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All Journal ILKOM Jurnal Ilmiah Jurnal Teknik Informatika (JUTIF) Emerging Information Science and Technology Internet of Things and Artificial Intelligence Journal
Adi, Puput Dani Prasetyo
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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An Exploration of the Work Performance of Educators in Transformative Schools: Leveraging Machine Learning for Performance Insights Maulidi, Rakhmad; Palandi, Jozua Ferjanus; Kristanto, Bagus Kristomoyo; Isyriyah, Laila; Rahmatullah, Rizky; Adi, Puput Dani Prasetyo; Kitagawa, Akio
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.2358.109-125

Abstract

Education has gone through various phases, and entered the transformative school mode which can be said to change the existing order of the previous schooling system or procedures, because many modes can be done in the transformative school, students can learn in school buildings or classes, or in the field or real industry or the real world of work, with the introduction of a wider and more complex world, this is one of them. This research tries to create and analyze transformative schools in 3 algorithms, namely regression algorithms, classification algorithms, and clustering algorithms that provide a detailed analysis of the results of the analysis of transformative schools currently promoted by the government. from the results of the analysis raises performance conclusions, and in this phase a conclusion can be drawn whether the Transformative school is able to provide answers about the performance of teachers, students, teacher education levels, school locations, number of students, learning methods, or any paramaters that can provide detailed and detailed answers to get performance analysis from Machine Learning, and Work Performance of teachers in Transformative schools with precision. Quantitatively, the value of performance is determined by innovation by 43.2%, followed by technological capabilities and collaboration, 27.9% and 17.2% respectively. and based on cluster level, cluster 3 is the best with 118 educators, cluster 0, 127 educators with high innovators, and cluster 2, 126 educators, and cluster 1 with 129 educators. and from the paradox of transformative practices 30.6% are high Adopters
Early Fusion of CNN Features for Multimodal Biometric Authentication from ECG and Fingerprint Using MLP, LSTM, GCN, and GAT Priyatama, Muhammad Abdhi; Nugrahadi, Dodon Turianto; Budiman, Irwan; Farmadi, Andi; Faisal, Mohammad Reza; Purnama, Bedy; Adi, Puput Dani Prasetyo; Ngo, Luu Duc
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5299

Abstract

Traditional authentication methods such as PINs and passwords remain vulnerable to theft and hacking, demanding more secure alternatives. Biometric approaches address these weaknesses, yet unimodal systems like fingerprints or facial recognition are still prone to spoofing and environmental disturbances. This study aims to enhance biometric reliability through a multimodal framework integrating electrocardiogram (ECG) signals and fingerprint images. Fingerprint features were extracted using three deep convolutional networks—VGG16, ResNet50, and DenseNet121—while ECG signals were segmented around the first R-peak to produce feature vectors of varying dimensions. Both modalities were fused at the feature level using early fusion and classified with four deep learning algorithms: Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), and Graph Attention Network (GAT). Experimental results demonstrated that the combination of VGG16 + LSTM and ResNet50 + LSTM achieved the highest identification accuracy of 98.75 %, while DenseNet121 + MLP yielded comparable performance. MLP and LSTM consistently outperformed GCN and GAT, confirming the suitability of sequential and feed-forward models for fused feature embeddings. By employing R-peak-based ECG segmentation and CNN-driven fingerprint features, the proposed system significantly improves classification stability and robustness. This multimodal biometric design strengthens protection against spoofing and impersonation, providing a scalable and secure authentication solution for high-security applications such as digital payments, healthcare, and IoT devices.
Co-Authors Adiprabowo, Tjahjo Agusti, Nelly Ajib Hanani Andi Farmadi Bagus Kristomoyo Kristanto Bedy Purnama Dodon Turianto Nugrahadi Ganis Chandra Puspitadewi Harry Soepandi Hsb, Elysa Rohayani Irmayani, Deci Irwan Budiman Johan Ericka Wahyu Prakasa Jozua Ferjanus Palandi Kitagawa, Akio KUNCORO, EKO Laila Isyriyah Lesmana, Yandri Lukman Hakim Muhammad Reza Faisal, Muhammad Reza Muhammad Yassir Ngo, Luu Duc Novilda, Mustamu Elizabeth Nurdiana, Dian Pane, Rahmadani Priyatama, Muhammad Abdhi Rahmatullah, Rizky Rakhmad Maulidi Saharuna, Saharuna Sastya Hendri Wibowo Satyawan, Arief Suryadi Sihombing, Volvo siregar, Victor M.M. Stekelorom, Kevin Vasista, Tatapudi Gopikrishna Yulianto, I Putu Eka Yuyu Wahyu