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All Journal Aptisi Transactions on Management ADI Bisnis Digital Interdisiplin (ABDI Jurnal) IAIC Transactions on Sustainable Digital Innovation (ITSDI) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application JOT
Qurotul Aini
Satya Wacana Christian University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology Other

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Self Supervised Transformers for High Dimensional Time Series Anomaly Detection Aswadi Jaya; Derlina; Qurotul Aini; Agung Rizky; Richard Evans
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/b-front.v6i1.1078

Abstract

This study addresses anomaly detection in high dimensional time series data within the context of Artificial Intelligence (AI) driven software development, where modern systems generate large temporal data streams and reliable monitoring remains difficult due to noise, complexity, and limited labeled anomalies. The objective of this research is to develop an effective and scalable anomaly detection framework based on self supervised transformer models that can learn meaningful temporal representations without heavy reliance on manual annotation. The proposed method applies self supervised pretraining through masked sequence reconstruction and contrastive temporal learning on large scale, unlabeled multivariate time series datasets, followed by transformer based attention mechanisms to capture long range dependencies and compute anomaly scores. Experiments are conducted using benchmark datasets and real world system log data implemented with Python based deep learning tools and transformer architectures to evaluate detection performance. The results indicate that the proposed approach improves detection accuracy and reduces false positive rates compared to traditional statistical techniques and supervised deep learning models, particularly in high dimensional and low label settings. In conclusion, integrating self supervised learning with transformer architectures provides a robust and generalizable solution for time series anomaly detection, contributing to software analytics and monitoring systems by lowering labeling costs and improving adaptability across application domains.
A Data Driven Information System for Cybersecurity Vulnerability Management Qurotul Aini; Agung Rizky; Suca Rusdian; Azwani Aulia; Archa Erica
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v10i1.2600

Abstract

The rapid growth of digital infrastructures has amplified cybersecurity vulnerabilities, challenging organizations to manage risks effectively. Traditional vulnerability assessment methods, such as static scoring systems, often overlook dynamic threat information, leading to suboptimal prioritization. This study addresses the gap in existing vulnerability management approaches by introducing a data-driven framework that combines internal system data, public vulnerability databases, and external threat intelligence using predictive analytics. The proposed decision support information system employs machine learning as an analytical component to estimate the likelihood of vulnerability exploitation and support vulnerability prioritization decisions. The novelty of this approach lies in its ability to prioritize vulnerabilities not only based on technical severity but also considering the context of real-world threat activity. When benchmarked against conventional methods, this approach demonstrates superior performance in identifying exploitable vulnerabilities, improving accuracy and recall, thus optimizing resource allocation. By adopting a proactive, risk-based strategy, the framework prioritizes the most critical vulnerabilities in complex IT environments. The results highlight the potential of predictive models in enhancing cybersecurity management and supporting sustainable infrastructure, driving a shift toward more efficient, data-driven decision-making.  
Data Driven A or B Testing Methodology for Website Effectiveness Qurotul Aini; Aulia Khanza; Vinkan Likita; Steven Harazaki Lase; Yasir Mustafa Kareem
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/t04mab20

Abstract

Website design and optimization decisions are often driven by subjective opinions, internal organizational preferences, or prevailing industry trends rather than empirical evidence derived from large-scale user interaction data, resulting in suboptimal performance and inconsistent user experiences. In digital environments characterized by high data volume and velocity, the absence of a structured experimentation methodology limits organizations’ ability to effectively leverage Big Data for continuous website improvement. This paper presents a comprehensive and systematic methodological guide to A or B testing as a data-driven approach for enhancing website effectiveness in data-intensive contexts. Unlike existing A or B testing guides that focus mainly on tools or isolated experimental outcomes, this study proposes an end-to-end framework integrating hypothesis formulation, scalable experimental design, statistical rigor, iterative learning, and practical decision-making into a unified and replicable process. The methodology outlines the complete A or B testing lifecycle, including alignment of business objectives with measurable data signals, development of testable hypotheses, controlled experiment implementation, large-scale data collection, and statistical analysis to ensure validity and significance of findings. The results demonstrate that a disciplined and continuous A or B testing program supported by Big Data analytics enables incremental yet compounding improvements in website performance. Through illustrative case examples, the study shows that relatively small, data-informed changes to website elements such as headlines, calls-to-action, images, and layout structures can lead to statistically significant gains in conversion rates, user engagement, and overall user experience. The paper concludes that A or B testing serves as a strategic Big Data analytics mechanism that supports evidence-based website optimization decisions grounded in empirical user behavior rather than intuition.
The Influence of Brand Awareness and Social Media Marketing on Purchase Intention through Brand Trust Marviola Hardini; Qurotul Aini; Fitra Putri Oganda; Sheila Aulia Anjani; Richard Andre Sunarjo
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 7 No 2 (2026): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v7i2.719

Abstract

The rapid development of digital technology has transformed how businesses interact with consumers, making brand awareness is an important strategy to strengthen brand presence and influence consumer behavior in online environments. In increasingly competitive digital markets, companies must build strong and credible brand images to foster consumer confidence and encourage purchasing decisions. This study aims to analyze the effect of brand awareness on purchase intention through brand trust as a mediating variable. A quantitative research approach is employed using a survey method to collect data from 135 consumers who interact with brands through digital platforms, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships between brand awareness, brand trust, and purchase intention. The results of the study reveal that brand awareness has a significant positive effect on brand trust and purchase intention, while brand trust also significantly influences consumers’ purchase intention and acts as a mediating variable between brand awareness and purchase intention. These findings indicate that effective brand awareness strategies not only improve brand visibility but also enhance consumer trust, which ultimately increases the likelihood of consumers making purchasing decisions. Therefore, this study provides theoretical contributions to the field of digital marketing by explaining the mediating role of brand trust in the relationship between digital branding and purchase intention, while also offering practical implications for businesses to strengthen digital branding strategies in order to build stronger consumer trust and improve market competitiveness in the digital era.
Integration of Artificial Intelligence in Digital Marketing Strategies Based on Business Data Analytics: Integrasi Kecerdasan Buatan dalam Strategi Pemasaran Digital Berbasis Analisis Data Bisnis Qurotul Aini; Sutama Wisnu Dyatmika; Mochamad Heru Riza Chakim; Miftakhul Khasanah; Zabenaso Queen
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 1 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i1.1230

Abstract

The digital transformation in the era of Artificial Intelligence (AI) has revolutionized marketing practices by placing data analysis at the core of adaptive and precision-based strategies. This study aims to analyze how the integration of AI and business data analytics can strategically and sustainably enhance the effectiveness of digital marketing. The research method employs a qualitative approach through literature review and case analysis of AI implementation in digital business contexts. The findings indicate that the use of AI in market segmentation, consumer behavior prediction, and content personalization significantly improves conversion rates, customer loyalty, and marketing cost efficiency. Moreover, this technological integration also supports the achievement of the Sustainable Development Goals (SDGs), particularly Goal 8 (decent work and economic growth) and Goal 9 (industry, innovation, and infrastructure). These findings highlight the importance of strengthening digital capabilities through the adoption of AI-based technology and data analytics as a foundation for building responsive, innovative, and sustainable marketing strategies aligned with the demands of the digital economy.
Explainable AI Supporting Responsible Human AI Interaction in Intelligent Decision Support Systems Cut Amalia Saffiera; Takumi Sase; Qurotul Aini; Danny Manongga; Harry Agustian
Journal of Orange Technology Vol. 3 No. 1 (2026): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v3i1.105

Abstract

The rapid adoption of Artificial Intelligence (AI)-enabled Intelligent Decision Support Systems (IDSS) has transformed decision-making across multiple sectors. However, increasing AI complexity often limits users’ understanding of recommendation processes, creating challenges for responsible human-AI interaction. Existing studies mainly emphasize explainability and transparency from technical perspectives while providing limited evidence regarding their influence on responsible interaction and decision quality. This study investigates the effects of Perceived Explainability and Perceived Transparency on Responsible Human-AI Interaction and Decision Quality, including the mediating role of Responsible Human-AI Interaction. Grounded in the Human-AI Teaming perspective and the Responsible AI Framework, this quantitative study employs a survey of 180 respondents with experience using AI-enabled Intelligent Decision Support Systems. Data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study is expected to demonstrate that explainability and transparency strengthen responsible human-AI interaction, which subsequently enhances decision quality. These findings are expected to enrich responsible AI literature and provide practical guidance for designing transparent, human-centered Intelligent Decision Support Systems that promote trustworthy decision-making and reinforce the humanistic values underpinning intelligent technologies
Co-Authors Agung Rizky Archa Erica Aswadi Jaya Aulia Khanza Azwani Aulia Cut Amalia Saffiera Danny Manongga Derlina Fitra Putri Oganda Harry Agustian Marviola Hardini Miftakhul Khasanah Mochamad Heru Riza Chakim Richard Andre Sunarjo Richard Evans Sheila Aulia Anjani Steven Harazaki Lase Suca Rusdian Sutama Wisnu Dyatmika Takumi Sase Vinkan Likita Yasir Mustafa Kareem Zabenaso Queen