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All Journal Scientific Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) International Journal of Engineering, Technology and Natural Sciences (IJETS) BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Ilmu Komputer dan Informatika J-ESA (Jurnal Ekonomi Syariah) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Taroa : Jurnal Pengabdian Masyarakat SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Abdi Masyarakat UIKA Journal of Excellence Humanities and Religiosity Jurnal Pengabdian Nusantara Jurnal Pengabdian kepada Masyarakat (PEMAS) Scientific: Journal of Computer Science and Informatics Journix: Journal of Informatics and Computing Indonesian Applied Research Computing and Informatics
Sutriawan Sutriawan
Department Of Computer Science, Universitas Muhammadiyah Bima, Indonesia
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Journal : Journix: Journal of Informatics and Computing

 1 
FCM-Guided CNN with Fuzzy Membership Maps for Robust Brain MRI Tumor Classification Firnanda Al-Islama Achyunda Putra; Kukuh Yudhistiro; Sutriawan; Zumhur Alamin
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.9

Abstract

Accurate brain MRI classification is critical for early tumor diagnosis and computer-aided clinical decision support. Conventional convolutional neural networks (CNNs) are effective in learning deep hierarchical features but often struggle with intensity heterogeneity and partial volume effects inherent to MRI data. To address these limitations, this study proposes a hybrid Fuzzy C-Means–CNN (FCM–CNN) framework that integrates unsupervised soft clustering with deep feature learning. The fuzzy segmentation stage preserves boundary uncertainty by generating multi-channel membership maps, which are then fed into a CNN for robust classification. Evaluations conducted on the Kaggle brain MRI dataset (3,264 slices across four diagnostic categories) under Stratified 5-Fold Cross-Validation show consistent improvements over baseline models. The proposed FCM–CNN achieves a mean accuracy of 96.26% and Macro-F1 of 0.9622, surpassing both CNN-only and K-Means+CNN by +4.84% and +2.74% respectively. Ablation analysis confirms that soft memberships enhance discrimination between visually similar tumors, while statistical testing verifies that the gains are systematic and reproducible. Furthermore, the fuzzy membership maps provide interpretable visual cues, aligning with recent trends in explainable AI (XAI) for medical imaging. Overall, the FCM–CNN framework demonstrates that combining fuzzy logic with deep learning yields a balanced trade-off between performance, interpretability, and computational efficiency, making it promising for clinical-grade brain MRI analysis.
Implementation of Post-Quantum Cryptography Algorithms for Financial Applications in Indonesia Sutriawan; Enggar Novianto
Journix: Journal of Informatics and Computing Vol. 1 No. 2 (2025): August
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i2.13

Abstract

The development of quantum computing poses a serious threat to classical cryptographic algorithms that have been used to protect digital data and financial transactions. Algorithms such as RSA and Elliptic Curve Cryptography (ECC) are vulnerable to quantum computer attacks capable of running Shor's algorithm to efficiently solve large number factorization problems. This study aims to explore and analyze the implementation of Post-Quantum Cryptography (PQC) algorithms, specifically Falcon and Dilithium, in the context of digital financial systems in Indonesia. The research approach was conducted through literature studies and case study analysis on the Algorand platform, which has adopted the Falcon algorithm to strengthen digital signature security. The results of the study show that the integration of PQC algorithms can be done without sacrificing system efficiency, while providing a significant increase in security resilience against quantum threats. This research is expected to serve as a reference for financial institutions and national regulators in formulating transition strategies towards a secure digital security infrastructure in the quantum era.
Co-Authors Ade Kurniawan Afril Efan Pajri Ahmad Zainul Fanani Ahmad Zaniul Fanani Alkhair Alzami, Farrikh Astuti, Rahma Hayuning Budiman Budiman Enggar Novianto Erham Erham Erviana, Irma Fathir Fathir Fathir Fathurrahman Fathurrahman Gufran Gufran, Gufran Hamiah, Nurul Husnul Khatimah, Nur Khairunnas Khairunnas, Khairunnas Khairunnisa Khairunnisa Khairunnisa Khatimah, Nur Husnul Kukuh Yudhistiro, Kukuh LUKMAN, LUKMAN M. Fikram Muammar Iksan Muh. Alimin Muhammad Akbar Muhammad Amirul Mu'min Muhammad Hayun Muljono Muljono Mursalim Mursalim Nimas Ratna Sari Nur Hunul Khatimah Nur Husnul Khatimah Nur Wahyu Hidayat Nurfidianty Annafi, Nurfidianty Putra, Firnanda Al Islama Achyunda Rahmawati, Lailia Randitha Missouri Rosyid, Romadhoni Ruri Suko Basuki Sahrul Ramadhan Sanusi, Gufran Siti Mutmainah Taufik Azhary Teguh Ansyor Lorosae Ummu Rofikah Widayati, Ulfa Zumhur Alamin Zuraini, Irma