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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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Leveraging Machine Learning to Analyze User Conversion in Mobile Pharmacy Apps Using Behavioral and Demographic Data Lestari, Sri; Setiawan, Kiki; Aula, Raisah Fajri
International Journal for Applied Information Management Vol. 4 No. 3 (2024): Regular Issue: September 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i3.86

Abstract

This study explores the use of machine learning techniques to predict user conversion in a mobile pharmacy app based on user behavior and demographic data. The analysis was conducted using two classification models: Logistic Regression and Random Forest. Key features such as time spent on the product page, adding items to the cart, and user demographics (age, gender, device type) were evaluated to determine their impact on conversion rates. Both models demonstrated strong performance, with the Logistic Regression model achieving an Area Under the Curve (AUC) of 0.88 and the Random Forest model achieving an AUC of 0.87. These results indicate that both models effectively distinguish between users who convert and those who do not, with Logistic Regression showing a slightly better overall performance. Feature importance analysis revealed that factors such as adding items to the cart and the time spent on the product page are the most significant predictors of conversion. Furthermore, demographic features like age group and device type also contributed to the model’s predictive power, although they had a smaller impact compared to user engagement features. The findings suggest that machine learning models, particularly Logistic Regression, can be utilized to predict user behavior and optimize user engagement strategies in mobile apps. The study also highlights the importance of user engagement in driving conversions and the potential for targeted marketing based on demographic data. Future work should focus on hyperparameter tuning, exploring additional algorithms, and incorporating real-time data to further enhance model accuracy and adaptability.
Analysis of Factors Influencing Fraudulent Transactions in Digital Financial Systems Using Machine Learning Models Saputra, Jeffri Prayitno Bangkit; Hidayat, Muhammad Taufik Nur
International Journal for Applied Information Management Vol. 4 No. 3 (2024): Regular Issue: September 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i3.87

Abstract

This paper explores the use of machine learning, specifically the Random Forest algorithm, to detect fraudulent transactions in digital financial systems. As digital finance grows, the risk of fraud increases, making effective detection systems crucial for maintaining trust and security. The study focuses on identifying key factors influencing fraudulent transactions, such as transaction amount and type, and evaluates the model's performance using accuracy, precision, recall, F1-score, and AUC-ROC metrics. Results show that Random Forest outperforms traditional methods, achieving high accuracy of 95%, precision of 1.00 for fraudulent transactions, and an AUC of 0.98, indicating excellent discriminatory power. By analyzing transaction data, the model identifies important patterns, offering financial institutions practical insights for enhancing fraud detection systems. The findings suggest that focusing on critical features like transaction amount and transfer type can optimize detection systems. However, limitations include the need for further exploration of additional features, such as user behavior, and the integration of more advanced techniques to address emerging fraud tactics. The study’s outcomes provide a robust framework for improving fraud detection in the evolving landscape of digital transactions.
Sentiment Analysis on Job Descriptions in the Technology Sector: Measuring Positive and Negative Perceptions of Companies Using Natural Language Processing Techniques Yang, Liu; Pigultong, Matee
International Journal for Applied Information Management Vol. 4 No. 3 (2024): Regular Issue: September 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i3.88

Abstract

Sentiment analysis in job descriptions plays a critical role in shaping employer branding and recruitment strategies. This study investigates the sentiment of job postings in the technology sector using NLP techniques, focusing on the emotional tone of descriptions across various job types, companies, and subcategories. The analysis reveals that positive sentiment predominates in job descriptions, with a clear trend towards using optimistic language to attract candidates. The findings show that Software Development positions tend to have the most positive tone, while roles such as IT Management exhibit a more balanced sentiment. Additionally, the use of inclusive language, such as "equal opportunity" and "years of experience", is prevalent in the descriptions, highlighting the growing importance of diversity and inclusivity in recruitment. Visualization tools like word clouds and trend analysis illustrate how sentiment shifts over time, with a noticeable increase in positive sentiment from 2020 onwards. The results underscore the potential of sentiment analysis and NLP in optimizing recruitment processes, aligning job descriptions with candidate expectations, and enhancing employer branding strategies.
Applying K-Means Clustering to Group Jobs Based on Location and Experience Level: Analysis of the Job Recommendation Kumar, Vinoth; S, Priya
International Journal for Applied Information Management Vol. 4 No. 3 (2024): Regular Issue: September 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i3.89

Abstract

Labor market analysis plays a crucial role in helping job seekers identify employment opportunities that align with their qualifications, location, and experience level. This study uses the K-Means clustering algorithm to group jobs based on these critical factors. By analyzing job market data, the research identifies the most sought-after skills across various industries and highlights the geographic and experience-level disparities in job availability. Key findings include the high demand for foundational skills such as customer service, sales, and production planning, as well as more specialized skills like Medical Research in certain sectors. The study provides actionable insights for job seekers and policymakers, suggesting that targeted skill development and training programs are essential for improving job match quality. However, the study also acknowledges its limitations, such as the lack of consideration for broader economic and social factors that influence labor market trends. Future research is recommended to address these gaps, using more comprehensive datasets and advanced analytical techniques.
Optimization of Fraud Detection in E-Commerce: A CGAN Data Augmentation Approach to Address Class Imbalance Zulham; Yasir, Amru
International Journal for Applied Information Management Vol. 4 No. 4 (2024): Regular Issue: December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i4.90

Abstract

The rapid growth of e-commerce has increased the risk of fraud in online transactions, resulting in significant financial losses and decreased consumer trust. One of the main challenges in fraud detection is data imbalance, where the number of legitimate transactions far exceeds fraudulent transactions. This imbalance causes machine learning models to fail in accurately identifying fraudulent transactions. This study aims to evaluate the effectiveness of Conditional Generative Adversarial Network (CGAN) in improving fraud detection performance in e-commerce through data augmentation. Two machine learning algorithms, Random Forest (RF) and XGBoost, were used to classify transactions in both the original imbalanced dataset and the dataset augmented with CGAN. The study uses key evaluation metrics, including accuracy, precision, recall, and F1-score, to measure the model's performance. The results show that data augmentation using CGAN significantly improved the performance of both models. RF on the augmented dataset achieved an accuracy of 99.96%, precision of 99.93%, recall of 99.99%, and F1-score of 99.96%. Meanwhile, XGBoost achieved an accuracy of 99.93%, precision of 99.91%, recall of 99.94%, and F1-score of 99.92%. The main contribution of this study is to demonstrate that CGAN can effectively address the challenge of data imbalance and improve the reliability of fraud detection systems in e-commerce. This approach has the potential to be applied in various sectors facing similar issues, such as anomaly detection in finance and cybersecurity.
Analysis of Demographic and Consumer Behavior Factors on Satisfaction with AI Technology Usage in Digital Retail Using the Random Forest Algorithm Priyanto, Eko; Saekhu, Ahmad; Prasetyo, Priyo Agung
International Journal for Applied Information Management Vol. 4 No. 4 (2024): Regular Issue: December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i4.91

Abstract

The rapid integration of artificial intelligence (AI) into digital retail has reshaped consumer interactions, enabling personalized services and operational enhancements. This study investigates the demographic and behavioral factors influencing consumer satisfaction with AI technologies in digital retail, using the Random Forest classification algorithm for predictive modeling. After comprehensive preprocessing and hyperparameter tuning through grid search cross-validation, the Random Forest model achieved an overall accuracy of 83%. While the model showed strong performance for predicting satisfied consumers yielding a precision of 0.84, recall of 0.97, and F1-score of 0.90, it performed poorly in identifying dissatisfied users, with a recall of only 0.27 and F1-score of 0.39, highlighting a class imbalance issue. Feature importance analysis revealed that experiential factors, particularly enhanced AI experience and preference for online services, significantly influenced satisfaction levels, whereas demographic variables such as age and gender had limited predictive value. These findings emphasize the need for digital retailers to focus on user-centric design and service personalization, rather than demographic segmentation alone, to enhance customer satisfaction and loyalty. Furthermore, the study contributes methodologically by demonstrating the effectiveness of Random Forest in handling complex consumer datasets and theoretically by validating TAM and Customer Satisfaction Theory in the context of AI adoption. Despite limitations related to class imbalance and sector-specific data, this research offers actionable insights for retailers, marketers, and system developers aiming to improve AI-driven service quality and consumer engagement. Future studies are encouraged to address these limitations through the inclusion of emotional and contextual variables and by expanding the analysis to other industries for broader applicability.
User Profiling Based on Financial Transaction Patterns: A Clustering Approach for User Segmentation Pratama, Satrya Fajri; Putri, Nadya Awali
International Journal for Applied Information Management Vol. 4 No. 4 (2024): Regular Issue: December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i4.92

Abstract

User profiling based on financial transaction patterns is crucial for improving customer segmentation and personalizing financial services. This study uses clustering techniques, specifically K-means, to analyze transaction data and segment users based on transaction amounts, times, and types. Three clusters were identified, each demonstrating distinct transaction behaviors: Cluster 0, primarily focused on purchases and occurring early in the week; Cluster 1, which emphasizes transfers and higher transaction amounts, typically occurring mid-week; and Cluster 2, similar to Cluster 0 but with a preference for later-week transactions. The analysis demonstrates that transaction patterns, including amount, time, and type, provide valuable insights for targeting specific user groups with personalized marketing strategies and financial products. The study also highlights the importance of improving clustering accuracy, as indicated by the moderate Silhouette Score of 0.33, suggesting that further refinement in the clustering methodology could lead to more distinct user segments. The findings of this study emphasize the potential for clustering techniques to enhance user profiling, ultimately improving business strategies, customer satisfaction, and loyalty. Limitations of the study, including the dataset’s single-month duration, suggest that further research incorporating larger and more diverse datasets, as well as alternative clustering techniques, could offer deeper insights into user behavior and refine segmentation strategies.
Assessing Sentiment in YouTube Video Content: A Title and Description Analysis Approach to Analyze User Reactions Sanyour, Rawan; Abdullah, Manal; El Emary, Ibrahiem M. M.
International Journal for Applied Information Management Vol. 4 No. 4 (2024): Regular Issue: December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i4.93

Abstract

This study investigates the relationship between sentiment in YouTube video titles and descriptions and user engagement metrics, such as view count, like count, and comment count. The findings reveal that videos with positive sentiment generally attract higher levels of engagement, including more views, likes, and comments, while videos with negative sentiment typically receive lower interaction levels. The research emphasizes the importance of emotionally resonant content, suggesting that content creators should focus on producing videos with positive emotional tones to maximize audience interaction. Additionally, the study highlights the significance of well-crafted titles and descriptions as key drivers of engagement, as these textual elements influence viewers' initial expectations and emotional reactions. However, the study is limited to analyzing titles and descriptions, which may not fully capture the emotional tone of the video itself. Future research should incorporate the actual video content and explore additional engagement metrics, such as shares and watch time, for a more comprehensive understanding of viewer behavior. Despite these limitations, the study provides valuable insights that can guide content creators in tailoring their video content and metadata to foster greater viewer engagement and content success.
Implementation of Machine Learning Algorithms for Detecting Bot and Fraudulent Accounts on Instagram Based on Public Profile Characteristics Maidin, Siti Sarah; Xing, Zhang; Lie, Ye
International Journal for Applied Information Management Vol. 4 No. 4 (2024): Regular Issue: December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i4.94

Abstract

The rapid growth of Instagram as a social media platform has led to increased challenges related to fake accounts, including bots, spam, and scam profiles, which threaten the integrity and trustworthiness of online information. This study implements machine learning algorithms, particularly the Random Forest classifier, to detect and classify Instagram accounts into four categories: Real, Bot, Spam, and Scam, based on publicly available profile characteristics. A dataset of 15,000 Instagram profiles was collected and preprocessed, extracting features such as follower count, following count, posting frequency, and presence of profile information. The Random Forest model was trained and evaluated, achieving an accuracy of 97% with high precision and recall across all categories. Behavioral analysis revealed distinct patterns in following/follower ratios, posting activity, and mutual friends that differentiate genuine users from fake accounts. Feature importance ranking highlighted follower count as the most influential attribute for classification. The model demonstrated strong robustness through ROC and Precision-Recall curves, underscoring its effectiveness in a multiclass classification task. This approach not only enhances automated detection and moderation of malicious accounts but also contributes to maintaining a safer social media environment by mitigating misinformation and fraud. Future work could improve detection by incorporating temporal activity data, linguistic analysis, and real-time monitoring to adapt to evolving deceptive behaviors. Taken together, this study confirms the viability of machine learning methods in addressing the growing issue of fake accounts on Instagram, offering scalable and interpretable solutions for social media security.
Enhancing Minority Class Prediction in Wearable Sensor-Based Activity Recognition Using SMOTE Oversampling Sarmini; Widiawati, Chyntia Raras Ajeng; Yunita, Ika Romadoni
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.v5i1.95

Abstract

Wearable sensor-based activity recognition has become increasingly important in various domains, particularly healthcare and sports. However, a significant challenge in this field is the issue of class imbalance, where minority activity classes are underrepresented compared to majority classes in datasets. This imbalance leads to biased classifiers that struggle to accurately identify rare but critical activities, which is especially problematic in health monitoring scenarios. This study evaluates the effectiveness of the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance in the mHealth dataset, which contains multi-sensor data from wearable devices placed on the chest, left ankle, and right lower arm. We employ the XGBoost classifier combined with SMOTE oversampling to improve recognition performance for minority classes. Model evaluation is conducted using precision, recall, F1-score, Area Under the Precision-Recall Curve (AUC-PR), ROC curve, and calibration analysis. The results demonstrate that applying SMOTE improves minority class recall from 0.75 to 0.85 and F1-score from 0.796 to 0.865, despite a slight decrease in overall accuracy from 97% to 96.5%. The AUC-PR also increases from 0.81 to 0.88, indicating a better balance in detecting minority and majority classes. Calibration curves reveal that probability estimates still require refinement to be more reliable for decision-making. This study confirms the efficacy of SMOTE in mitigating class imbalance in wearable sensor-based activity recognition and provides valuable insights for developing more accurate and fair health monitoring systems.

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