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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
 1 
Search results for , issue "Vol. 4 No. 2 (2024): Regular Issue: July 2024" : 5 Documents clear
Job Clustering Based on AI Adoption and Automation Risk Levels: An Analysis Using the K-Means Algorithm in the Technology and Entertainment Industries Hasibuan, Muhammad Siad; Fikri, Ruki Rizal Nul; Dewi, Deshinta Arrova
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.79

Abstract

This study explores job clustering based on AI adoption levels and automation risks in the technology and entertainment industries using the K-Means algorithm. By applying K-Means clustering, jobs were grouped into five clusters based on their AI adoption and susceptibility to automation. The analysis revealed that Cluster 1, with roles such as software engineers and data scientists, exhibited higher AI adoption and lower automation risks, making these positions more resilient to automation. In contrast, other clusters reflected varying degrees of AI integration and automation vulnerability, offering insights into workforce trends. Principal Component Analysis (PCA) and a heatmap of salary distributions further highlighted the economic implications of these clusters, with Cluster 3 representing the highest-paying roles. The findings suggest the importance of tailored upskilling and reskilling strategies to address the challenges of workforce displacement in AI-driven environments. This study provides actionable insights for workforce planning in industries facing rapid technological transformation.
Sentiment Analysis of Doctor’s Responses to Patient Inquiries in a Medical Chatbot: A Logistic Regression Approach Yel, Mesra Betty; Rodhiyah
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.80

Abstract

This study addresses the challenge of improving doctor-patient communication in medical chatbot systems by integrating sentiment analysis to classify doctor responses as positive or negative. The primary objective was to develop a model that enhances the emotional intelligence and appropriateness of chatbot interactions using Logistic Regression. The model achieved 98.63% accuracy, 99.68% precision, 95.90% recall, and 97.75% F1-score, demonstrating its high reliability in classifying sentiments with minimal misclassifications. While the model performs well, further improvements could focus on reducing false negatives to increase recall. The implications of this research are significant for digital healthcare, as the model enables chatbots to provide more empathetic, context-aware responses, improving patient engagement and overall communication. The novelty of this study lies in applying sentiment analysis within medical chatbot systems, contributing to the growing field of emotional intelligence in digital healthcare. The findings highlight the potential of sentiment analysis to enhance patient interactions, making medical chatbots more effective and human-like. This study provides a solid foundation for further advancements in healthcare chatbots, demonstrating the potential of machine learning to improve the quality of doctor-patient communication in a digital context.
Using Random Forest and Support Vector Machine Algorithms to Predict Online Shopper Purchase Intention from E-Commerce Session Data Alamsyah, Reza; Wahyuni, Sri
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.81

Abstract

This study explores the use of machine learning algorithms to predict online shopper purchase intention, aiming to provide e-commerce businesses with actionable insights into consumer behavior. The Online Shoppers Purchasing Intention dataset, containing 12,330 session records from an e-commerce site, was analyzed using two classification models: Random Forest and Support Vector Machine (SVM). The models were evaluated based on key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. Results showed that the Random Forest model outperformed the SVM model, achieving an accuracy of 90.43% and a ROC AUC score of 0.94, indicating strong predictive capability. PageValues and ProductRelated_Duration were identified as the most important features influencing purchasing behavior, with higher values of these features being strongly associated with successful purchases. The study provides valuable insights into the behaviors that drive purchasing decisions in e-commerce, showing that longer engagement with product-related content and higher monetary value pages significantly increase the likelihood of conversion. While the study contributes to understanding online shopper behavior through machine learning, it is limited by the class imbalance in the dataset and the absence of more granular customer data. Future research could address these limitations by incorporating additional features and exploring deep learning models for more accurate predictions. Practical implications of the study suggest that e-commerce businesses can improve conversion rates by optimizing product-related pages and focusing on key user behaviors that are predictive of purchases.
Forecasting AI Model Computational Requirements Using Random Forest and XGBoost with Entity and Domain Characteristics Ayuningtyas, Astika; Wulandari, Rindi Nur
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.82

Abstract

This research aims to predict the computational power required by artificial intelligence (AI) models, specifically measured in petaFLOP (Floating Point Operations Per Second), based on their domain and entity characteristics. The study employs Random Forest and XGBoost regression models to predict the amount of computational power needed by AI models. Both models were trained on a dataset that includes features such as the training year, domain (e.g., Language, Vision), and entity characteristics. The results demonstrate that the Random Forest model outperforms XGBoost in terms of prediction accuracy, as indicated by higher R-squared values and lower error metrics. Feature importance analysis revealed that the year of training and domain were the most significant predictors of computational power, with the Language domain emerging as the most influential in both models. The findings highlight the potential for machine learning models to forecast AI computational requirements, which can aid organizations in optimizing computational resources for AI projects. However, the study faces limitations due to data sparsity, particularly in the target variable, and the relatively simple nature of the models employed. Future work should explore incorporating additional features, such as hardware specifications, and leveraging deep learning models to better capture the complexity of AI computational demands. This study lays the groundwork for further research into more precise predictions of AI model resource consumption, helping organizations plan their AI initiatives more effectively.
Predicting AI Service Focus in Companies Using Machine Learning: A Data Mining Approach with Random Forest and Support Vector Machine Sangsawang, Thosporn; Tang, Lin; Pasawano, Tiamyod
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.83

Abstract

This study investigates the prediction of AI service focus in companies using machine learning models. The primary objective is to predict the percentage of AI service focus based on company characteristics such as project size, hourly rate, number of employees, and geographical location. Two machine learning models, Random Forest Regressor and Support Vector Regressor (SVR), were trained and evaluated to determine their effectiveness in predicting AI adoption. The dataset consists of 3099 companies, with key features cleaned and preprocessed, including the transformation of categorical variables into numerical ones using one-hot encoding and imputation techniques applied to handle missing values. The Random Forest model demonstrated better performance, with an R² value of 0.12, indicating a modest ability to explain the variance in AI service focus. In contrast, the SVR model had a negative R² value of -0.03, suggesting that it struggled to capture the underlying relationships in the data. The analysis identified project size and hourly rate as the most significant predictors of AI service focus, with larger projects and higher hourly rates correlating with a greater emphasis on AI services. Despite the relatively low performance of both models, this research provides valuable insights into the factors that influence AI adoption. The findings emphasize the importance of project-related characteristics in determining a company's AI service focus. However, the study is limited by missing data and the absence of additional features that could further improve prediction accuracy. Future research could benefit from incorporating more business-specific features and advanced modeling techniques to enhance the predictive power and generalizability of the model.

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