Article Per Year (5 Year)
p-Index From 2020 - 2025
1.235
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Performa : Jurnal Manajemen dan Start-up Bisnis IJIIS: International Journal of Informatics and Information Systems International Journal for Applied Information Management Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Izumi, Calvina
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Misconceptions of metaverse: from etymology to technology Putawa, Rilliandi Arindra; Izumi, Calvina; Sugianto, Dwi; Ghaffar, Soeltan Abdul
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp314-320

Abstract

The emergence of the metaverse in society is followed by certain confusions, whereas the line between virtual reality and the metaverse remains unclear. Ironically, this has affected the development of the metaverse itself, focusing more on virtual reality while being one of its side components. This has led to the concept losing popularity compared to artificial intelligence technology. This research is a qualitative study that aims to explore the issues at the root of misconceptions and reconstruct the true meaning of the metaverse itself. This research indicates that the misconception already existed when the term was first used alongside virtual reality technology. The term "meta" refers to a higher reality, whereas the terms "digiverse" or "virtuverse" can be used, considering that the terms "digital" and "virtual" can refer to realities lower than the universe.
Uncovering the Efficiency of Phishing Detection: An In-depth Comparative Examination of Classification Algorithms Sugianto, Dwi; Putawa, Rilliandi Arindra; Izumi, Calvina; Ghaffar, Soeltan Abdul
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.72

Abstract

This research aims to investigate the potential security risks associated with phishing email attacks and compare the performance of three main classification algorithms: random forest, SVM, and a combination of k-fold cross-validation with the xgboost model. The dataset consists of 18,634 emails, with 7,312 identified as phishing emails and 11,322 considered safe. Through experiments, the combination of k-fold cross-validation and xgboost demonstrated the best performance with the highest accuracy of 0.9712828770799785. The email classification graph provides a visual insight into the distribution of classification results, aiding in understanding patterns and trends in phishing attack detection. The analysis of the ROC curve results indicates that k-fold cross-validation and xgboost have a higher AUC compared to random forest and SVM, signifying a better ability to predict the correct class. The conclusion emphasizes the importance of the combination of k-fold cross-validation and xgboost in enhancing email security, with the potential for increased accuracy through parameter adjustments.
Pengaruh Workplace Ostracism, Workplace Loneliness melalui Cyberloafing terhadap Employees’ Performance pada Digital Agency Izumi, Calvina
PERFORMA Vol. 8 No. 4 (2023): Performa
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/jp.v8i4.3981

Abstract

Peran internet dalam industri digital marketing agency dapat membawa dampak positif dan negatif. Persaingan digital yang makin ketat terus mendorong digital agency untuk menciptakan ide kreatif dan inovasi menarik agar dapat bersaing. Dalam tingginya tekanan, tanpa disadari internet menjadi celah yang digunakan semena-mena oleh karyawan perusahaan, cyberloafing. Penelitian ini dilakukan untuk menguji pengaruh Workplace Ostracism, Workplace Loneliness terhadap Employees’ Performance melalui Cyberloafing pada startup Digital Agency. Responden penelitian ini berasal dari seluruh populasi PT XXX yang merupakan startup yang menerapkan sistem kerja hybrid dan dominan menggunakan internet. Variabel yang digunakan pada penelitian ini yaitu Workplace Ostracism, Workplace Loneliness sebagai variabel independen, Employees’ Performance variabel dependen, dan Cyberloafing sebagai variabel mediasi. Metode yang digunakan dalam penelitian ini yaitu metode analisis berupa Partial Least Square (PLS) dengan software SmartPLS. Metode pengambilan sample dilakukan dengan sampling jenuh melalui pembagian kuesioner kepada 53 karyawan PT XXX. Berdasarkan analisis yang dilakukan, diperoleh hasil bahwa Workplace Ostracism, Workplace Loneliness, dan Cyberloafing berpengaruh signifikan terhadap Employees’ Performance, Cyberloafing tidak dapat mendukung mediasi pengaruh baik Workplace Ostracism dan Workplace Loneliness terhadap Employees’ Performance pada karyawan PT XXX.
A Quantitative Analysis of Artificial Intelligence’s Impact on Students’ Mindset and Critical Thinking in Higher Education Prambudi, Niko Lugas; Putawa, Rilliandi Arindra; Izumi, Calvina
International Journal of Informatics and Information Systems Vol 7, No 4: December 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i4.222

Abstract

The rapid advancement of Artificial Intelligence (AI) has significantly transformed higher education, redefining how students learn, reason, and engage with academic content. This study investigates the impact of AI utilization on students’ mindsets and critical thinking skills within university learning settings. Employing a quantitative research design, data were gathered through an online questionnaire administered to 28 students from various academic disciplines. The survey assessed students’ engagement with AI tools including ChatGPT, Gemini, and Perplexity in learning processes such as understanding course materials, completing assignments, and problem-solving activities. The results indicate that most participants perceive AI as highly beneficial for enhancing comprehension, efficiency, and creativity in academic work. Students report that AI applications help them approach problems from diverse perspectives and stimulate idea generation. Nevertheless, concerns about overdependence are evident, as 53.6% of respondents believe that excessive reliance on AI may diminish autonomy and critical reasoning capacity. While a majority of students claim to verify AI-generated responses, a minority remain unaware of biases and inaccuracies, emphasizing the need to strengthen AI literacy in academic contexts. Overall, the findings suggest that AI serves as both a catalyst for deeper learning and a potential risk to intellectual independence. Its integration into higher education must therefore be approached with pedagogical mindfulness, ensuring that AI acts not as a replacement for human thought but as a tool for reflection, creativity, and metacognitive growth. Educators are encouraged to design learning experiences that require students to analyze, compare, and critique AI outputs critically. In conclusion, AI represents a dual-edged innovation: when applied ethically and reflectively, it can foster a growth-oriented mindset and strengthen critical thinking, but without proper guidance, it may cultivate intellectual complacency and dependency.
Enhancing Customer Satisfaction and Product Quality in E-commerce through Post-Purchase Analysis using Text Mining and Sentiment Analysis Techniques in Digital Marketing Izumi, Calvina; Ghaffar, Soeltan Abdul; Setiawan, Wilbert Clarence
Journal of Digital Market and Digital Currency Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i1.26

Abstract

This study explores the application of text mining and sentiment analysis to enhance product quality and customer satisfaction within the e-commerce landscape. Using the Customer360Insights dataset, which comprises 236 records of customer interactions, demographic details, product information, and transactional data, we identified key drivers of negative feedback and returns. The descriptive statistics revealed a diverse customer base with an average age of 45.33 years and significant variability in monthly income ($5,470.24 ± $1,442.80). The text mining process, including tokenization and term frequency analysis, identified frequent terms such as "poor" (95 occurrences), "arrived" (92 occurrences), and "damaged" (45 occurrences). Sentiment analysis using VADER and TextBlob indicated that 80.08% of the feedback was negative, highlighting pervasive dissatisfaction. Topic modeling using Latent Dirichlet Allocation (LDA) revealed five main topics, consistently emphasizing issues like product quality and delivery timeliness. Common return reasons included poor value (55 occurrences), wrong item delivered (49 occurrences), and late arrivals (47 occurrences). These insights suggest critical areas for improvement, such as enhancing quality control, optimizing logistics, and refining pricing strategies. The findings have significant implications for digital marketing strategies, emphasizing the need for targeted interventions to improve customer satisfaction. By addressing identified issues and leveraging data-driven insights, e-commerce businesses can enhance their product offerings, optimize post-purchase support, and foster customer loyalty. Future research should validate these findings using real-world data and explore additional data mining techniques to provide a comprehensive understanding of customer satisfaction drivers.
Analyzing Price Volatility of Hedera Hashgraph Using GARCH Models: A Data Mining Approach Izumi, Calvina; Setiawan, Wilbert Clarence; Ghaffar, Soeltan Abdul
Journal of Current Research in Blockchain Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i2.35

Abstract

This study employs the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to analyze the volatility dynamics of Hedera Hashgraph, a prominent cryptocurrency. Using a dataset of 1,901 daily price observations, we investigate the presence of volatility clustering and the persistence of market shocks, which are hallmarks of financial markets. The GARCH(1,1) model demonstrates robust performance, with a Log-Likelihood of 2927.50, AIC of -5846.99, and BIC of -5824.79, confirming its suitability for volatility estimation. Key findings reveal significant volatility clustering, with alpha (α = 0.20) and beta (β = 0.78) indicating moderate sensitivity to recent shocks and high persistence of volatility, respectively. Visualizations of conditional volatility and historical price data highlight the inverse relationship between price stability and volatility, with high volatility periods accounting for 33% of the dataset. These insights underscore the importance of real-time volatility monitoring for risk management and investment strategies. The study concludes by suggesting future research directions, including the integration of GARCH models with machine learning techniques and the exploration of external factors influencing cryptocurrency price dynamics.
In-Depth Analysis of Web3 Job Market: Insights from Blockchain and Cryptocurrency Employment Landscape Izumi, Calvina; Hariguna, Taqwa
International Journal Research on Metaverse Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i1.4

Abstract

The emergence of Web3, underpinned by blockchain technology, has reshaped the digital realm, ushering in a decentralized and trustless internet paradigm. In this paper, we conduct an extensive analysis of the Web3 job market, leveraging data from 2000 job postings to delineate prevalent keywords, sought-after skills, prevalent job titles, and salary determinants. Our examination reveals compelling insights into the job landscape, showcasing the dominance of technical competencies such as Ethereum proficiency and software development expertise. Among the top skills sought by employers, Ethereum (371 occurrences), React (213 occurrences), NFT (213 occurrences), Java (205 occurrences), and Rust (102 occurrences) prominently feature. Moreover, our analysis uncovers the ascendancy of specialized roles in cybersecurity, technical leadership, and project management, which command premium compensation levels. Notably, security positions emerged as the highest paying roles (average salary: $153,295.86), followed by tech lead (average salary: $121,526.32) and operations (average salary: $120,396.55). These findings offer valuable insights for job seekers, employers, educators, and policymakers navigating the evolving Web3 job landscape. By delineating key trends and challenges, our study contributes to a nuanced understanding of the transformative potential of Web3 and its implications for the future of work.
Co-Authors Ghaffar, Soeltan Abdul Prambudi, Niko Lugas Putawa, Rilliandi Arindra Setiawan, Wilbert Clarence Sugianto, Dwi Taqwa Hariguna