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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Intelligent Computing and Health Informatics (JICHI)
Kafitra Marna Ibrahim
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Dentistry Education Electrical & Electronics Engineering Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health

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Analysis of the Integrated E-Mesp 4CS Mobile Instrument Eny Winaryati; Muhammad Munsarif; Iwan Junaedi; Utomo; Alya Dwi Arianty; Asiva Khoirunisa; Kafitra Marna Ibrahim
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12980

Abstract

The need for valid and contextually relevant supervision instruments remains a key challenge in realizing 21st-century education that integrates the 4Cs skills (critical thinking, creativity, collaboration, and communication) with the Profil Pelajar Pancasila. This study aimed to develop and evaluate the feasibility of the Integrated E-MESp 4Cs Mobile as a digital instrument for academic supervision. Using a research and development (R&D) approach with qualitative data collection, this study conducted Focus Group Discussions, in-depth interviews, and document analyses involving teachers, principals, supervisors, and education experts. The data was analyzed through interactive qualitative procedures, including data reduction, presentation, and verification. The results show that the E-MESp 4Cs Mobile instrument is feasible and relevant to be used as an integrated supervision tool that combines 4Cs indicators with the dimensions of the Pancasila Student Profile. Stakeholders also emphasized the need for standardization aligned with national guidelines and the potential use of artificial intelligence to support data-based supervision and policy recommendations. Overall, the E-MESp 4Cs Mobile instrument demonstrates strong potential to enhance teacher supervision practices, support deep learning, and strengthen the development of humanistic, inclusive, and character-oriented graduates.
The Hybrid Residual–BiLSTM Model for Mitigating the Vanishing Gradient Problem in Synthetic Image Detection Kafitra Marna Ibrahim; Brian Halubanza; AHMAD SHUKRI MOHD NOOR; Muhammad Sam'an; Muhammad Munsyarif; Azyan Yusra Kapi
Journal of Intelligent Computing & Health Informatics Vol 7, No 2 (2026): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i2.22744

Abstract

The rapid advancement of generative artificial intelligence has enabled the creation of highly realistic synthetic images, raising concerns regarding the authenticity of digital media. While convolutional neural network (CNN)-based methods have shown promising results for synthetic image detection, deep architectures often suffer from training instability caused by the vanishing gradient problem and limited representation learning. This study proposes a Deep Residual BiLSTM Fusion model that integrates residual-based CNN feature extraction with bidirectional long short-term memory (BiLSTM) modeling to enhance training stability and contextual representation learning. The proposed model is evaluated on the CIFAKE dataset, a challenging low-resolution benchmark for synthetic image detection. Experimental results demonstrate competitive performance with an accuracy of 97.27% and balanced precision and recall across both real and synthetic classes. Gradient flow analysis further confirms stable optimization behavior, highlighting the effectiveness of the proposed hybrid architecture for robust synthetic image detection.
Co-Authors AHMAD SHUKRI MOHD NOOR Arianty, Alya Dwi Asiva Khoirunisa Azyan Yusra Kapi Brian Halubanza Eny Winaryati Iwan Junaedi Muhammad Munsarif Muhammad Munsyarif Muhammad Sam'an Utomo