A Comprehensive Review of Cyber Hygiene Practices in the Workplace for Enhanced Digital Security

In today's digital age, cybercrime is increasing at an alarming rate, and it has become more critical than ever for organizations to prioritize adopting best practices in cyber hygiene to safeguard their personnel and resources from cyberattacks. As personal hygiene keeps one clean and healthy, cyber hygiene combines behaviors to enhance data privacy. This paper aims to explore the common cyber-attacks currently faced by organizations and how the different practices associated with good cyber hygiene can be used to mitigate those attacks. This paper also emphasizes the need for organizations to adopt good cyber hygiene techniques and, therefore, provides the top 10 effective cyber hygiene measures for organizations seeking to enhance their cybersecurity posture. To better evaluate the cyber hygiene techniques, a systematic literature approach was used, assessing the different models of cyber hygiene, thus distinguishing between good and bad cyber hygiene techniques and what are the cyber-attacks associated with bad cyber hygiene that can eventually affect any organization. Based on the case study and surveys done by the researchers, it has been deduced that good cyber hygiene techniques bring positive behavior among employees, thus contributing to a more secure organization. More importantly, it is the responsibility of both the organization and the employees to practice good cyber hygiene techniques. Suppose organizations fail to enforce good cyber hygiene techniques, such as a lack of security awareness programs. In that case, employees may have the misconception that it is not their responsibility to contribute to their security and that of the organization, which consequently opens doors to various cyber-attacks. There have not been many research papers on cyber hygiene, particularly when it comes to its application in the workplace, which is a fundamental aspect of our everyday life. This paper focuses on the cyber hygiene techniques that any small to larger organization should consider. It also highlights the existing challenges associated with the implementation of good cyber hygiene techniques and offers potential solutions to address them

Academic Performance Prediction Using Supervised Learning Algorithms in University Admission

Each educational institution has designed an academic system with the aim of providing as perfect learning as possible to students. The quality of good students is influenced by various factors, one of which is the available academic system. Previous research has shown that the quality of a student, which can be called academic achievement, can be determined through historical data on the student admission process. This research aims to process one of the admission processes previously implemented in Indonesian state universities using the National Selection for State University Entrance (SNMPTN) data, combined with Cumulative Achievement Index (GPA) data, so that it can be processed using a machine learning model. The algorithm used to create the model is a Supervised Learning Classification algorithm, which includes a Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). The research was carried out in three schemes based on the percentages of training data and test data. The results obtained show that DT produces the highest accuracy and precision values, with an accuracy value of 0.79 and a precision value of 0.56, respectively. The XGB produces the highest recall and f1-score values, with a recall value of 0.35 and an f1-score value of 0.36. The model with the highest f1-score can be selected as the best model, namely, the model with the XGB algorithm on a 70%-30% train-test data scheme. The resulting model achieved a success rate of 77%

Overview of Software Re-Engineering Concepts, Models and Approaches

 Legacy systems face issues such as integrating new technology, fulfilling new requirements in the ever-changing environment, and meeting new user expectations. Due to the old complex system structure and technology, modification is hardly applied. Therefore, re-engineering is needed to change the system to meet new requirements and adapt to new technology. Software re-engineering generally refers to creating a new system from the existing one. Software re-engineering is divided into three (3) main phases: reverse engineering alteration and forward engineering. Reverse engineering examines, analyzes, and understands the legacy system in deriving the abstract representation of a legacy system; then, through necessary alterations such as restructuring, recording, and a series of forward engineering processes, a new system is built. This paper introduces the concepts of software re-engineering, including the challenges, benefits, and motivation for re-engineering. In addition, beginning with the traditional model of software re-engineering, this paper provides an overview of other models that provide different processes of software re-engineering. Each model has its unique set of processes for performing software re-engineering. Furthermore, re-engineering approaches show various ways of performing software re-engineering. Software re-engineering is a complex process that requires knowledge, tools, and techniques from different areas such as software design, programming, testing, et cetera. Therefore, monitoring the re-engineering process to meet the expectations is necessary

Social Platforms in the Deepfake Age: Navigating Media Trust through Media Literacy

The issues with social media landscape are proliferation of disinformation, misinformation, and misinformation. The widespread of deepfakes makes is harder to distinguish between authentic content and fabricated content. The mediating effect of media literacy on news credibility has been understudied in previous research; the objective of the study is to investigate how much media literacy, news skepticism and fear of missing out (FOMO) influencing users' trust in the news disseminated on social media platforms. To achieve this, a survey was conducted to assess trust in and skepticism towards social media news, FOMO levels, and media literacy associated with deepfake news content. Educational efforts and media literacy initiatives are crucial in fostering informed and discerning news consumption. Furthermore, news organizations continue to prioritize transparency and accuracy to maintain credibility on social media since the news is easily accessible in the era of an information overload. The limitation of the study was the lack of assessment on evaluating effectiveness of media literacy in combating fabricated news content on social media. It is suggested to broaden scope by studying additional factors to combat fake news such as journalistic standards, fact-checking and verification are important to build reader’s trust. Future studies should also measure the effectiveness of media literacy initiatives ensure they really make a difference. The generalizability of future study can be strengthened with the inclusion of diverse age groups especially vulnerable populations

Toponym Extraction and Disambiguation from Text: A Survey

Toponym is an essential element of geospatial information. Traditionally, toponyms are collected in a gazetteer through field surveys that require significant resources, including labor, time, and money. Nowadays, we can utilize social media and online news portals to collect event locations or toponyms from the text. This article presents a survey of studies that focus on the extraction and disambiguation of toponyms from textual documents. While toponym extraction aims to identify toponyms from the text, toponym disambiguation determines their specific locations on the earth. The survey covered articles published between January 2015 and April 2023, presented in English, and gathered from five major journal databases. The survey was conducted by adopting the Kitchenham guidelines, consisting of an initial article search, article selection, and annotation process to facilitate the reporting phase. We employed Mendeley as a reference management tool and NVivo to categorize certain parts of the articles that are the focal points of interest in this survey. The primary focus of the survey was on the methods or approaches performed in the research articles to extract and disambiguate toponyms. Additionally, we also discuss some general challenges in toponym research, different applications for toponym extraction and disambiguation, data sources, and the use of languages other than English in the studies. The survey confirms that each approach has its limitations. Extracting and disambiguating toponyms from text is complex and challenging, especially for low-resource languages. We also suggest some research directions related to toponym extraction and disambiguation that could enrich the gazetteer

Challenges of Agile Software Development in the Banking Sector: A Systematic Literature Review

The banking industry is expected to thrive, generate profits, and contribute to national development and societal welfare. However, this sector is susceptible to volatility caused by global and domestic economic fluctuations. This research aims to identify and address challenges related explicitly to implementing agile methodologies within the banking sector. The study utilized a Systematic Literature Review (SLR) approach based on the guidelines provided by Kitchenham. A substantial number of academic journals (1,933) were analyzed during this review. Among the vast pool of literature, 28 relevant studies were extracted. These studies were chosen because they provided insights into the challenges of implementing agile practices in the banking domain. The analysis and categorization of these studies were structured according to the Project Management Body of Knowledge (PMBOK) 6th edition framework. This framework was employed to organize and understand the identified challenges systematically. The study's primary finding is that the most prevalent challenge encountered in the context of agile development within the banking sector is "Project Resource Management." In essence, effectively managing and allocating resources is a significant hurdle banks face when adopting agile methodologies. The challenges related to resource management are not confined to a single aspect. Instead, they encompass various dimensions, including human resources, technological resources, and organizational factors. This suggests that challenges in agile banking are multifaceted, involving issues related to people, technology, and the structure and processes within banking organizations

Two-Way Thesis Supervisor Recommendation System Using MapReduce K-Skyband View Queries

Timely graduation is an important indicator of the quality of higher education. Yet, many students struggle to complete their studies on time due to challenges in finding relevant research topics and suitable supervisors. This study developed a two-way supervisor recommendation system that considers the preferences and expertise of both students and supervisors. The main contribution of this research is the comparison of Block Nested Loop (BNL) k-skyband and MapReduce k-skyband algorithms. The recommendation model developed in this study uses course syllabi to obtain research topics and academic grades to determine students' interests in research topics. A total of 239 research topics were obtained from 37 courses. Optimal recommendations were achieved with a k value of 16. Implementing the MapReduce algorithm in this recommendation model demonstrated a computation speed 8 times faster than the BNL k-skyband approach, making it effective in handling large datasets. The proposed recommendation system received positive feedback from students, with scores of 3.5 for relevance, 3.7 for topic diversity, 3.4 for serendipity, and 3.5 for novelty. These findings suggest that the proposed recommendation system can support students in their research endeavors and improve the overall supervision process in academic settings, with potential for widespread implementation across various study programs. Thus, contributing to the overall improvement of higher education quality

A Comprehensive Analysis of Break Bulk Port Efficiency Using an Analytic Network Process Model

Ports significantly contribute to economic development, especially in developing nations. Although containerization prevails in cargo transportation, break bulk freight remains vital for items that cannot be accommodated in ordinary containers. This paper examines the successful techniques of breaking bulk terminals, concentrating on Vietnam, where these ports are essential to commerce. Employing qualitative and exploratory methodologies within the Entrepreneurial Action Theory (EAT) framework, data from 16 port executives at Vietnam's principal break bulk ports were examined to identify critical success factors, internal competencies, and external influences impacting port performance. The Analytic Network Process (ANP) ranks success factors, emphasizing cargo throughput and ship call stability as paramount, succeeding by profitability, operational efficiency, and customer happiness. External influences, such as the escalation of vessel sizes, port competition, and changes in international trade, profoundly influence port operations, necessitating adaptability and infrastructure investment. Government influence, developments in port technology, labor shortages, and cost changes are significant factors, whereas leadership vision and green initiatives, despite their lower ranking, possess long-term strategic importance. Moreover, internal success variables, including port infrastructure, labor proficiency, and technological integration, are crucial for sustaining efficiency. This study presents a conceptual framework for decision-makers in resource allocation and strategic planning by merging ANP findings with EAT. The findings enhance maritime research by providing insights for optimizing break-bulk operations and guaranteeing resilience in the face of growing industry challenges

Optimising iCadet Assignment through User Profiling

Industry Cadetship programme is a programme that assigns penultimate year students to companies matching their profiles, bridging academic learning and industry skills.  Manual data analysis for assignments is time-intensive, prompting this study’s objectives: (i) propose an algorithm to optimize student-company assignment by using the student and company profiles, (ii) propose a method for the assignment of lecturers to company, and (iii) use similarity measure techniques to recommend companies with similar characteristics. Data was collected from a university's student, company, and lecturer datasets. To assign students to companies, the Haversine, OpenStreetMap, and NetworkX were used to calculate the shortest geographical distance between the students and the companies; evaluated based on mean, variance, standard deviation, and utilization rate. For the lecturer assignment, cosine similarity was applied to measure the similarity between domain descriptions and company or lecturer information after performing Voyage AI embeddings. Lecturers are assigned to companies based on the highest domain similarity scores. The performance was evaluated using accuracy, precision, recall, and F1- score.  Findings showed embedding techniques significantly enhanced the matching process, with accuracy improved from 0.464 to 0.6071, precision increased from 0.417 to 0.5058, recall saw an equal rise from 0.464 to 0.6071, and the F1-score advanced from 0.417 to 0.5264. Longer descriptive inputs further improved performance, with accuracy rising from 0.6154 to 0.7692, precision from 0.5744 to 0.7751, recall remaining steady at 0.7692, and F1-score increasing from 0.5807 to 0.7484. This work can be extended to explore job portal dataset by aligning profiles with geography and specialization

Analyzing Perceptions of Maternal and Pediatric Care in Jakarta: An Integrated VADER and GloVe Analysis of Google Reviews in Mother and Child Hospitals

In the rapidly digitizing landscape of healthcare feedback, online reviews have become a vital source of patient-reported experiences. This study leverages sentiment analysis to decode the narrative content of Google reviews for Mother and Child Hospitals in Jakarta. Utilizing the VADER sentiment analysis tool and GloVe for keyword extraction, the research aimed to correlate qualitative sentiment with quantitative star ratings. This study meticulously processed and analyzed a selection of Google reviews using VADER for sentiment scoring and GloVe for refining the focus on relevant healthcare discussions. This methodological approach allowed for a comprehensive sentiment assessment of the reviews. The analysis revealed a prevalent positive sentiment in higher-rated reviews and negative sentiment in lower-rated reviews, with notable anomalies that underscore the complexity of patient experiences and perceptions. Specific aspects of care, including staff behavior, facility quality, and treatment efficacy, were recurrent themes in the feedback. These findings highlight the potential of patient-reported experiences in shaping healthcare practices and policy. The study emphasizes the importance of healthcare providers understanding and responding to patient feedback to improve care quality. Limitations such as the representativeness of online reviews and the challenges of sentiment analysis in capturing nuanced emotions are discussed. This study offers valuable insights into patient perceptions of maternal and pediatric care in Jakarta, affirming the significance of leveraging online reviews for healthcare quality monitoring and improvemen