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All Journal Indonesian Journal on Software Engineering (IJSE) Kreatif: Jurnal Pemikiran Pendidikan Agama Islam TAJDID: Jurnal Pemikiran Keislaman dan Kemanusiaan Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) J-ESA (Jurnal Ekonomi Syariah) Taroa : Jurnal Pengabdian Masyarakat SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Journal of Excellence Humanities and Religiosity Jurnal Pengabdian kepada Masyarakat (PEMAS) Jurnal Pengembangan Sains dan Teknologi Pendiri: Jurnal Riset Pendidikan Scientific: Journal of Computer Science and Informatics Journix: Journal of Informatics and Computing Indonesian Applied Research Computing and Informatics
Zumhur Alamin
Universitas Muhammadiyah Bima
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Journix: Journal of Informatics and Computing

 1 
Real-Time Phishing Detection Using Google Safe Browsing API and Machine Learning Zumhur Alamin; Ritzkal
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.v1i2.8

Abstract

Phishing remains one of the fastest-evolving cybersecurity threats, where attackers mimic legitimate websites to obtain sensitive user information. This study presents a real-time evaluation of a phishing detection system integrating the Google Safe Browsing API with ensemble machine learning models. The research aims to enhance detection accuracy and responsiveness against emerging phishing websites by combining real-time threat intelligence with automated URL analysis. The dataset used comprises over 20,000 URLs collected from Google Safe Browsing, PhishTank, and OpenPhish between June and December 2024. Four approaches were evaluated: (1) machine learning models without API, (2) API-only detection, (3) machine learning with API as an additional feature, and (4) machine learning with API as a validator. The best performance was achieved by the API-as-validator model, reaching 98.2% accuracy, reducing false positives to 2.1%, and lowering false negatives to 3.2%, with an average latency of 108 ms. These findings demonstrate that integrating real-time threat feeds significantly enhances adaptability and reliability in phishing detection. Future research will focus on latency optimization and federated learning to enable large-scale collaborative detection systems.
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.
Co-Authors Ade S. Anhar Al-zainuri, Awaluddin Alaudin, Nurlatifah Aldillah Aldillah Arisandi Dahlan Fathir Fathir Fathir Husnatul Mahmudah Ihwan Ilmih, Muhammad Khaeruddin Khaeruddin Khaeruddin Khairunnas Khairunnas, Khairunnas Kukuh Yudhistiro, Kukuh Lukman LUKMAN, LUKMAN Mayada, Mayada Mohammad Nur Cholis, Mohammad Nur Muh. Alimin Muhammad Akbar Muhammad Amirul Mu'min Muhammad Hayun Mursalim Mursalim Musmuliadin Nimas Ratna Sari Nur Fadhillah Nur Hunul Khatimah Nur Wahyu Hidayat Nurfidianty Annafi, Nurfidianty Putra, Firnanda Al Islama Achyunda Ramadhan, Sahrul Randitha Missouri Ritzkal, Ritzkal Safitriani, Nurinsa Sa’adati, Yuan Sembiring, Rinawati Setiawan, Muhammad Iwan Siti Mutmainah Sri Jamilah Sutriawan Teguh Ansyor Lorosae Ulil Azmin Ummu Rofikah Wahyunti, Sri Yana Safitri, Yana