Journal of Information Systems and Informatics (Journal-ISI)
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Assessing Employee Performance in the Information Technology Department Using K-Means Clustering: A Case Study Approach
Get PDFThis research explores the utilization of the K-Means method for evaluating the performance of employees within the Information Technology Department at PT Nusa Halmahera Minerals. The research leverages data from Manage Engine to analyze diverse parameters of employee performance. By employing the K-Means method, employees are categorized based on specific characteristics, facilitating a deeper understanding of each individual's contribution and potential in attaining company objectives. The implementation of the K-Means method aims to offer a more objective perspective on employee performance, empowering companies to make informed decisions in the enhancement and development of their human resources
Enhancing Website Management Through Expertise and Rapid Application Development Frameworks
Get PDFEffective website management is crucial for organizations seeking to engage users and communicate effectively with stakeholders. This research explores the role of specialized expertise in typography, audio and visual design, copywriting, and the implementation of Rapid Application Development (RAD) frameworks in optimizing website management practices. By leveraging the skills of typography, design, and copywriting specialists, organizations create visually appealing and engaging online experiences that effectively convey messages and drive user interaction. Additionally, adopting RAD methodologies enables agile and iterative website development processes, allowing for quick prototyping, feedback integration, and rapid deployment of updates. Through synthesizing expert knowledge and RAD principles, organizations enhance their online presence, meet the evolving needs of users and stakeholders, and achieve their strategic objectives in today's dynamic digital landscape
Evaluation of the Open ERP Implementation for MSME (Case Study: Palembang City)
Get PDFThe emergence of Enterprise Resource Planning (ERP) systems has transformed the way businesses operate, particularly for Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. Serves as a relevant area of study to understand the impact, challenges, and benefits of using Open ERP. ERP is a method used by industries to enhance business process efficiency. This is done by sharing information both within and between business processes. This study aims to evaluate the implementation of Open ERP in the MSME ecosystem using the HOT-Fit method, which measures the fit between Human, Organization, and Technology components in the use of the Odoo application for MSMEs. Odoo is a open-source ERP software that is suitable for small and medium businesses. Based on the analysis results, it can be concluded that the implementation of OpenERP using the Odoo application only affects the human component. Simultaneous testing indicates that these three variables collectively influence net benefits
Sentiment Analysis of Skincare Products Using the Naive Bayes Method
Get PDFThe number of reviews about skincare products can be used as an evaluation of product quality and satisfaction from consumers who have used it as well as considerations for other consumers to try the product. With the number of reviews, it is important to classify reviews into positive, negative, and neutral classes so that the level of product quality from each classification class can be known. The number of reviews causes the review classification process to be unable to be carried out automatically, so sentiment analysis is carried out. To determine the classification of positive sentiment, negative sentiment, and neutral sentiment on the skincare product, the Naive Bayes algorithm method is used. Naive Bayes was chosen because it is easy to implement and has a probability value to classify data. To determine the percentage of results from the specified classification, the Confusion Matrix will be used. The results of the classification process using the Naive Bayes method produce data into 3 types, namely 65 positive classes, 87 neutral classes, and 24 negative classes with an accuracy value of 73%, precision 77%, recall 61%, and f1-score 63%
Evaluating Digital Narratives in Heritage Tourism and Museum: Content Analysis, Toxicity Score, and Sentiment Classification Trough SVM and SMOTE
Get PDFThis research uses the Digital Content Reviews and Analysis Framework to explore the dynamic interplay between digital content, sentiment, and toxicity within the context of heritage tourism at the Sangiran site. The study is driven by the urgency to understand how digital narratives impact public engagement and perception, particularly for heritage sites of global significance. Through a comprehensive analysis, the research evaluates toxicity scores, sentiment classifications, and thematic content across multiple videos related to Sangiran. The toxicity analysis reveals generally low levels of harmful content, with an average score of 0.04717, but identifies occasional peaks, highlighting the potential for negative discourse. Sentiment analysis, conducted using the SVM model enhanced by SMOTE, achieves an accuracy rate of 94.59%, with precision and recall rates of 92.07% and 97.79%, respectively, demonstrating the model's robustness in capturing audience sentiment. Content analysis identifies critical themes, such as human evolution and fossil discoveries, emphasizing the educational value of digital content. The research underscores the importance of curating digital narratives that engage, educate, and foster a positive and respectful discourse. The findings suggest that while digital content successfully educates the audience, managing contentious topics is crucial to maintaining constructive engagement. This study contributes to developing more effective digital strategies for heritage tourism, ensuring the sustainable promotion and preservation of sites like Sangiran while addressing the challenges of online discourse. The research highlights the need for continued exploration of digital content's role in shaping public perceptions of cultural heritage
Exploring the Digital Narratives in Tourism and Culture through The Case of Rambu Solo: Sentiment, Toxicity, and Content Analysis
Get PDFThis research urgently addresses the need to understand and manage viewer interactions with culturally significant video content, particularly the Rambu Solo ritual. By integrating the Digital Content Reviews and Analysis Framework with sentiment classification performance, toxicity score evaluation, and content analysis, the study systematically analyzed 21,562 posts across four videos, revealing critical themes related to cultural preservation and tourism impact that shaped viewer perceptions. Sentiment and toxicity evaluations of 15,762 posts showed an average toxicity score of 0.068, with a peak of 0.85174. Sentiment classification, using algorithms like SVM, k-NN, NBC, and DT, highlighted the superior performance of SVM enhanced by SMOTE, with an accuracy of 81.97%. However, the study identified limitations in automated sentiment analysis tools, noting that they may not fully capture the complexities of human expression. This research recommends incorporating advanced natural language processing techniques and multimodal analysis within the framework. This comprehensive methodology offers essential insights into the intersection of culture, tourism, and digital media, emphasizing the importance of creating and managing content that respects and promotes cultural heritage in the digital age. The findings are crucial for developing more effective strategies for digital content creation and community engagement, ensuring that cultural narratives are presented thoughtfully and respectfully to global audiences
Enhancing Network Security in Mobile Applications with Role-Based Access Control
Get PDFIn today's dynamic networking environment, securing access to resources has become increasingly challenging due to the growth and progress of connected devices. This study explores the integration of Role-Based Access Control (RBAC) and OAuth 2.0 protocols to enhance network access management and security enforcement in an Android mobile application. The study adopts a waterfall methodology to implement access control mechanisms that govern authentication and authorization. OAuth 2.0, a widely adopted open-standard authorization framework, was implemented to secure user authentication by allowing third-party access without exposing user credentials. Meanwhile, RBAC was leveraged to streamline access permissions based on predefined user roles, ensuring that access privileges are granted according to hierarchical role structures. The main outcomes of this study show significance towards the improvements in security enforcement and user access management. Specifically, the implementation of multi-factor authentication, session timeout mechanisms, and user role-based authorization ensured robust protection of sensitive data while maintaining system usability. RBAC proved effective in controlling access to various system resources, such as database operations which was presented in scenario of physical access to doors, while OAuth 2.0 provided a secure communication channel for authentication events. These protocols, working in tandem, addressed key issues like unauthorized access, data integrity, and scalability in network security policy enforcement. This research deduces that combining RBAC and OAuth 2.0 protocols in mobile applications enhances security posture, simplifies access management, and mitigates evolving threats
A Readiness Assessment Tool for Smart City Implementation in Small and Rural Municipalities
Get PDFSmall and rural municipalities are lagging in terms of implementing a smart city. These municipalities have limited resources to provide basic services to the citizens. There is a need for these municipalities to implement a smart city to manage resources effectively. However, an assessment tool to assess small and rural municipalities’ readiness for smart city implementation is lacking. This article offers such an assessment tool tailored specifically to assess small and rural municipalities’ readiness for smart city implementation. Design science research methodology (DSR) was used in a wider study to develop a related smart city readiness framework. In the preceding cycles of the DSR study, a literature review was used to provide relevant data for the construction of a conceptual framework, which was validated and improved using semi-structured interviews in a second and third cycle. The last cycle of the research developed and validated an assessment tool as an artefact that could be used to address critical issues, including limited resources and governance complexities that are unique to these municipalities. The findings showed that the proposed tool covered all the salient aspects, except for the aspect of smart buildings that are capable of collecting data without human intervention. This element was added to the final assessment tool. The tool can be used by personnel and consultants who are responsible for developing or implementing a smart city in small and rural municipalities. Furthermore, what makes this assessment tool unique is its alignment with the needs of small and rural municipalities. It was validated through participatory and expert reviews, providing a reliable instrument for policymakers and municipality managers in making an informed decision toward the readiness assessment of a smart city. A formula to calculate a municipality’s readiness level quantitively as a percentage, as well as a proposed evaluation heuristic, is also provided. The final, revised assessment tool prompts actionable insights informing the implementation of a smart city in small and rural municipalities
Usability Analysis of the IBOSS PTSP BP Batam User Interface Using Heuristic Evaluation
Get PDFThis research investigates user dissatisfaction with the IBOSS user interface, reported by 54% of users in an initial survey, and aims to improve the interface through heuristic evaluation and subsequent redesign using Gestalt principles. The usability of the IBOSS user interface was assessed using the heuristic evaluation method, focusing on 10 usability variables. Among the 10 heuristic variables, only the ‘Aesthetics and Minimalist Design’ variable was rated positively, with a score of 78% and was considered satisfactory with no need for improvement. Based on the evaluation results, the interface was redesigned using Gestalt principles to improve user experience. After the redesign, interviews with experts showed a significant improvement in the user interface. The experts gave positive feedback compared to their assessment before the redesign. The heuristic evaluation identified key areas for improvement in the IBOSS user interface, excluding aesthetic aspects. Applying Gestalt principles in the redesign process resulted in improved usability and received favourable feedback from experts, indicating the success of the redesign effort
Realtime-Based System for Facemask Detection Using PCA, with CNN and COCO Model
Get PDFThe instant spread of COVID-19 has underscored the need for effective measures such as wearing face masks to control transmission. As a response, facemask detection systems using advanced machine learning techniques have become essential for ensuring compliance and public safety. This research focused on developing a system for detecting facemask usage using a hybridized approach comprising of Convolutional Neural Networks (CNN), Principal Component Analysis (PCA), and the Common Objects in Context (COCO) model. A hybridized detection model is often explored to enhance the precision and efficiency of previous methods that leveraged traditional machine learning or deep learning for the same task. Hence, this system effectively identifies whether individuals are properly wearing masks, not wearing masks at all, or wearing masks improperly from images and real-time video streams using bounding boxes. The results demonstrate that the hybrid approach achieves high accuracy in detecting various facemask conditions across different scenarios. Evaluation metrics such as Average Precision (AP) and Average Recall (AR) indicate the model's robustness, with a reported AP value of 70% and an AR value of 81%, primarily evaluated on larger objects within images. Further evaluations involving different individuals and types of facemasks revealed variability in detection accuracy, highlighting the model's effectiveness and areas for improvement. Nevertheless, the development and deployment of facemask detection systems are crucial for managing public health and ensuring safety in the face of ongoing and future pandemics