International Journal of Education and Management Engineering @ijeme
Статьи журнала - International Journal of Education and Management Engineering
Все статьи: 676
Employee attendance monitoring system by applying the concept of Enterprise Resource Planning (ERP)
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Enterprise Resource Planning (ERP) is a business process management system that integrates and automates the activities of an organisation in terms of its technology, human resources and services. ERP provides an integrated environment that links the business processes of different departments of an organisation into one unit so that departments benefit from each other through their joint transactions. This study applies the ERP concept to create an employee attendance monitoring system (EAMS). Three departments, HR, Finance and the Director of Administration, were linked using the EAMS. The traditional system of attendance monitoring was time consuming and required greater effort. This is because the attendance report needed to be printed from the HR department and then sent to the Finance Department for any necessary actions (i.e. salary deduction). The EAMS will automate the whole process, thus resulting in fair decisions in less time. In the traditional system, the any delay in calculations of attendance can be unwarranted or maintaining an accurate time record can be difficult as it is manually updated. To solve these issues, a computer based monitoring system is required to establish accuracy and fairness. For that purpose, we designed and developed the EAMS. The EAMS automatically calculates any delay in employee attendance using the concept of ERP systems. More precisely, this system will calculate the delay in the attendance within set rules, as defined and applied by the different departments in the organisation. The attendance times will be checked automatically by the system: if there is any delay which violates the set rules of the organisation, necessary action will be taken automatically against the employee in terms of salary deduction or other notifications. To apply the EAMS, we constructed a case study at the “Faculty of Economics and Business Administration at King Abdulaziz University, (Female Section)”. Later, it is hoped, that this system can be used in other organisations based on their needs and enhancement to the existing framework. The expected results of this system are that it will save time and effort for all employees at the Faculty of Economics and Administration. In addition, there will be a reduction of errors in the attendance reports.
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Recent technological advancements have fueled a notable increase in credit card usage, consequently amplifying the prevalence of credit card fraud in both offline and online transactions. Although measures such as PIN codes, embedded chips, and supplementary keys like tokens have enhanced credit card security, financial institutions are compelled to bolster their usage controls and deploy real-time monitoring systems to promptly identify and mitigate suspicious activities. This study explores the utilization of ensemble methods, incorporating the k-nearest neighbors (KNN), Random Forest (RF), and Logistic Regression (LR) models, along with the Isolation Forest (iForest) algorithm, to enhance the efficacy of credit card fraud detection. Additionally, automated parameter optimization using GridSearchCV is employed to fine-tune the iForest model parameters. By integrating multiple classifiers into an ensemble approach and automating parameter tuning for the iForest model, our research aims to provide a robust solution capable of adapting to varying datasets and improving fraud detection accuracy. Through empirical analysis and comparison of individual models with the ensemble approach, we underscore the significance of ensemble learning and parameter optimization in enhancing fraud detection capabilities, thereby contributing to the advancement of financial security measures in the realm of credit card transactions.
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One of the effects of the rapid adoption of the cashless policy in Nigeria and the introduction of new naira notes is operational difficulties among financial institutions, which have led to a significant increase in ATM card theft and fraud among clients. Absence of real-time analysis of access points, combined with the intermittent and simultaneous quality of fraudulent dealings, are two major factors that make conventional fraud detection systems fail regularly. Towards reducing ATM fraud, this paper will present a high-performance, intelligent based, AI-based model to integrate three factors of biometric authentication, spending pattern analysis, and password verification into a three-factor model. Results of experiments based on real banking data prove that the proposed solution is superior to traditional models in terms of accuracy, precision, recall, and F1-score. The model uses an optimized Bi -Directional Long Short-Term Memory (BiLSTM) network to analyze historical ATM transaction records and identify behavioral abnormalities that could point to fraud. A Cuttlefish Optimization (MCFA) algorithm that is based on mapping is used to fine-tune the parameters, thus improving the reliability and accuracy of the classification. Biometric verification combined with behavioral modeling using AI stands out as a scalable and dependable framework of minimizing ATM card fraud and instilling confidence within the banking industry.
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This paper introduces a novel approach an AI-powered Multi-Agent System (MAS) for dynamically optimizing support to enhance real-time travel reservation-side customer experience. It has an architecture with specialized agents working together under a centralized agent manager, including natural language processing, booking, optimization, and context-aware modules. The system proposes to address common constraints encountered in traditional travel platforms: delayed response to user queries, ambiguity treated poorly, and adaptation to user preferences not incorporated. Through simulated environments and realistic use cases, the MAS enables complex travel requests to be dealt with, availability to be changed dynamically, and user satisfaction to be enhanced. The modular architecture design allows easy integration into larger smart tourism infrastructures. This study thus pushes the frontier further by merging AI, multi-agent collaboration, and user-centered design in a time-sensitive application world. Future directions include adaptive learning agents, multilingual interaction capabilities, and broadening the domain applications to hotel management and intelligent itinerary planning.
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Enhancing Employee Engagement through Machine Learning: Insights from K-Means Clustering Analysis
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This study provides insight into how machine learning methods, in particular k-means clustering algorithm could contribute to greater degree of employee engagement in the businesses. Using Work-Life Balance, Environment Satisfaction and Job Satisfaction found in employee survey data as an illustrative lens of the engagement phenomenon, patterns are identified that differ from traditional perspectives with implications for organizational actions. The study categorizes workers in clusters and identifies the significant gaps of satisfaction among them, using k-means clustering. Logistic regression analysis is used for the prediction of attrition risk, which also helps in determining factors responsible behind employee retention. The findings reveal the importance of understanding such facilitators to generate targeted interventions and strategies that foster a positive work environment and improve organisational performance. This approach ensures less attrition risks, and better job satisfaction leading to greater overall organisation productivity / wellbeing.
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Enhancing Institutional Quality Assessment in Higher Education Using LSTM-NMPSO Hybrid Model
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Advancements in educational assessment methodologies, driven by high-speed data networks, have enabled the efficient management and analysis of large datasets, replacing traditional testing methods. Even though they are frequently used, traditional statistical methods have the potential to incorporate biases into assessments of the caliber of universities. To address these limitations, the application of automated technologies is necessary for identifying key factors influencing institutional quality. Developing effective educational programmes in higher education requires quality assurance. Academic performance evaluation using Machine Learning (ML) and Artificial Intelligence (AI) techniques yields more accurate predictive models than traditional methods. This research proposes a hybrid approach that integrates Long Short-Term Memory (LSTM) neural networks with Novel Modified Particle Swarm Optimization (NMPSO) to optimize model architecture, enabling more precise and unbiased assessments of institutional quality. The objective of the proposed methodology is to improve the objectivity and reliability of institutional quality evaluations in higher education.
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The Indonesian Election is one of the most anticipated political contestations among the Indonesian people. Mainly because the results of the Indonesian Election are leaders in Indonesia ranging from governors and legislative members to the president and vice president of Indonesia, who will lead the next five years, considering the importance of the five-year agenda, the dissemination of good information about work programs, the activities of prospective leaders who will elect in the 2024 election and various news stories are starting to spread on Twitter. Based on this, this research aims to analyze public sentiment on Twitter wa The research method used is SMOTE-Tomek Links to overcome imbalanced data. In contrast, sentiment analysis uses Binary Logistic Regression. Evaluation related to this model measures accuracy and ROC Curves. The evaluation results show that the SMOTE-Tomek Links method is less than optimal for the data used in the research, namely the 2024 election data, with an accuracy value of 0.581 for training data and 0.406 for testing data. Undersampling methods such as Tomek Links and Random (undersampling) show higher values when combined with Binary Logistic Regression in analyzing the sentiment produced in this study, namely 0.983 and 0.938 for the Tomek Links method and 0.964 and 0.902 for the Random (undersampling) method, respectively -each for training and testing data.
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Enhancing Student Performance Prediction with ANN-Based Transfer Learning
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Predicting student performance in higher education is challenging when data distributions differ across cohorts or programs. This paper proposes an adaptive transfer learning framework to improve prediction accuracy on a student dataset with simulated domain shifts. The dataset contains demographic, academic, and macroeconomic features for university students, with the target outcome indicating whether a student graduated, dropped out, or is still enrolled. We partition the data into distinct domains by academic program to emulate distributional differences. An Artificial Neural Network (ANN) model is first trained on a source domain and then fine-tuned on a target domain with a subset of layer weights frozen. We evaluate model performance using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R^2), comparing the proposed transfer learning approach against a baseline without transfer. The results show that transfer learning significantly improves prediction accuracy: RMSE and MAE are reduced while R^2 increases on the target domain, indicating better generalization. The findings demonstrate that an ANN-based transfer learning method can effectively mitigate domain shift in student performance prediction. This study presents the benefits of transfer learning in an educational context by using attribute-based domain separation, offering a practical approach for academic institutions to predict student outcomes across different programs or semesters.
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The fields of augmented engineering are confronted with formidable obstacles because of the absence of chances for self-paced learning, the wide coverage of undergraduate curricula, uneven academic content standards, and shortages in teacher knowledge. This study suggests a thorough strategy to overcome these drawbacks. In order to enhance current course offerings through bridge or add-on courses, we want to integrate the NPTEL Swayam and NIELIT platforms as additional resources of Self-Paced Learning. This plan will improve students’ knowledge acquisition, give them various learning options, and promote a continuous learning culture reinforced by certification processes. The project intends to solve issues with skill development, student engagement, and standardized academic material by incorporating various online platforms as supplemental or add-on courses which are used for Curriculum Enhancement. To test the efficacy of this strategy, a pilot deployment encompassing course selection, curriculum integration, and student enrollment was carried out. Positive student results in terms of knowledge acquisition and skill enhancement are indicated by preliminary studies. Nonetheless, issues with workload management and technical difficulties were noted.
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Enlightenment on Chinese Evaluation Standard of Educational Game Software from TEEM's
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The author takes the evaluation standard of educational game software（EGS） made by TEEM as an example and analyzes its dimensionality, content and effectiveness. Then the author stresses the enlightenment on the evaluation standard of Chinese EGS about evaluation organization, evaluation scientificity and evaluation efficacy. The aim of this paper is to offer guidance to those enterprises producing EGS, schools and families purchasing EGS, teachers and students using EGS and promote Chinese evaluation standard to match international standard.
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Enterprise Architecture Design at PT Perkebunan Kaltim Utama Using TOGAF ADM
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Information systems and information technology have become indispensable in modern business operations, serving as critical tools for enhancing efficiency, streamlining processes, and supporting strategic decision-making. To ensure these technologies effectively meet organizational needs, enterprise architecture design plays a key role in aligning business goals with IT systems. This alignment not only improves operational efficiency but also lays the foundation for long-term organizational success. This study employs The Open Group Architecture Framework (TOGAF), focusing on its Architecture Development Method (ADM), to design an enterprise architecture tailored for PT Perkebunan Kaltim Utama. TOGAF ADM offers a structured, iterative approach to architecture development, encompassing phases from the initial planning stage to the final design and implementation analysis. Each phase is designed to integrate business processes with IT systems, enabling a cohesive and adaptive framework. PT Perkebunan Kaltim Utama, a company specializing in palm oil mill maintenance, faces significant operational challenges due to its reliance on manual processes and lack of integration. These inefficiencies hinder productivity and affect the company’s ability to meet strategic goals. This research systematically identifies the functional and technological requirements for PT Perkebunan Kaltim Utama’s business activities, laying the groundwork for an integrated solution. The proposed architecture design addresses these inefficiencies by providing a comprehensive blueprint for implementing a unified system. This system will not only enhance PT Perkebunan Kaltim Utama’s operational performance but also support its strategic objectives, enabling the company to remain competitive and responsive to industry demands. By integrating TOGAF ADM into its processes, PT Perkebunan Kaltim Utama can ensure a more effective alignment of business and IT, paving the way for sustainable growth and improved decision-making capabilities.
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Sungai Kunjang is one of the primary land transportation facilities in Samarinda City, East Kalimantan, located on Untung Suropati Street in the Karang Asam Ulu subdistrict. Officially inaugurated on June 24, 1989, by Mayor Waris Husain, it serves multiple transportation modes, including public passenger vehicles (PPV), pioneer services, and intercity routes. Although the station currently provides essential information services—such as departure schedules, route options, fare details, and a basic complaint system—these services are not yet supported by a structured Information Technology (IT) and Information System (IS) framework. The lack of integration hampers service efficiency and the optimization of business processes. This research aims to design an Enterprise Architecture (EA) for the station by applying The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM). The proposed design focuses on aligning business objectives with IT/IS strategies to improve the delivery of transport information and complaint management services. The resulting blueprint is expected to serve as a strategic reference for developing an integrated information system that enhances decision-making, streamlines operations, and improves service quality at Sungai Kunjang Station. By using selected phases of the TOGAF ADM, the study provides a practical foundation for digital transformation within public transport infrastructure in the region.
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Enterprise Architecture: A Tool for IS Strategy Formulation
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Nowadays, many organizations are concerned with how successfully they can formulate an information system (IS) strategy. Have them adopted enterprise architecture, they are concerned of how to activate the business and IS decisions. From a functional perspective, enterprise architecture demonstrates how all information technology elements in an organization, systems, processes and people work together as a whole. Hence, enterprise architecture is an approach of aligning the business area of an organization with the IT area. It has become widely recognized that an enterprise architecture plays a key role in influencing the IS strategy formulation. Strategy formulation in enterprises is a continuous process and it is considered an implicit process that is influenced by a set of factors in which enterprise architecture is a major one. In this paper, we discuss that role that an enterprise architecture plays in influencing the IS strategy formulation.
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Essential and New Maintenance KPIs Explained
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Maintenance in any manufacturing organization is critical, given its significant role in ensuring business continuity. Maintenance plays a crucial role and has a significant impact on the results of industrial companies. Therefore, it is essential to manage maintenance, observe, understand, and improve actions by adopting well-chosen performance indicators according to the company's needs. These indicators are known as Maintenance KPIs or Key Performance Indicators, which allow for gathering knowledge and exploring the best means to achieve the organization's goals. Maintenance KPIs are critical to keeping track of the function, monitoring performance, and ensuring fulfillment of business expectations. In addition, KPIs drive reliability growth while guiding decisions to improve maintenance efficiency and performance. A helpful maintenance KPIs help to identify the problems causing the maintenance effect and help to select the right strategy to support or correct the actions that produced the results. They also allow to identify the causes of equipment failures (measure the influence of life cycle factors), direct what maintenance does with its time and resources (measure the efficiency and effectiveness of the maintenance group) and identify if maintenance removes failure causes ( measure the improved reliability and operational risk reduction results of maintenance effort) and help drive the business benefits provided by maintenance (measure the contribution to the business value of maintenance). Essential maintenance KPIs are the most commonly used for maintenance management and are adopted by most industries; among these primary KPIs which are essential for maintenance management, we cite Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), and Overall Equipment (OEE). Nevertheless, it is crucial to continuously redefine and update KPIs to ensure they are appropriate for the organization's current environment, significantly when the constant market or research methodologies change. Hence, researchers and the industry propose several other maintenance KPIs outside the essential ones used in the industry according to the needs and within the performance improvement framework. These proposed KPIs aim to compensate for the lack of maintenance data, the absence of decision support, and the problems related to specific equipment, also in the context of improving the management strategy, the application of predictive maintenance, and the quality control of a maintenance process or the monitoring of systems reviews. Unfortunately, these indicators are not sufficiently known and are, therefore, not used by the industry. However, we believe that some of them should gain maturity and reach the status of widely used traditional indicators, such as the KPI of obsolescence management in maintenance operations and schedule compliance KPIs that aim to link maintenance planning with production. In addition, although not all proposed KPIs in the literature are generalizable, it has been identified that they can sometimes be specific to problematic situations, equipment categories, and even sectors of industry activity. Therefore, this work aims to inventory the most widely used maintenance KPIs and some of the KPIs proposed by researchers and the industry. In addition, we study the trends and challenges of selecting these KPIs and for what purposes they are used to help their understanding and usability. Indeed, Maintenance managers need to select relevant KPIs aligned with the maintenance strategy and the company objectives.
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Evacuation Simulation of Counter-current Behavior Using Agent Model for Pedestrian Dynamics
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It is an important issue that all occupants should be able to evacuate to safety from sports stadium under emergency. The behavior of occupants is quite complex, and individual behavior will greatly affect the result of the evacuation. Such as the phenomenon of counter-current behavior, sub-group behavior, following behavior and so on. In this paper, we mainly study the counter-current behavior, and a system simulation model is presented, which based on the agent technology. And in order to study the effect on evacuation, two cases have been studied to analyze the impact of evacuation. Simulation processes and results display that the counter-current behavior will be harmful for the movements of people. Simulation processes and results display that the model is very close to real movements of people.
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This paper is dedicated to a comprehensive analysis of hybrid energy options, with a specific focus on exploring their economic and environmental advantages within the context of an ice cream factory located in Fukuoka, Japan. The study takes a holistic approach, delving into various facets such as power generation, energy expenses, and related factors to uncover the potential benefits associated with specific configurations of hybrid energy solutions. The analysis presented in this study serves as a valuable tool for assessing the impact of different power generation technologies and energy management strategies. It sheds light on how these choices can influence not only the factory's operational costs but also its environmental footprint. By quantifying these effects, the study provides critical insights that can guide decision-makers toward more sustainable and economically sound energy solutions. As a forward-looking application approach, this research envisions the utilization of a PV-wind-diesel-grid-electrolyzer power system. This hybrid configuration serves as a versatile platform for conducting simulation studies, allowing for the exploration of a wide spectrum of potentially viable solutions. The insights derived from these simulations not only facilitate informed decision-making but also pave the way for anticipating and strategically planning future energy implementations. In essence, this study represents a proactive and data-driven approach to energy optimization, offering the ice cream factory in Fukuoka a roadmap to harnessing the benefits of hybrid energy systems, ultimately contributing to both economic efficiency and environmental sustainability. So, at a cost of energy (COE) of 18.313¥ per kWh, this arrangement stands out as an economically advantageous and environmentally friendly solution for the electrification of the ice cream factory.
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Key Process Areas (KPAs) for Software Engineering Institute (SEI) Maturity Level 4 can be described in terms of Quantitative Process Management (QPM) which is the metric to control the quantitative performance of a software project. On the other hand, Software Quality Management (SQM) monitors and controls the quality of the project. The survey conducted in this paper covers around 20 software houses of Pakistan. The study revealed that there is weakness in both KPAs, SQM and QPM. Each KPA defines a set of rules that are necessary to be followed to meet the standard but many organizations fail to follow these rules defined in every KPA. If specified and appropriate measures are taken, the software industry will lift it up to the higher CMMI Level.
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With the development of technology the use of Computer Aided Diagnosis has become a key for breast cancer diagnosis. It is important to increase the accuracy and effective of such systems. The concept of data mining can be applied on the data gathered through such systems for prediction and prevention of breast cancer. In this research, we have conducted the comparison between seven classification algorithms with the help of WEKA (The Waikato Environment for Knowledge Analysis) tool on the 569 instances (10 nucleus attributes) of data with two classes Malignant(M) and Benign (B) of breast cancer aspirate cells. Furthermore the influence of each attribute on prediction was evaluated. The accuracy of these algorithms was above 91% with the highest value of 94.02% for random forest and the predictive power of conclave points was highest whereas lowest was of Fractal Dimension.
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Evaluation of Machine Learning Techniques for Email Spam Classification
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Electronic mail (Email) is one of the official and very common way of exchanging data and information over digital and electronic devices. Millions of users worldwide use email to exchange data and information between email servers. On the other hand, unwanted emails or spam became phenomenon challenging major companies and organizations due to the volume of spam which is increasing dramatically every year. Spam is annoying and may contain harmful contents. In addition, spam consume computers, servers, and network resources, causes harmful bottleneck, effect on computing memory and speed of digital devices. Moreover, the time consumed by the users to remove unwanted emails is huge. There are many methods developed to filter spam like keyword matching blacklist/whitelist and header information processing. Though, classical methods like blocking the source to prevent the spam are not effective. This study demonstrates and reviews the performance evaluation of the most popular and effective machine learning techniques and algorithms such as Support Vector Machine, ANN, J48, and Naïve Bayes for email spam classification and filtering. In con conclusion, support vector machine performs better than any individual algorithm in term of accuracy. This research contributes on the for the development of methods and techniques for better detection and prevention of spam.
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Evaluation of Voice & Ear Biometrics Authentication System
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The core aim of biometrics authentication methods and technologies is to solve issues and concerns existing in traditional authentication methods like passwords, PIN numbers or identity cards. The Most important concern for business sectors and organizations is to authenticate individuals who interact with them and their services. By considering more than one biometric technology, the authentication process is expected to be more reliable and secure. The Ear and Voice Multimodal Biometrics System is evaluated to compare its performance with ear and voice unimodal systems; the multimodal system takes advantages of the permanence characteristic of ear biometric and voice biometric which is highly acceptable by users. According to the experiment, the ear and voice multimodal system provides better performance than the ear or voice unimodal system. In addition to that, the multimodal system makes a right balance between false rejection and false acceptance rates. This evaluation is intended to contribute to multimodal biometrics research by using behavioral biometric (voice print) and physiological biometric (ear-print) and makes advantage of using both of them in one system.
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