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All Journal Foristek International Journal of Advances in Intelligent Informatics JOURNAL OF APPLIED INFORMATICS AND COMPUTING Paedagoria : Jurnal Kajian, Penelitian dan Pengembangan Kependidikan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika IAIC Transactions on Sustainable Digital Innovation (ITSDI) Innovative: Journal Of Social Science Research The Indonesian Journal of Computer Science SISFOTENIKA Bomba: Jurnal Pembangunan Daerah
Septiano Anggun Pratama
Universitas Tadulako
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Social Sciences Transportation Veterinary Other

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Pattern recognition for facial expression detection using convolution neural networks Pusadan, Mohammad Yazdi; Sasuwuk, James Rio; Pratama, Septiano Anggun; Laila, Rahma
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1602

Abstract

The COVID-19 pandemic was a devastating disaster for humanity worldwide. All aspects of life were disrupted, including daily activities and education. The education sector faced significant challenges at all levels, from kindergarten to elementary, junior high, and high school, as well as in higher education, where learning had to be online. Human emotions are primarily conveyed through facial expressions resulting from facial muscle movements. Facial expressions serve as a form of nonverbal communication, reflecting a person’s thoughts and emotions. This research aims to classify emotions based on facial expressions using the Convolutional Neural Network (CNN) and detect faces using the Viola-Jones method in video recordings of online meetings. We utilize the VGG-16 architecture, which consists of 16 layers, including convolutional layers with the ReLU activation function and pooling layers, specifically max pooling. The fully connected layer also employs the ReLU activation function, while the output layer uses the Softmax. The Viola-Jones method is used for facial detection in images, achieving an accuracy of 87.6% in locating faces. Meanwhile, the CNN method is applied for facial expression recognition, with an accuracy of 59.8% in classifying emotions.
Artificial Intelligence Untuk Identifikasi Motif Tenun Tradisional Sulawesi Tengah Pusadan, Mohammad Yazdi; Laila, Rahma; Pratama, Septiano Anggun
Bomba: Jurnal Pembangunan Daerah Vol 5 No 1 (2025)
Publisher : Badan Riset dan Inovasi Daerah Sulawesi Tengah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65123/bomba.v5i1.93

Abstract

Traditional weaving from Central Sulawesi, such as the motifs of Magau, Banua Oge/Souraja, and Tadulako, reflects deep cultural and historical values. However, the complexity of the patterns and motifs often makes manual identification challenging. This research employs an Artificial Intelligence (AI) approach using Convolutional Neural Networks (CNN) to automate the identification of these motifs. The AI model is trained using a diverse dataset of woven motif images and shows significant accuracy in classifying Magau, Banua Oge/Souraja, and Tadulako motifs. This research opens up cultural preservation and innovation opportunities in woven products with modern technology. The achieved result is the evaluation of the AI model using the following metrics: accuracy, precision, recall, and the confusion matrix. The accuracy obtained for each motif reaches 90%.
Co-Authors Ain, Moch. Zukhruf Anita Ahmad Kasim Ar Lamasitudju, Chairunnisa AR. Lamasitudju, Charunnisa Ardiyansyah, Rizka Arsita, Tiara Juli Chairunnisa Ar. Lamasitudju Deny Wiria Nugraha Dwi Shinta Angreni Fahmil, Fahmil Faldiansyah, Faldiansyah Hanama, Ikhsan Wahyudin Iskandar Iskandar Komang, Gusti Laila, Rahma Manoppo, Maryam Mohammad Yazdi Pusadan Muhammad Akbar Nanda, Rifqi Nouval Trezandy Lapatta Nurasyah Dewi Napitupulu Pagiu, Harry T. Putri, Fadiah Suryani Rahmah Laila Reinaldy Mansa, Jeremy Rifki, Moh Rini Septiani Rinianty, Rinianty Rizka Ardiansyah Ryfial Azhar, Ryfial Ryka Marina Walanda Saleh, Muhammad Taufik Sasuwuk, James Rio Siddiq Assegaf, Djafar Siti Fitriana Syahrullah Syahrullah Syaiful Hendra Ulhak, Muhamad Zia Wahyu Setiawan Wawagalang, A. Nolly Sandra Wirdayanti Yuri Yudhaswana Joefrie Yusuf Anshori