammad Al Faris; Harahap, Lailan Sofinah; Nasution, Muhammad Rafiq
Jurnal Teknologi informasi dan Ilmu Komputer Vol. 2 No. 1 (2026): Januari 2026
Publisher : Nolsatu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65258/jutekom.v2.i1.44

Abstract

Facial emotion recognition is an important research area in computer vision and artificial intelligence, with applications in human–computer interaction, affective computing, and intelligent systems. This study aims to evaluate the performance of a Convolutional Neural Network (CNN) for facial emotion recognition using the FER2013 dataset. The FER2013 dataset consists of grayscale facial images with a resolution of 48×48 pixels and includes seven emotion classes: angry, disgust, fear, happy, neutral, sad, and surprise. Due to its low image resolution and imbalanced class distribution, FER2013 presents significant challenges for emotion classification tasks. An experimental research approach was employed by implementing a baseline CNN architecture composed of convolutional, pooling, and fully connected layers. Image normalization and batch-based data generation were applied during preprocessing. The model was trained using the Adam optimizer with categorical cross-entropy loss, and an early stopping mechanism was utilized to prevent overfitting. 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 test accuracy of 55.50%. Emotions with distinctive facial features, such as happy and surprise, obtained higher F1-scores, while minority and visually subtle classes, particularly disgust and fear, exhibited lower performance. These findings indicate that a simple CNN architecture can provide reasonable performance on challenging facial emotion datasets while highlighting the impact of class imbalance and limited image resolution. The proposed model can serve as a baseline for further improvements in facial emotion recognition systems.
Strategi Digitalisasi UMKM melalui Rancang Bangun Website sebagai Langkah Promosi dan Pelayanan Informasi Pradana, Risky Ananta; Nasution, Muhammad Rafiq; Syabil, Muhammad Alfaris; Atiqi, Muhammad Farros; Lubis, Ahmad Ardiansyah
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 1 (2026): Edisi Januari - Maret IN PROGRESS
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i1.7515

Abstract

Wd Advertising Medan merupakan UMKM jasa pembuatan huruf timbul dan neon box yang masih mengandalkan promosi konvensional melalui media sosial sehingga jangkauan pemasaran dan penyampaian informasi layanan belum optimal. Kegiatan pengabdian ini bertujuan mendukung transformasi digital usaha melalui perancangan dan pembangunan website sebagai media promosi dan pelayanan informasi. Metode pelaksanaan meliputi observasi kebutuhan, perancangan antarmuka, pengembangan website, uji fungsional, serta pelatihan pengelolaan konten kepada pemilik usaha. Hasil kegiatan menghasilkan website informatif yang memuat profil usaha, katalog layanan, portofolio, blog artikel, dan kontak layanan. Evaluasi kuantitatif mitra menunjukkan skor rata-rata 94/100 dan diproyeksikan dapat meningkatkan efektivitas promosi hingga ±70% serta menarik 6–10 calon pelanggan baru per bulan setelah website dipublikasikan. Novelty kegiatan terletak pada pengembangan website yang tidak hanya fokus pada promosi, tetapi juga pada kesiapan keberlanjutan melalui pelatihan pengelolaan konten secara mandiri. Kegiatan ini menunjukkan bahwa digitalisasi berbasis website mampu meningkatkan profesionalitas dan daya saing UMKM.
Co-Authors Atiqi, Muhammad Farros Harahap, Lailan Sofinah Lubis, Ahmad Ardiansyah Pradana, Risky Ananta Syabil, Muhammad Al Faris Syabil, Muhammad Alfaris