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All Journal Joutica : Journal of Informatic Unisla Journal of Embedded Systems, Security and Intelligent Systems
Titik Khawa Abd Rahman
Asia E University
Computer Science & IT Control & Systems Engineering

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Ensemble Learning for Android Privacy-Risk Flow Pre-Screening Using Permission and Metadata Features Tri Wahyuni; Muhammad Faisal; Titin Wahyuni; Nurnawaty; Rio Prasetyo Lukodono; Titik Khawa Abd Rahman
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.13180

Abstract

Purpose - This study develops a privacy-oriented pre-screening framework for identifying Android applications with potential Sensitive Data Exposure by combining lightweight permission and metadata features with ensemble learning. Design/methods/approach - Android applications obtained from the AndroZoo repository were analyzed using FlowDroid to construct reference labels based on sensitive source–sink flows. Privacy-oriented features were derived from permissions, application metadata, source and sink indicators, and interaction patterns. An Ensemble Stacking model integrating Random Forest, Support Vector Machine, and Extreme Gradient Boosting with Logistic Regression as the meta-classifier was evaluated under class imbalance. Additional circularity, ablation, repeated validation, and clean-feature experiments were conducted to assess robustness and deployment feasibility. Findings - The proposed framework demonstrated strong capability in distinguishing applications containing FlowDroid-defined potential privacy-risk flows. FlowDroid-derived source and sink indicators were highly discriminative, while permission-only features were less effective. Importantly, the clean-feature configuration retained strong discriminatory capability without requiring FlowDroid at inference time, supporting its use as a lightweight first-stage screening mechanism before more computationally intensive taint analysis. Research implications/limitations - The framework can support developers, security auditors, and platform administrators in prioritizing applications for deeper privacy inspection. However, the study relies on static analysis, a single application repository, and FlowDroid-derived reference labels. Originality/value - This study contributes a two-stage Android privacy-risk screening framework that combines lightweight deployable features with targeted static taint analysis while explicitly addressing label-feature circularity and inference-time feasibility.
TOGAF-Guided Microservice Architecture for Scalable and Interoperable Madrasah Learning Portals Andi Nailah Mashfufah Sulfa; Muhammad Faisal; Titin Wahyuni; Jufri; Titik Khawa Abd Rahman; Nurnawaty
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 4 (2026): December 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i4.13069

Abstract

Purpose – This study aims to design and evaluate an integrated madrasah learning portal by combining The Open Group Architecture Framework (TOGAF) with microservice architecture to improve the scalability, modularity, and interoperability of academic information services. Methods – The study employed selected TOGAF Architecture Development Method phases to align business, data, application, and technology requirements. The proposed architecture decomposed academic functions into User, Learning, and Assessment Services connected through REST APIs. Academic datasets representing students, teachers, subjects, classes, attendance, assignments, and assessments were used for proof-of-concept implementation. The architecture was evaluated through workload-based backend processing, cross-service identifier consistency, simulated workload iterations, and exploratory comparisons with monolithic architectural scenarios. Findings – The proposed architecture successfully integrated core academic services through modular service decomposition and standardized communication mechanisms. The workload evaluation demonstrated stable processing behavior under lower workloads, while larger workloads required increased execution time. Cross-service data relationships maintained consistent identifier references across the evaluated academic datasets. The exploratory architectural comparison also indicated that the Microservice + TOGAF configuration provided more efficient backend processing behavior than the evaluated monolithic configurations within the controlled environment. Research Implications – The proposed model provides a reference architecture for educational institutions seeking structured, modular, and interoperable academic information systems. Originality – This study integrates TOGAF-guided enterprise architecture with microservice-based academic service decomposition and evaluates the resulting architecture through scalability and interoperability analyses in a madrasah learning context.
CI/CD Pipeline Execution Time Optimization Using Code Churn-Based Selective-SAST on Jamstack Architecture Fadilah Tun Hazimah; Muhammad Faisal; Emil Agusalim Habi Talib; Titik Khawa Abd Rahman
Joutica Vol 11 No 02 (2026): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jti.v11i02.1695

Abstract

Full execution of Static Application Security Testing (SAST) on every commit in a modern CI/CD pipeline creates significant bottlenecks, especially in Jamstack architecture characterized by high commit frequency and fragmented code changes. This study evaluates the effectiveness of the Selective-SAST approach based on code churn analysis as a strategy to optimize pipeline execution time. The proposed approach utilizes four indicators of code change risk, namely relative churn above the 75th percentile, developer entropy above 1.5, intersection with sensitive paths (authentication, API, configuration, CI/CD), and days since last modification above 14 days, to determine the scanning coverage. The experiment was conducted using a paired comparison design on 49 open-source Jamstack repositories, 2,450 unique commits, and 120,472 lines of data resulting from the combination of churn metrics and Semgrep findings. Empirical results show that Selective-SAST provides a median time saving of 15.21% (95% CI bootstrap 12.88%-17.28%; ; rank-biserial ) while also demonstrating promising security retention potential (precision 1.00, recall 0.9412, and F1-score 0.9697). However, because the basis of SAST findings in the sub-sample is very sparse (only 17 baseline findings from 7 commits), these retention metrics should be interpreted as preliminary evidence. This study concludes that Selective-SAST shows promise in accelerating security feedback on the Jamstack CI/CD pipeline, but absolute validation requires testing on datasets with higher vulnerability density.
Co-Authors Andi Nailah Mashfufah Sulfa Emil Agusalim Habi Talib Fadilah Tun Hazimah Jufri . Muhammad Faisal Nurnawaty Rio Prasetyo Lukodono Titin Wahyuni Tri Wahyuni