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Journal of Computer Science and Technology Application
Published by Pandawan Sejahtera Indonesia
ISSN : 30467616     EISSN : 30643597     DOI : https://doi.org/10.33050
Core Subject : Science, Education,
Computer Science & IT Other
The Journal of Computer Science and Technology Application (CORISINTA) is an international, open-access journal dedicated to advancing Information and Communication Technology (ICT). CORISINTA publishes research in Artificial Intelligence, Big Data, Cybersecurity, and Computer Networks. Through its rigorous double-blind peer-review process, the journal ensures the highest standards of quality. CORISINTA actively supports the United Nations Sustainable Development Goals (SDGs), including SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 17 (Partnerships for the Goals).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 57 Documents
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AI Framework for Synthesizing Qualitative User Feedback A Literature Review Santa Lusianna Sitorus; Ratih Komala Dewi; Vika Febrian; Muhammad Faris Ariq; Ramiro Santiago Ikhsan
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/r1yewe47

Abstract

The increasing reliance on user-centered design in digital product development has intensified the need for systematic approaches to transforming qualitative user feedback into actionable insights for UX and UI decision-making. Although qualitative feedback provides rich understanding of user motivations, frustrations, and contextual behaviors, product teams often face challenges such as data ambiguity, interpretive bias, information overload, and weak alignment between research outcomes and product strategy. This literature review aims to synthesize existing academic research and industry practices to propose a structured framework that bridges qualitative analysis and technology-driven product decisions. Using a qualitative research design based on framework analysis, this study reviews established methods including user interviews, usability testing, open-ended surveys, thematic analysis, and affinity-based synthesis. These approaches are integrated into a four-step framework consisting of feedback coding, theme identification, alignment with product objectives, and formulation of actionable insights. The findings of this review suggest that applying a structured synthesis process enhances analytical clarity, improves traceability between user feedback and design actions, and supports more consistent prioritization in UX/UI practices. Illustrative applications drawn from prior studies demonstrate how the framework can translate qualitative insights into concrete design recommendations without relying on empirical experimentation. This study concludes that qualitative user feedback delivers meaningful value only when processed through a systematic synthesis mechanism that connects user narratives with strategic and operational product decisions, providing a conceptual foundation for data-driven and AI-supported UX/UI design environments.
Blockchain Enabled Consumer Data Sovereignty for Privacy Oriented Digital Governance Denok Wahyudi Setyo Rahayu; Muhamad Firdaus; Abdullah Arif Kamal
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/vbq94q72

Abstract

This study examines blockchain technology as an enabling infrastructure for consumer centric data governance through the conceptual lens of the sovereign consumer. Departing from platform centric models that frame data privacy primarily as a regulatory compliance or technical security issue, this paper positions consumer sovereignty as both a governance principle and an operational capability. Using a qualitative and analytical research design, the study synthesizes insights from consumer sovereignty theory, digital governance literature, and decentralized systems research to conceptualize how blockchain characteristics namely decentralization, immutability, transparency, cryptography, and smart contracts can be mapped onto core data governance functions. The analysis demonstrates that blockchain has the potential to transform data ownership from a passive legal status into an enforceable and programmable right, allowing individuals to dynamically grant, monitor, and revoke consent over personal data usage. Rather than relying on absolute data concealment, privacy is operationalized through controlled transparency supported by cryptographic safeguards and hybrid on chain and off chain architectures. This governance oriented framework emphasizes accountability, auditability, and user authority without compromising regulatory alignment. Although the proposed framework remains conceptual and does not involve empirical implementation, it provides a structured analytical contribution relevant to AI driven digital ecosystems and sustainable digital development. In particular, the study aligns consumer centric data sovereignty with Sustainable Development Goals SDG 9, SDG 12, and SDG 16 by supporting resilient digital infrastructure, responsible data usage, and transparent governance mechanisms. The findings offer implications for policymakers, platform designers, and organizations seeking privacy oriented digital governance models grounded in consumer empowerment.
Accelerating Time to Market through Agile in AI Innovation Susy Alestriani Sibagariang; Farisha Andi Baso; Nugroho Prihantoni; Noah Rangi
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v3i1.158

Abstract

Rapid digital transformation, driven by the increasing integration of Artificial Intelligence (AI) into digital products and services, has intensified competition across industries and positioned Time-to-Market (TTM) as a critical success factor for digital innovation projects. AI-oriented development requires rapid iteration, continuous data processing, and frequent model refinement, which are often difficult to support using traditional project management approaches, leading to delayed deployment and reduced adaptability. In this context, agile methodologies have emerged as a flexible and adaptive framework that aligns well with the iterative and data-driven nature of AI development. This study examines the impact of agile adoption on TTM performance in digital innovation projects, particularly those involving AI-enabled components, using a qualitative multiple-case study design based on semi-structured interviews with product managers, developers, and agile practitioners, complemented by analysis of project documentation, AI workflows, and delivery metrics. The findings show that organizations adopting agile methodologies achieve a development cycle time reduction of approximately 25% to 40% within the first two years of implementation, supported by key practices such as iterative sprint development, cross-functional collaboration between engineering and data teams, continuous feedback loops, and early testing of AI components. Overall, the study confirms that agile methodologies serve as an effective strategic mechanism for accelerating TTM in AI-driven digital innovation projects.
Addressing the Scalability Trilemma for Mass Market Gamichain Applications Ayun Maduwinarti; Muhamad Ikhsan Mustopa; Henry Zainarthur
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v3n1

Abstract

The convergence of blockchain technology and gamification has led to the emergence of Gamichain ecosystems that are increasingly adopted in digital business environments to enhance user engagement, transparency, and value exchange. Despite this potential, the blockchain scalability trilemma, which involves trade offs between decentralization, security, and scalability, remains a critical barrier to the mass market adoption of Gamichain applications. This study aims to analyze how the scalability trilemma influences the feasibility of large scale Gamichain systems and to identify architectural strategies that enable high frequency transactions and large user participation while preserving system trust. A qualitative conceptual approach supported by comparative case analysis is employed, examining representative implementations such as Axie Infinity, StepN, and Polygon based gaming platforms. The analysis evaluates existing scalability solutions including Layer 2 rollups, sharding mechanisms, and hybrid off chain and on chain architectures across key dimensions of security, decentralization, transaction throughput, and user experience. The results indicate that Layer 2 and hybrid architectures provide the most viable balance for mass market Gamichain deployment, as they significantly improve performance efficiency and transaction cost without substantially undermining decentralization and security guarantees. This study contributes an applied evaluation framework that connects blockchain scalability discourse with practical digital business requirements. In conclusion, scalable Gamichain adoption depends not only on technical scalability solutions but also on strategic architectural alignment with user experience optimization, regulatory readiness, and sustainable digital innovation, thereby supporting broader participation in the global digital economy.
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.
Machine Learning Enabled Social Media Competitive Intelligence System Hendri Handoko; Yulina Ismiyanti; Omar Arif Al-Kamari
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/480er062

Abstract

Social media platforms generate massive volumes of publicly accessible digital data that reflect organizational competitive strategies, yet most existing competitor analyses remain manual, descriptive, and limited to surface-level engagement metrics, resulting in low scalability and weak strategic intelligence. This study proposes a Machine Learning Enabled Social Media Competitive Intelligence System designed to automate competitor strategy extraction through artificial intelligence and big data analytics. The objective is to develop a computational framework capable of identifying strategic content patterns, communication objectives, audience positioning, and paid advertising behaviors using data-driven techniques. Large-scale public data from social media posts, engagement indicators, and advertising transparency libraries are collected and processed through data preprocessing pipelines, including text normalization, tokenization, and feature extraction using TF-IDF and word embedding representations. Supervised machine learning algorithms are implemented to classify content themes, detect strategic clusters, and model competitive positioning patterns, while performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics to ensure robustness and reliability. Experimental findings demonstrate that the proposed system significantly enhances analytical consistency, scalability, and strategic insight generation compared to traditional mixed method approaches. This research contributes to the advancement of AI-driven social media analytics and establishes a computational foundation for scalable big data-based competitive intelligence systems aligned with Artificial Intelligence and Big Data domains.
Efficient Machine Learning Acceleration with Randomized Linear Algebra for Big Data Dwi Cahyono; Apriani Sijabat; Kamal Arif Al-Farouqi
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/v3i1.163

Abstract

The rapid growth of big data has significantly increased the computational complexity of machine learning models, particularly due to intensive linear algebra operations that limit scalability and efficiency. This study aims to investigate the effectiveness of Randomized Linear Algebra (RLA) as an acceleration strategy for machine learning in large scale data environments. The research adopts an experimental methodology by integrating randomized techniques such as matrix sketching and random projection into standard machine learning pipelines and evaluating their performance against deterministic baseline approaches. Experiments are conducted on large dimensional datasets using multiple machine learning models, with performance assessed in terms of computational time, memory usage, model accuracy, and scalability. The results demonstrate that the proposed RLA based approach substantially reduces computational cost and memory consumption while maintaining comparable predictive accuracy to conventional methods. These findings indicate that randomized techniques provide an effective trade off between efficiency and accuracy, enabling scalable machine learning for big data applications. In conclusion, this study contributes to the advancement of efficient Artificial Intelligence (AI) systems by demonstrating that RLA can serve as a practical and scalable solution for accelerating machine learning computations in big data contexts, aligning with the growing demand for resource efficient and high performance AI infrastructures.
Smart Contract Based Data Strategy for Startup Growth in Digital Business Systems Paroli Paroli; Muhtarom Muhtarom; Mungkap Mangapul Siahaan; Derlina Derlina; Kgomotso Moyo
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/gkh09876

Abstract

This study explores how smart contract based data strategies can support startup growth in digital business systems characterized by competition, limited resources, and increasing demand for transparency. In the context of rapid digital transformation, startups require reliable data governance mechanisms to manage transactions, automate business processes, and strengthen stakeholder trust. The background of this research highlights that conventional data management often faces challenges related to data integrity, operational inefficiency, centralized control, and limited transparency, making smart contracts a strategic alternative for supporting secure and automated digital business operations. The objective of this study is to analyze how smart contract based data strategies contribute to startup growth by enhancing data transparency, workflow automation, trust formation, and operational scalability. Employing a qualitative conceptual approach, this study synthesizes academic literature, blockchain related studies, and documented insights on smart contract implementation in digital business environments. The analysis focuses on key dimensions, including decentralized data management, automated transaction execution, transparency mechanisms, security assurance, and workflow efficiency. The results indicate that smart contracts function not merely as blockchain tools but as strategic data mechanisms that enable startups to reduce operational risks, improve process reliability, and strengthen digital business credibility. The conclusion reveals that effective smart contract implementation can support sustainable startup growth by improving data accountability, enhancing stakeholder confidence, and enabling scalable digital operations. This study contributes to blockchain and digital business literature by positioning smart contracts as strategic data capabilities for startup development.
Deep Learning Driven Big Data Architecture for Scalable Intelligent Network Threat Detection Sigit Anggoro; Palma Juanta; Ariesya Aprillia; Adele Valerry
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/vc0kmq21

Abstract

This study proposes a deep learning driven big data architecture designed to enable scalable and intelligent network threat detection in high volume traffic environments. Increasing network traffic volume and heterogeneity generated by enterprise systems, cloud services, and Internet of Things devices require more adaptive and intelligent security mechanisms beyond traditional signature-based approaches. This study aims to develop an intelligent threat-detection framework that leverages deep-learning models and big data analytics to enhance detection accuracy, scalability, and real-time response capabilities in large-scale network environments. A distributed big data architecture is integrated with advanced deep neural networks to process high-dimensional network traffic features, perform automated feature learning, and classify malicious activities using optimized training and validation strategies. The proposed framework is evaluated using benchmark intrusion detection datasets and simulated real-world network traffic scenarios to ensure robustness and generalizability. Experimental findings demonstrate that the proposed approach achieves superior detection accuracy, lower False-Positive Rates, and improved processing efficiency compared with conventional machine learning-based intrusion-detection systems. The integration of deep learning and big data analytics provides a scalable and adaptive solution for intelligent threat detection in computer networks, contributing to the development of next-generation cybersecurity systems capable of addressing evolving and sophisticated cyber attacks.
Artificial Intelligence and Big Data Framework for Cybersecurity Resilience in Distributed Networks Syaifuddin Syaifuddin; Ahmad Gunawan; Maulana Arif Komara; Agung Lorenzo
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kjn69m96

Abstract

The rapid expansion of distributed computer networks, driven by cloud computing, IoT ecosystems, edge computing, and software-defined infrastructures, has increased cybersecurity complexity. The growing volume, velocity, and variety of network data challenge traditional security mechanisms that focus primarily on threat detection, often neglecting system resilience, adaptive response, and recovery. This study develops a resilience-oriented intelligent big data analytics framework integrating Artificial Intelegence (AI), big data processing, and distributed cybersecurity monitoring to strengthen resilience in modern digital environments. A qualitative approach was employed through systematic literature review, conceptual modeling, thematic synthesis, and comparative analysis of existing architectures. The framework consists of four interconnected layers: data acquisition and aggregation, big data processing, intelligent analytics, and adaptive response and recovery. It supports continuous monitoring, anomaly detection, threat prediction, automated mitigation, and recovery orchestration. Comparative analysis indicates that prior studies focus mainly on improving intrusion detection or machine learning techniques, providing limited attention to resilience dimensions such as adaptability, fault tolerance, recovery efficiency, and operational stability. In contrast, the proposed framework integrates intelligent analytics with scalable big data infrastructures and distributed security mechanisms to create a unified resilience-oriented cybersecurity ecosystem. Findings suggest that combining AI-driven analytics, distributed processing, and adaptive security orchestration provides a strategic foundation for enhancing cybersecurity resilience, supporting sustainable digital infrastructure development, and ensuring operational stability in increasingly complex and interconnected network environments.

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