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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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Utilizing Analytical Hierarchy Process (AHP) in Developing Decision Support System for Evaluating Teacher Performance Prasetyo, Doni; Marodiyah, Inggit
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.71

Abstract

One effort to measure the quality level in schools is by assessing the performance aspects of teachers as professional educators teaching in those schools. The performance aspect of teachers is measured as one of the requirements for promotion to higher positions or as a prerequisite recommendation to participate in teacher certification activities. In order for teacher performance assessment to be conducted objectively, a method that can assist in the process is required. The Analytical Hierarchy Process (AHP) method can be used to aid in decision-making. This is because the AHP method is a model for structured and comprehensive decision-making. Data from the Analytical Hierarchy Process calculation were obtained from 5 questionnaires filled out by respondents, and the final result obtained was C with a superior weight of 0.7604 or 76.04%, the second priority was obtained by B with a weight value of 0.2079 or 20.79%, and the lowest priority was obtained by A with a weight value of 0.0517 or 5.17%.
Uncovering the Efficiency of Phishing Detection: An In-depth Comparative Examination of Classification Algorithms Sugianto, Dwi; Putawa, Rilliandi Arindra; Izumi, Calvina; Ghaffar, Soeltan Abdul
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.72

Abstract

This research aims to investigate the potential security risks associated with phishing email attacks and compare the performance of three main classification algorithms: random forest, SVM, and a combination of k-fold cross-validation with the xgboost model. The dataset consists of 18,634 emails, with 7,312 identified as phishing emails and 11,322 considered safe. Through experiments, the combination of k-fold cross-validation and xgboost demonstrated the best performance with the highest accuracy of 0.9712828770799785. The email classification graph provides a visual insight into the distribution of classification results, aiding in understanding patterns and trends in phishing attack detection. The analysis of the ROC curve results indicates that k-fold cross-validation and xgboost have a higher AUC compared to random forest and SVM, signifying a better ability to predict the correct class. The conclusion emphasizes the importance of the combination of k-fold cross-validation and xgboost in enhancing email security, with the potential for increased accuracy through parameter adjustments.
Assessing Ticket.com App Usability Through the System Usability Scale (SUS) Method Putri, Shiffa Intania; Liu, Kevin
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.73

Abstract

The influence of technology has increased ease and comfort, especially in the online ticket ordering process. One of the online ticket booking platforms that is popular among users is Tiket.com. However, on the Tiket.com application, there are still various negative reviews given by users. One way to maintain an application is to pay attention to usability aspects, especially user input. So, this research aims to evaluate the Tiket.com application in terms of usability using the System Usability Scale (SUS) method as a data processing method. The results obtained from calculating the SUS score in usability evaluation were 55.55. This score shows that the usability level of the Tiket.com application is quite good, with the Adjective Ranking being in the OK category, and Acceptable at Marginal level and Grade D level. Even though the assessment results are acceptable, there are various things that need to be considered, including increasing the use of features. to function properly and pay attention to every user input. This is done so that it can have a significant impact on the Tiket.com application, especially in improving the usability aspect.
Navigating English Learning via Heutagogical Approaches in Self-Directed Learning with Technology Xuan, Zhicheng Dai; Xhu, Xiaoliang
International Journal for Applied Information Management Vol. 4 No. 1 (2024): Regular Issue: April 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i1.74

Abstract

Technology has created a demand for new learning methods in education, such as e-learning, blended learning, and flipped learning. Self-directed is one of the new learning approaches that functions primarily based on technological learning mediums. With the advent of technology, present-day learners can access several new learning mediums that expose them to language learning resources. This accessibility motivates the learners to choose the content, manage their learning activities, and assess what they learn with the support of technology. This study designs a tech-driven teaching-learning methodology by blending SDL with heutagogy and further aims to discover how much technological learning mediums help students in SDL. It also emphasizes the role of technology in developing SDL as a heutagogical approach to learning several components of the English language. A survey was conducted among the first-year engineering students of Anna University to collect data on using learning media in SDL regarding English Language Learning. Findings reveal that most students prefer technological learning mediums to learning by themselves. It also leads to the recommendation that students create awareness about SDL as a learning system that will help them promote self-paced learning.
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.
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.

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