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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Informatika dan Komputasi STMIK Indonesia Jakarta Bulletin of Electrical Engineering and Informatics ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Data Science: Journal of Computing and Applied Informatics Journal of Robotics and Control (JRC) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) Jurnal Sistem Informasi dan Bisnis Cerdas Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)
Zainal Arifin Hasibuan
Universitas Indonesia
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Neuroscience Public Health Social Sciences Transportation

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Journal : Journal of Applied Data Sciences

 1 
Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods Santiko, Irfan; Soeprobowati, Tri Retnaningsih; Surarso, Bayu; Tahyudin, Imam; Hasibuan, Zainal Arifin; Che Pee, Ahmad Naim
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.598

Abstract

Learning achievement is undoubtedly impacted by each person's unique learning style. The assessment pattern is less focused due to the intricacy of the current components. In fact, general elements like VARK are thought to create complexity that can impair focus when combined with elements like environmental conditions, teacher effectiveness, and stakeholder policies. Although it is only ideal in specific areas, the application of supported information technology has so far yielded positive results. This essay attempts to be creative in evaluating how well students learn in higher education settings. An assessment framework that uses multidimensionality and simplifies features is the innovation that is being offered. Method, Material, and Media (3M) are the three categories into which simplification of aspects is separated. However, the Dimensions are categorized into five groups: Traditional, Enhance, Mobile, Ubiquitous, and Smart (TEMUS). Approximately 1200 respondents consisting of students and lecturers formed into a dataset in 2 types of data, namely test data and training data. The trial was conducted using 4 models, namely Random Forest, SVM, Decision Tree, and K-Nearest. The test results were interpreted in MSE, R-Square, Accuracy, Recall, Precision, and F1-Score. Based on the comparison of test results, it states that Random Forest has the most optimal results with MSE values of 0.46, R Square 0.99, Accuracy 0.86, Recall 0.86, Precision 0.87, F1 Score 0.84. Based on the results obtained, it proves that in addition to being able to carry out the classification process, the TEMUS Dimensional Framework can form a pattern of compatibility with each other, between the learning styles of Lecturers and Students. According to this TEMUS framework, teacher and student performance will be deemed suitable and effective when the 3M components are assessed from both perspectives in the same way. If not, a review will be conducted.
Transformer Architectures for Automated Brain Stroke Screening from MRI Images Abstract Sukmana, Husni Teja; Hasibuan, Zainal Arifin; Rahman, Abdul Wahab Abdul; Bayuaji, Luhur; Masruroh, Siti Ummi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.736

Abstract

Early and accurate detection of stroke is critical for timely medical intervention and improved patient outcomes. This study explores the application of deep learning models, particularly the Vision Transformer (ViT), for the automated classification of brain stroke from medical images. A curated dataset of brain scans was used to train and evaluate the ViT model, which was benchmarked against a widely used convolutional neural network (CNN), ResNet18. Both models were trained using transfer learning techniques under identical preprocessing and training configurations to ensure fair comparison. The results indicate that the ViT model significantly outperforms ResNet18 in terms of validation accuracy, class-wise precision, and recall, achieving a peak accuracy of 99.60%. Visual analyses, including confusion matrices and sample prediction comparisons, reveal that ViT is more robust in detecting subtle stroke patterns. However, ViT requires more computational resources, which may limit its deployment in real-time or low-resource settings. These findings suggest that transformer-based architectures are highly effective for medical image classification tasks, particularly in stroke diagnosis, and offer a viable alternative to traditional CNN-based approaches.
Co-Authors A.A. Ketut Agung Cahyawan W Ajib Susanto Albaar Rubhasy Andi Kambau, Ridwan Arif Darmawan Aristo, Nabila Cynthia Atin, Sufa Bayu Surarso Bobi Kurniawan, Bobi Bramantyo, Satrio Bisma Budhi Kristianto Che Pee, Ahmad Naim Dalimarta, Fahmy Ferdian Damayanti, Sri Erina Dwi Andayani, Dwi Eko Sediyono Erlin Dolphina Firdaus, Dony Waluya Guruh Fajar Shidik Hakim, M. Iman Nur Heryandi, Andri Hinchcliff, Reece Humami, Faris Husni Teja Sukmana Ika Novita Dewi Imam Tahyudin Irfan Santiko Irmayanti, Hani Isworo, Slamet Iwan Setyawan Kusumawati, Yupie Luhur Bayuaji, Luhur Marjuni, Aris Nawawi, Muhamad Nova Rijati Popon Dauni Pratindy, Raka Pulung Nurtantio Andono Purnama Harahap, Eka Purwanto Purwanto Rahman, Abdul Wahab Abdul Reza, Ivan Muhammad Ricardus Anggi Pramunendar Rizal Rachman, Rizal Saputra, Filmada Ocky Setiawan, Santo Sinaga, Daurat Siti Shofiah Siti Ummi Masruroh Soeleman, Moch. Arief Sri Handayani Sri Supatmi Supatmi , Sri Syamsiar, Syamsiar Tri Retnaningsih Soeprobowati Untung Rahardja Winarsih, Nurul Anisa Sri Yaacob, Noorayisahbe Mohd. zulkifli, ridwan