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International Journal for Applied Information Management
Published by Bright Institute
ISSN : -     EISSN : 27768007     DOI : https://doi.org/10.47738/ijaim
Core Subject : Economy, Humanities, Science, Social,
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences
Journal menerbitkan penelitian tentang semua aspek manajemen informasi. Informasi dilihat di sini secara luas untuk mencakup tidak hanya produk/layanan dan proses tetapi juga pasar, dan organisasi serta informasi sosial. Ini termasuk studi tentang proses secara keseluruhan atau tahap individu, masalah seputar mengakses dan menggunakan sumber daya berwujud dan tidak berwujud secara efektif, strategi informasi, alat yang berbeda yang digunakan untuk mengelola informasi, dampak faktor industri, regional, dan nasional, dan implikasi pada kinerja. . IJAIM menyambut baik pekerjaan yang mengeksplorasi manajemen inovasi dalam konteks baru seperti tetapi tidak hanya layanan, organisasi sektor publik, dan perusahaan sosial dan komunitas, informasi sosial, pada satu atau beberapa tingkat termasuk tim atau proyek, organisasi, regional , nasional dan internasional. Makalah yang muncul di IJAIM harus didasarkan pada metode penelitian yang ketat. Mereka juga harus eksplisit tentang implikasi untuk teori dan praktek. Dengan demikian, penulis harus memastikan bahwa kontribusi terhadap keadaan seni diartikulasikan dengan jelas.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 139 Documents
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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.
Predicting the Popularity Level of Roblox Games Using Gameplay and Metadata Features with Machine Learning Models Yi, Ding; Jun, Luo; Govindaraju, S
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.v5i1.97

Abstract

The online gaming platform Roblox has become a significant player in the gaming industry, providing a space for user-generated content. Predicting the popularity of Roblox games can help developers design better games and optimize user engagement. This study explores the use of machine learning models to predict the popularity of games on Roblox using gameplay features and metadata. A dataset of 9,734 games was collected, including variables such as likes, visits, game age, and active players. Three machine learning models, Decision Tree, Random Forest, and Gradient Boosting were employed to predict the number of favorites, which serves as a proxy for game popularity. Among the models tested, Gradient Boosting outperformed the others, achieving the highest R-squared score (0.85) and the lowest Root Mean Squared Error (11,470). Key features such as likes, game age, and visits were identified as the most influential in predicting game popularity. Based on these findings, this study recommends that developers focus on features that increase player engagement, such as regular updates and optimizing game exposure. Additionally, incorporating additional data sources, such as user reviews, and exploring explainability methods like SHAP can further improve model accuracy and transparency. This research contributes valuable insights into how machine learning can support decision-making in the development and optimization of Roblox games.
K-Means Clustering for Segmenting AI Survey Respondents: Analysis of Information Sources and Impact Perceptions Evelyn; Suryodiningrat, Satrio Pradono; Tarigan, Masmur
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.v5i1.98

Abstract

This study employs K-Means clustering to analyze survey data from 91 university students, aiming to segment respondents based on their information-seeking behaviors (Question 2) and impact perceptions (Question 3) of artificial intelligence (AI). Two distinct clusters emerged: “Optimistic Problem Solvers,” who favor formal channels such as scholarly websites, peer-reviewed papers, and guided discussions, and express strong confidence in AI’s problem-solving capabilities with low concern for job displacement or dehumanization; and “Critical Watchers,” who rely more on informal, rapidly updated media (e.g., social platforms, general web searches) and exhibit heightened apprehension regarding AI’s socio-economic and ethical risks. Demographically, the former group skews toward sophomores with consistent GPAs and quantitatively oriented majors, while the latter displays broader disciplinary representation, balanced gender composition, and greater academic variability. These findings validate a dual-dimensional segmentation framework that integrates source behavior with perceptual orientation, highlighting the inadequacy of one-size-fits-all AI education. The study recommends differentiated instructional strategies, deep-dive, research-oriented modules for problem-solvers and trust-building, narrative-driven outreach for watchers, and outlines future research directions including larger, multi-institutional samples, longitudinal tracking, and mixed-methods approaches to refine and validate these profiles.
Anime Segmentation Based on User Preferences: Applying Clustering to Identify Groups of Anime with Similar Genres, Themes, and Popularity Tarigan, Riswan E; Wijaya, Yoana Sonia
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.v5i1.99

Abstract

The anime industry has experienced significant growth, with an increasing focus on user preferences for content discovery and engagement. This study applies clustering techniques, specifically K-means, to segment anime based on user preferences, genres, themes, and popularity. By analyzing a comprehensive dataset containing attributes such as user ratings, popularity, genres, and themes, the research identifies distinct groups of anime that align with varying viewer tastes. The clustering results reveal that anime can be categorized into several groups, including highly popular but critically less-acclaimed titles, well-regarded but moderately popular anime, and niche, critically acclaimed series that appeal to smaller but dedicated audiences. This segmentation allows streaming platforms to offer more personalized recommendations, enhancing user experience and engagement by matching viewers with content that best fits their preferences. Although clustering techniques provide valuable insights into anime content, the study acknowledges certain limitations, such as overlap between clusters, indicating that some anime may not fit perfectly into a single category. This highlights the need for further improvements in segmentation accuracy. The study suggests exploring hybrid clustering methods, combining K-means with other techniques, and integrating demographic data, such as age, gender, and geographic location, to refine recommendations. Overall, the application of clustering algorithms to better understand user preferences in anime offers a promising approach to developing more effective and personalized recommendation systems. This can ultimately improve user satisfaction and engagement in the rapidly growing and competitive anime streaming market.
Predicting Smartphone Prices Based on Key Features Using Random Forest and Gradient Boosting Algorithms in a Data Mining Framework Wahyusari, Retno; Azizah, Nur
International Journal for Applied Information Management Vol. 5 No. 2 (2025): Regular Issue: July 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i2.100

Abstract

This study aims to predict smartphone prices using machine learning models, specifically Random Forest and Gradient Boosting algorithms, based on various smartphone features such as internal memory, RAM, processor speed, battery capacity, and camera specifications. The dataset, consisting of 980 smartphones available in India, was preprocessed to handle missing values and categorical variables, ensuring it was ready for model training. The models were evaluated using Mean Squared Error (MSE) and R-squared (R²) scores, with Gradient Boosting outperforming Random Forest in terms of predictive accuracy. Key findings from the feature importance analysis revealed that internal memory, RAM, and processor speed were the most influential features in determining smartphone prices. The results indicate that machine learning models, particularly tree-based algorithms, are effective tools for predicting smartphone prices based on hardware specifications. This study has practical implications for businesses and consumers, as it provides insights into the factors influencing smartphone prices, helping businesses optimize pricing strategies and assisting consumers in making more informed purchasing decisions. Future research could explore deep learning models and incorporate additional features, such as market demand and consumer sentiment, to improve prediction accuracy.
Predicting Pharmaceutical Product Discontinuation Using Decision Tree and Random Forest Algorithms Based on Product Attributes Suhartono, Susilo; Nabila, Zahara
International Journal for Applied Information Management Vol. 5 No. 2 (2025): Regular Issue: July 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i2.101

Abstract

This study aims to predict the discontinuation of pharmaceutical products using machine learning models, focusing on key product attributes such as manufacturer, composition, price, and packaging. A comprehensive dataset of over 250,000 pharmaceutical products from India was analyzed, with two models—Decision Tree and Random Forest—being employed for prediction. The models were evaluated based on accuracy, precision, recall, and F1-score. The Random Forest model outperformed the Decision Tree with a higher accuracy, but both models struggled with the imbalanced dataset, showing low recall for the minority class (discontinued products). Feature importance analysis identified manufacturer and composition as the most influential factors in predicting product discontinuation. These findings offer valuable insights for pharmaceutical companies in managing product portfolios and optimizing their lifecycle strategies. Despite limitations in data quality and class imbalance, this study provides a foundation for future research, suggesting the integration of additional data sources and the application of deep learning techniques to further enhance prediction accuracy.
Clustering Netflix Shows Based on Features Using K-means and Hierarchical Algorithms to Identify Content Patterns Hayadi, B Herawan; Priyanto, Eko
International Journal for Applied Information Management Vol. 5 No. 2 (2025): Regular Issue: July 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i2.102

Abstract

This study explores clustering patterns within Netflix's movie catalog by applying K-means and hierarchical clustering algorithms. The primary objective is to identify distinct content groups based on features such as movie duration, release year, and content ratings. The dataset, which includes 5,185 Movies, was preprocessed by handling missing values, one-hot encoding categorical variables, and standardizing numerical features. Four distinct clusters were identified, with each cluster exhibiting unique characteristics. Cluster 0 primarily consists of longer, family-friendly Movies rated TV-14, while Cluster 1 contains shorter, mature Movies with a TV-MA rating. Cluster 2 represents a diverse range of TV-MA Movies with moderate durations, and Cluster 3 focuses on adult-oriented, longer Movies with an 'R' rating. These findings offer valuable insights into Netflix's content strategy, highlighting the platform's ability to cater to different audience segments based on content type and viewer preferences. The results suggest that Netflix can leverage clustering patterns to improve its recommendation system and content acquisition strategy. However, the study is limited by the absence of user-specific data and the reliance on basic metadata features. Future research could explore the integration of additional features like user ratings and apply deep learning techniques for more sophisticated clustering.
Analyzing Customer Spending Based on Transactional Data Using the Random Forest Algorithm Siddique, Quba; Wahid, Arif Muamar
International Journal for Applied Information Management Vol. 5 No. 2 (2025): Regular Issue: July 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i2.103

Abstract

This study explores customer spending behavior using transactional data from a retail dataset, employing a Random Forest Regressor to predict the total amount spent by customers. The dataset includes various customer attributes such as age, gender, and product category, alongside transactional details including quantity purchased and price per unit. Through Exploratory Data Analysis (EDA), it was found that Price and Quantity were the most significant factors influencing total spending, with other features like Age, Gender, and Product Category playing a minimal role in predicting spending behavior. The model achieved perfect accuracy, with an R-squared value of 1.000, indicating that it explained all the variance in customer spending. The findings suggest that transactional features, particularly Price and Quantity, are the key drivers of customer spending, and retailers can optimize their marketing and sales strategies by focusing on these factors. This study also highlights the importance of data preprocessing and feature engineering in enhancing model performance, though the results were limited by the lack of external and behavioral features. Future research could further explore the impact of customer loyalty, external factors, and more complex algorithms to improve predictive accuracy.
Clustering Sleep Patterns and Health Metrics Using K-Means Algorithm to Identify Profiles of Sleep Quality and Well-being in a Diverse Population Septiadi, Abednego; Prasetyo, Muhamad Awiet Wiedanto
International Journal for Applied Information Management Vol. 5 No. 2 (2025): Regular Issue: July 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i2.104

Abstract

Sleep quality is a critical element of overall health that has garnered significant attention in recent years, particularly regarding its severe impacts on both physical and mental well-being. A growing body of research underscores that insufficient or poor-quality sleep is associated with various adverse health outcomes, ranging from cognitive impairments to chronic diseases. Studies have demonstrated that the duration and quality of sleep are intricately linked to physical, social, and emotional health outcomes among various populations, including adults and children (Lallukka et al., 2018). The crucial role sleep plays in regulating both physiological and psychological processes makes it an indispensable component of health maintenance. Research indicates that sleep disturbances can impede cognitive development in children, with implications for their growth and emotional regulation (Purwanti et al., 2024). For instance, infants who achieve a higher quality of sleep demonstrate improved cognitive abilities, which can lead to better academic performance and emotional health throughout their lives (Purwanti et al., 2024). These findings suggest that interventions designed to enhance sleep quality during infancy could yield substantial long-term health benefits. Furthermore, comparable patterns have been observed in adults, where good sleep quality significantly influences psychological factors such as emotion regulation and overall mental health (Scott et al., 2021). The psychological ramifications of poor sleep quality extend beyond mere cognitive performance. Poor sleep has been linked to increased rates of anxiety, depression, and other mood disorders (Becerra et al., 2022). A meta-analysis revealed that improving sleep quality could ameliorate the symptoms of these mental health conditions (Scott et al., 2021). Moreover, chronic sleep deprivation has been identified as a precursor to substance use disorders, suggesting a complex interplay between sleep, addiction, and mental health (Freeman & Gottfredson, 2018). The challenges of maintaining sleep quality have been exacerbated by external stressors such as the COVID-19 pandemic, leading to increased anxiety and altered sleep patterns among various demographics, particularly students and healthcare workers (Fan et al., 2021). Physical health is equally affected by sleep quality. Quality sleep is pivotal for metabolic regulation, immune function, and recovery from injuries. Poor sleep has been associated with a range of chronic health issues, including obesity, diabetes, and cardiovascular diseases (Du et al., 2021). For instance, individuals who experience poor sleep tend to exhibit higher dietary risks and poor lifestyle choices, leading to serious health complications (Du et al., 2021). The immunological impacts of sleep deprivation further elucidate its role in health, indicating that compromised sleep can weaken the immune system and elevate susceptibility to viral infections such as COVID-19 (Pillay et al., 2020).
Deciphering Weather Dynamics and Climate Shifts in Seattle for Informed Risk Management Ramadani, Nevita; Nanjar, Agi
International Journal for Applied Information Management Vol. 5 No. 3 (2025): Regular Issue: September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i3.105

Abstract

This research presents a comprehensive analysis of the weather characteristics in the city of Seattle over the past few years. Through a detailed understanding of the distribution of maximum and minimum temperatures, the findings indicate significant fluctuations between summer and winter seasons. The increasing temperature trend from year to year provides insights into the potential climate changes in the region. Additionally, rainfall data reveals consistent increases over time, particularly during the winter, with significant impacts on the environment and daily life. Wind speed stability throughout the year provides insights into wind dynamics, influencing the transportation and maritime sectors. Annual averages of rainfall, sunshine hours, snowfall, and foggy days provide foundational information for long-term planning and risk management in Seattle. The percentage of rainy and clear weather throughout the year gives a comprehensive overview of the seasons, facilitating daily activity planning. Through these findings, the research aims to make a significant contribution to the understanding of the general public, natural resource managers, and economic sectors regarding the potential impacts and opportunities arising from future weather changes. It is hoped that this research can serve as a solid foundation in efforts to mitigate and adapt to the continually changing weather dynamics in the city of Seattle

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