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All Journal Aptisi Transactions on Management ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Journal of Engineering, Science and Information Technology Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Kristina Vaher
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Design of Campus Activity Feedback Information System Based on Content Management System (CMS) Muhtarom Muhtarom; Iqbal Mansis; Meri Mayang Sari; Ari Asmawati; Kristina Vaher
ADI Bisnis Digital Interdisiplin Jurnal Vol 7 No 1 (2026): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v7i1.1432

Abstract

The management of student activity evaluation often faces significant efficiency challenges due to feedback collection methods that are still manual, fragmented, and not well integrated, resulting in slow data recapitulation, high potential for data inconsistency, and difficulties in tracing historical evaluation archives, which ultimately weakens the effectiveness of decision-making in quality assurance processes. To address these issues, this study aims to design and develop a feedback information system based on the WordPress Content Management System (CMS), chosen for its flexibility, ease of implementation, extensibility, and capability to centralize data management without requiring complex technical infrastructure. The system development adopts the Waterfall model, consisting of systematic stages including requirements analysis, system design, implementation, and testing, ensuring that the development process is structured and measurable. The analysis stage identifies user needs and system requirements through observation and document review, while the design stage focuses on developing a user-friendly interface, structured data storage, and role-based access control for administrators and users. During implementation, WordPress is configured and customized through plugins and online feedback forms that enable automatic data processing and centralized archiving. Functional testing simulations demonstrate that the system operates according to its specifications and is capable of supporting online data input, automatic report generation, and efficient data management. Preliminary evaluation results indicate that the proposed system can reduce data recapitulation time by up to 70% compared to conventional methods, making it a practical solution for supporting the digitalization of quality assurance in student activity management.
Equity and Government Bond Relationship in Indonesia During Covid Pandemic Gracia Shinta S. Ugut; Liza Handoko; Kristina Vaher
APTISI Transactions on Management (ATM) Vol 10 No 2 (2026): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v10i2.2619

Abstract

This study extends prior Covid-19 finance literature by examining the dynamic relationship between Indonesian equity returns and sovereign benchmark bond returns using daily data across two pandemic waves. Unlike previous studies focusing primarily on developed markets or conventional flight-to-safety behavior, this study provides evidence that government bonds in emerging markets may temporarily exhibit equity-like risk characteristics during pandemic-induced fiscal stress. Specifically, the findings show that equity market performance is positively correlated with government bond returns in Indonesia during the two waves of the Covid 19, as opposed to the findings from previous studies when there were financial crises, and also the results show negative correlation between the government bond return and the spread of the Credit Default Swap. Furthermore, this study examines the impact of the Covid pandemic to the local Indonesian long-term and medium-term benchmark bonds after applying the international risk factor variable to the model. The results show shifting investors’ attention from the international risk factors to the local risk factors in both medium and long tenor of the bonds during the pandemic period. Overall, this study highlights how pandemic-induced fiscal uncertainty alters stockbond dynamics in emerging markets and challenges conventional safe-haven assumptions regarding sovereign bonds.
Audit Driven Evaluation of Carrier Style Memory Malware Detection Under Obfuscation and Adversarial Attacks Syamsu Hidayat; Kusrini Kusrini; Ema Utami; Arief Setyanto; Kristina Vaher
APTISI Transactions on Management (ATM) Vol 10 No 2 (2026): ATM (APTISI Transactions on Management: May)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v10i2.2632

Abstract

This study evaluates Carrier-style memory malware detection under obfuscation using a reproducible, audit-driven protocol for verifiable reporting. We reproduce a stacking pipeline (Naive Bayes, Random Forest, Decision Tree with a Logistic Regression meta-learner) and benchmark it against strong single-model baselines. To limit leakage, we apply exact deduplication, train-only preprocessing, and group-disjoint splitting with explicit overlap checks, and we report dataset difficulty diagnostics to interpret near-ceiling results. Transfer is tested via cross-collection evaluation on the shared feature intersection between Obfuscated MalMem2022 and MemMalDet 2024, separating a low-shift validation setting from a higher-shift stress setting to keep generalization claims bounded. Robustness is assessed under a feasibility-preserving feature-space threat model with empirical bounds, non-negativity, and integer rounding, using a coordinate-search attack on the clean-correct subset across L0 budgets B=1,3,5, and 10 with confidence intervals. On obfuscated MalMem2022, Random Forest achieves 99.99% Accuracy, 99.99% F1, and 1.00 AUC, while the Carrier-style stack reaches 99.92% Accuracy, 99.92% F1, and 1.00 AUC, with no meaningful improvement over the best single model. Cross-collection validation yields F1 = 99.98 and AUC = 1.0, consistent with low-shift stability under aligned features rather than broad domain generalization. At B=10, ASR is 0.03 (95% CI: 0.0138–0.0639), and baseline defenses show clean-versus-robust trade-offs without consistent ASR reduction. We release four reusable artifacts an audit table, a leakage ablation matrix, a shift-aware cross-collection report, and robustness curves with confidence intervals.
Distributed Data Integrity and Decentralized Storage Leveraging IPFS in Blockchain Systems Ahmad Gunawan; Mungkap Mangapul Siahaan; Rendhika Adyatama; Toktar Kerimbekov; Kristina Vaher
Blockchain Frontier Technology Vol. 5 No. 2 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i2.915

Abstract

In the digital era that increasingly relies on distributed systems, data integrity has become a major challenge for various technological platforms, ranging from cloud services to blockchain-based infrastructures. The dispersion of data across multiple nodes without centralized oversight increases the risk of manipulation, loss, and inconsistency. This study aims to evaluate the effectiveness of the InterPlanetary File System (IPFS) as a distributed data storage solution capable of maintaining data integrity in an automatic, efficient, and verifiable manner. The research method involves a literature review of 30 scientific references and a case study-based simulation conducted within a local network consisting of four active IPFS nodes. The tests include file uploads, verification of Content Identifiers (CID), simulation of node failures, and evaluation of system performance based on parameters such as security, efficiency, and scalability. The results indicate that IPFS successfully detects file changes through CID discrepancies, ensures continued data access despite node failures, and optimizes bandwidth usage via caching and replication mechanisms. In conclusion, IPFS offers a secure, scalable, and tamper-resistant approach to distributed data storage, making it highly relevant for modern digital systems requiring transparency, reliability, and strong data integrity guarantees.
Visual Design Optimisation for Increased Conversion Rate on Digital Platforms Risma Haris; Noor Azura Zakaria; Harlis Setiyowati; Kristina Vaher; Lukita Pasha
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1761

Abstract

In the ever-evolving digital age, visual design optimisation has become an important focus for companies operating on digital platforms. Attractive, brand-appropriate visual design can influence user perception and increase engagement and conversions on the platform. This study aims to investigate the influence of visual design variables, including design quality and information engagement, on user engagement and conversion rates on digital platforms. The Structural Equation Modelling (SEM) method with SmartPLS was used to analyse data from online surveys involving respondents using digital platforms. The results of the analysis show that the quality of visual design has a significant influence on user engagement and conversion rates, while informational engagement also contributes positively to user engagement. The implication of these findings is the importance of paying attention to visual design and effective information delivery in improving user interaction and achieving business goals on digital platforms. The study provides valuable insights for digital platform developers and graphic design professionals in designing engaging and effective user experiences.
Big Data Governance Framework for Trustworthy Artificial Intelligence Decision Systems Adam Faturahman; Alfri Adiwijaya; Ardivan Avandi; Nanda Septiani; Kristina Vaher
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kp2fkq63

Abstract

The rapid adoption of Artificial Intelligence (AI) decision systems has increased organizational dependence on large-scale data, making big data governance a critical requirement for ensuring reliable and responsible decision-making. Although AI systems are often evaluated based on predictive accuracy and computational performance, their trustworthiness is strongly influenced by the quality, security, privacy, traceability, and fairness of the data used throughout the AI lifecycle. This study aims to develop a Big Data Governance Framework for Trustworthy AI Decision Systems by integrating key governance dimensions with trustworthy AI requirements. A qualitative conceptual framework development approach was employed, supported by structured literature review, thematic synthesis, and design science research principles. Relevant literature on big data governance, trustworthy AI, data quality, privacy, security, explainability, accountability, fairness, and AI decision systems was reviewed to identify recurring concepts and research gaps. The results show that trustworthy AI decision systems require seven core governance dimensions: data quality governance, security and privacy governance, metadata and data lineage, bias and fairness control, explainability support, accountability mechanisms, and continuous monitoring. These dimensions strengthen trustworthy AI capabilities, including reliability, transparency, explainability, fairness, privacy preservation, security, robustness, and auditability. The proposed framework demonstrates that trustworthy AI is not only determined by algorithmic performance but also by strong data governance across the AI lifecycle. This study concludes that effective big data governance can improve decision accuracy, traceability, accountability, risk reduction, and stakeholder trust in AI-based decision systems.
Co-Authors Adam Faturahman Ahmad Gunawan Alfri Adiwijaya Ardivan Avandi Ari Asmawati Arief Setyanto Ema Utami Gracia Shinta S. Ugut Harlis Setiyowati Iqbal Mansis Kusrini Liza Handoko Lukita Pasha Meri Mayang Sari Muhtarom Mungkap Mangapul Siahaan Nanda Septiani Noor Azura Zakaria Rendhika Adyatama Risma Haris Syamsu Hidayat Toktar Kerimbekov