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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 5 Documents
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Search results for , issue "Vol. 4 No. 3 (2024): Regular Issue: September 2024" : 5 Documents clear
Unveiling Hidden Customer Segments in E-Commerce Using DBSCAN Clustering on Demographic and Behavioral Insights Aglasia, Adimas; Agus, Isnandar
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.v4i3.85

Abstract

Customer segmentation is a crucial process in e-commerce that allows businesses to tailor their marketing strategies to specific customer groups. This research applies the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to segment customers based on their demographic and behavioral data. The dataset used includes variables such as age, annual income, total spending, and campaign engagement, which are essential for identifying meaningful patterns within the customer base. The DBSCAN algorithm was chosen due to its ability to detect clusters of arbitrary shapes and handle noise, making it ideal for complex e-commerce datasets. The analysis identified one dominant customer segment, with a small portion of the data labeled as noise, indicating that the majority of customers exhibit similar behaviors. However, the results also highlight the challenge of parameter selection for DBSCAN, as the clustering outcome was sensitive to the chosen values of ε (epsilon) and MinPts. The segmentation revealed valuable insights, such as the fact that most customers share similar characteristics in terms of spending habits and engagement, yet a few outliers exist who do not align with these patterns. These findings provide a foundation for businesses to develop targeted marketing strategies based on customer segmentation. Despite the promising results, the study acknowledges limitations in the segmentation process, particularly with the influence of outliers and the need for further tuning of the algorithm's parameters. Future research could explore hybrid clustering models that combine DBSCAN with other techniques, as well as incorporating additional behavioral features for more refined segmentation. The insights gained from this research can guide businesses in crafting personalized marketing campaigns that cater to distinct customer segments.
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.

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