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p-Index From 2020 - 2025
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All Journal IJIIS: International Journal of Informatics and Information Systems International Journal for Applied Information Management International Journal Research on Metaverse
Haryani, Calandra
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Predicting IMDb Ratings of One Piece Episodes Using Regression Models Based on Narrative and Popularity Features Hery; Haryani, Calandra
International Journal for Applied Information Management Vol. 5 No. 1 (2025): Regular Issue: April 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i1.96

Abstract

This study explores the predictive modeling of IMDb ratings for episodes of the anime One Piece using a linear regression approach grounded in narrative and popularity-based features. The dataset comprises 1,122 episodes, with features including story arcs, episode types, and the number of viewer votes. After one-hot encoding categorical variables and training the model using Ordinary Least Squares (OLS), the model achieved a high coefficient of determination (R² = 0.855), a low Mean Absolute Error (MAE = 0.216), and Root Mean Squared Error (RMSE = 0.329). These results indicate a strong predictive performance based on limited but interpretable features. The findings reveal that narrative structure especially arc classification and viewer engagement contribute significantly to the perceived quality of episodes. While vote counts show limited correlation with ratings, combining them with narrative elements yields reliable predictions. This research offers a novel contribution to anime-based media analytics, emphasizing that minimal feature sets can provide robust predictive insight. The study also opens opportunities for enhancing content strategies and viewer understanding in serialized storytelling.
A Quantitative Study on Social Media Usage Patterns and Their Effects Among Internet Users Prasetya, Tegar Yudha; Hery, Hery; Haryani, Calandra
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.218

Abstract

This research conducts a quantitative analysis of social media usage habits and their effects among internet users, utilizing a secondary dataset of 999 respondents drawn from the Social Media Usage Survey available on Kaggle. Employing a descriptive–survey design, the study adopts a quantitative approach to examine behavioral tendencies, demographic variations, and relationships among variables such as usage duration, user motivation, privacy awareness, and intentions to reduce social media activity. Data analysis was performed using Python, incorporating descriptive statistics, crosstab analysis, and visual analytics through the Pandas, Matplotlib, and Seaborn libraries. The findings reveal that social media is deeply embedded in everyday routines, with users averaging 3.5 hours of screen time per day. Instagram, Facebook, and Twitter/X emerge as the most frequently used platforms, serving purposes that include entertainment, information access, and business promotion. Video-based content dominates user preferences, reflecting the broader global media consumption trend. Additionally, 69% of respondents acknowledge that social media influences their purchasing behavior, while 65% express moderate to high levels of privacy concern. Notably, about 68% of users report an intention to reduce their screen time, indicating a growing awareness of the need for digital balance. Correlation analysis shows that individuals with longer screen durations and heightened privacy concerns are more likely to intend reducing their usage, suggesting that excessive engagement may drive self-regulatory behavior. These insights illustrate the dual nature of social media—as a medium for empowerment and connectivity, yet simultaneously a potential source of psychological fatigue. Overall, this study contributes empirical evidence supporting efforts to foster healthy and responsible digital engagement, enriching the broader discourse on digital well-being, online literacy, and sustainable technology use in the modern digital landscape.
User Transaction Patterns in Smart Contracts Based on Call Frequency and Transfer Value Hery, Hery; Haryani, Calandra
International Journal Research on Metaverse Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i3.35

Abstract

Smart contracts are integral to blockchain technology, enabling decentralized and automated transactions. This study examines 1,000 smart contracts by analyzing metrics such as total transactions, unique users, total value transferred (ETH), gas consumption, and call frequency. Total transactions range from 1 to 18,902, with unique users spanning 1 to 14,839. The average total value transferred is 3,245.87 ETH, peaking at 7,850.16 ETH, while gas consumption averages 25,486,392 units with a maximum of 58,471,065 units. Strong correlations were identified between transaction volume (r = 0.78), user engagement, and gas consumption. Clustering analysis categorizes contracts into low, moderate, and high-activity groups, while anomaly detection highlights 32 contracts with unusual behaviors, indicating inefficiencies or vulnerabilities. These findings emphasize the importance of optimizing smart contract designs to improve efficiency, security, and scalability. The study provides actionable insights into operational patterns and proposes future research directions, including design optimization, real-time monitoring, cross-platform analysis, and machine learning applications for predictive modeling. By addressing these aspects, this research contributes to the ongoing development of robust and efficient decentralized systems.
Co-Authors Hery Hery Hery Prasetya, Tegar Yudha