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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 4 Documents
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Search results for , issue "Vol 3 No 1 (2026): February" : 4 Documents clear
Optimization of Digital Business to Support MSMEs Growth in the Industry 4.0 Transformation Jaya, Aswadi; Saputra, Farhan; Derlina; Ramadhan, Dwi Nur; Green, Thomas
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.v3n1.147

Abstract

Digital transformation has become essential for Micro, Small, and Medium-Sized Enterprises (MSMEs) in the Industry 4.0 age in order to improve resilience and competitiveness in spite of restricted resources. The influence of digital optimization techniques on MSME growth is investigated in this study. These tactics include digital marketing adoption, e-commerce platform usage, and digital financial management tools. Data from 100 MSMEs was gathered quantitatively using structured questionnaires, and the correlations between the variables were examined using SmartPLS 3. E-commerce platform usage has the biggest impact, followed by digital financial management tools and digital marketing adoption, according to the results, which show that all digital strategies have a favorable impact on MSME growth. According to the model's R Square value of 0.694 for MSME Growth, the examined strategies account for around 69.4% of the growth variation. These results demonstrate how crucial it is for MSMEs to embrace digital technology in order to increase their market reach, boost operational effectiveness, and fortify financial management. Future research is urged to examine other factors impacting digital adoption and to apply these findings in a variety of sector scenarios. The study concludes that MSMEs must invest in digital skills in order to achieve sustainable development in a competitive digital world.
Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques Eryc; Nasib; Muh. Fahrurrozi; Ramzi Zainum Ikhsan; Parker, Jonathan
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/p8sbs746

Abstract

This study, titled Strategic Business Forecasting and Market Trends Analysis Using Machine Learning Techniques, explores how artificial intelligence (AI) particularly machine learning (ML) can enhance the accuracy and strategic impact of business forecasting in dynamic markets. Traditional statistical forecasting methods often fail to accommodate complex, nonlinear, and high-dimensional data. To address this gap, the research develops and validates a machine learning–based forecasting model designed to integrate predictive analytics into strategic decision-making. The study adopts a quantitative approach and employs Structural Equation Modeling (SEM) using SmartPLS 3 to examine the interrelationships among four latent variables: Market Trends (MT), Forecasting Accuracy (FA), Strategic Planning Efficiency (SPE), and Business Performance (BP). Each construct is measured using three indicators, forming a structural model that tests six hypothesized relationships. The results indicate that understanding market trends significantly improves forecasting accuracy and strategic planning efficiency, which in turn positively influences business performance. Furthermore, forecasting accuracy directly enhances both planning efficiency and overall performance, emphasizing the strategic value of data-driven insights. The findings validate the reliability and predictive power of the proposed model, offering a robust framework for organizations aiming to leverage machine learning in strategic forecasting. By bridging the gap between algorithmic prediction and managerial application, this study contributes to the growing field of AI-driven business analytics and supports the development of more agile, informed, and resilient business strategies in a data-centric economy.
A Framework for Mining Customer Data in Management Information Systems Untung Rahardja; Lutfiani, Ninda; Agung Rizky; Yul Ifda Tanjung; Evans, Richard
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/m5qymx32

Abstract

The exponential growth of customer data within Management Information Systems (MIS) has generated an urgent need for structured analytical approaches capable of transforming raw information into valuable insights that support decision-making across various organizational processes. This study aims to develop a comprehensive and systematic framework for mining customer data in MIS by integrating preprocessing procedures, machine learning algorithms, and model evaluation techniques into a unified analytical workflow. Using the Design Science Research methodology, the framework was designed based on existing data mining standards, developed through iterative refinement, and demonstrated using a customer-behavior dataset processed with clustering, classification, and association rule mining techniques. The findings reveal that the proposed framework improves data quality, enhances segmentation accuracy, and strengthens predictive capability, enabling MIS to deliver deeper insights into customer behavior, purchasing tendencies, and potential churn risks. Experimental results show that combining K-Means, Random Forest, and Apriori algorithms yields more comprehensive and reliable patterns compared to using a single analytical technique. The outcomes of this research highlight the practical significance of applying an integrated data mining approach in MIS, allowing organizations to optimize marketing strategies, personalize services, and make more informed managerial decisions. Overall, this study contributes to the field by offering a scalable, adaptable, and effective framework for implementing customer data mining within real-world MIS environments.
AI Framework for Synthesizing Qualitative User Feedback A Literature Review Sitorus, Santa Lusianna; Dewi, Ratih Komala; Vika Febrian; Muhammad Faris Ariq; Ikhsan, Ramiro Santiago
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.

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