Optimization of Backpropagation (BP) Weight Values Using Particle Swarm Optimization (PSO) to Predict KIP Scholarship Recipients

The Indonesia Smart Card (KIP) Lecture program aims to improve the quality of human resources by providing educational assistance to students from underprivileged families. However, the distribution of KIP Lecture in Palembang still faces problems, such as inaccurate targeting and lack of public understanding of this program. The selection process for scholarship recipients is not optimal, causing students who should be prioritized to be overlooked. In addition, decision-making takes a long time due to the many variables that must be considered and the lack of transparency in data processing. This research discusses the Backpropagation (BP) method for predicting KIP College scholarship recipients, which has previously been applied to the classification of educational aid recipients with high accuracies results. However, BP has disadvantages such as minimum local risk and long training time. To overcome this, the Particle Swarm Optimization (PSO) algorithm is used to optimize the weights of the BP artificial neural network. PSO is a simple but effective optimization algorithm to find optimal weights more quickly and accurately. The results of previous studies show that the combination of BP with PSO can improve prediction accuracy compared to using BP alone. Therefore, this research aims to develop a more efficient and targeted prediction model for KIP College scholarship recipients through BP optimization using PSO, so that the selection process can be carried out more quickly and accurately

Digital Mapping of Fermented Foods for the Advancement of Gastronomy Tourism in Indonesia

This research introduces a pioneering digital mapping framework for Indonesian fermented foods that integrates geospatial technologies with traditional gastronomic knowledge systems. Employing Rapid Application Development methodology on the Oracle APEX platform, the study establishes a comprehensive documentation infrastructure capturing the geographical distribution, production methodologies, and cultural significance of diverse fermentation practices across Indonesia's archipelagic landscape. The resulting prototype offers multifunctional capabilities through an intuitive interface design that serves preservation imperatives and tourism development objectives. Findings demonstrate that systematic digital documentation of fermented food traditions creates measurable economic opportunities through enhanced destination competitiveness, specialized culinary tourism routes, and improved market visibility for artisanal producers. The community-driven documentation protocols position local knowledge-holders as primary content contributors, while the system architecture establishes essential connections between geographical contexts and traditional fermentation techniques. This research addresses critical documentation gaps while establishing standardized protocols applicable beyond Indonesia to other regions with significant fermentation heritage. The digital mapping system ultimately functions as both a cultural preservation mechanism and a strategic asset for sustainable gastronomy tourism development, offering a replicable model for transforming endangered culinary knowledge into economically viable digital assets that benefit traditional food-producing communities

Exploring Internet Radio’s Impact on Dispersed Communities in Ghana

Due to the constant challenges that migrants face in their host country, which sometimes lead to alienation, they strive to find solace in internet radio connecting them to happenings in their home countries as well as the world. Satisfaction with information systems (IS) has long been a topic of research in the discipline. It has primarily been employed as a stand-in for IS success. Researchers have utilized DeLone and McLean’s 2003 extensive model of factors to evaluate IS performance and the interrelationships between the variables. According to their approach, the success of IS is largely dependent on user pleasure. They also suggested that the main antecedents of user happiness are system, information, and service success. Drawing clues from the major components of Delone and McClean’s IS success model, the article explores factors that influence the use of internet radio as an information system. The literature review reveals some attribute levels (accessibility, empathy, trustworthiness, etc.) that are confirmed and amended by the empirical study. The study uses a qualitative approach using interviews to collect data that examines the impact of internet radio on people living in dispersed communities. The results prompt key attributes of IS success, which are used to chart the impact of internet radio. The findings reveal that service success attributes (accessibility, empathy, etc.), data success (understandability and relevancy, etc.), and technology success attributes (availability, ease of use, etc.) impact the use of internet radio. Using the attributes identified in the literature review, as confirmed by the empirical study, as well as three additional constructs that emerged during the semi-structured interviews, a framework is developed to determine the impact of internet radio on dispersed communities. The research presents a novel comprehension of the impact of internet radio by applying and extending multi-attributes from the Delone and McClean IS success model

Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression

This research delves into the crucial area of hazard detection and risk severity assessment within the construction industry, using machine learning techniques. The dataset utilized is from the Chinese Construction Company (CCECC), Uyo, Nigeria. Comprising over 100,000 instances, it captures various hazard categories prevalent in construction sites, providing a comprehensive foundation for predictive analysis. In the first phase of the study, the system is designed to detect hazards present in construction sites. Leveraging these data, the machine learning models are trained to predict potential hazards based on the information provided. Through TF-IDF vectorization, a feature extraction technique, the textual data is transformed into numerical representations. Multinomial Naive Bayes is employed for hazard classification due to its efficacy in handling text data, and with it, an accuracy of 0.99 was obtained. Subsequently, the trained model was evaluated to assess its performance and the severity of identified hazards are evaluated. The system quantifies the potential risk posed by each hazard using the risk severity attribute. Using the Linear Regression algorithm, the model predicts the severity of risks based on textual descriptions of a hazard.  In practical application, the research stresses the significance of risk management strategies in the construction industry to mitigate potential harm to personnel and infrastructure. This research contributes to advancing safety protocols within the construction sector, advocating for a culture of vigilance and precaution to address risks effectively

Systematic Review of Augmented Reality Applications in Wayang Heritage Preservation

This study presents a systematic literature review on Augmented Reality (AR) in Wayang from 2020 to 2025. AR has become an innovative solution that combines education and entertainment to increase the engagement of the younger generation and expand access to traditional Wayang art. This study examines the trend of AR in Wayang, including design approaches and user interaction strategies, as well as the benefits and challenges of implementing this technology. It also identifies research gaps and future development directions. This review discusses explicitly the application of AR to various forms of Wayang, including Wayang Kulit, Wayang Golek, and other traditional variants, while excluding Virtual Reality (VR) and other digital art forms. The results indicate that AR applications based on mobile platforms with gesture interaction and gamification effectively enrich the user experience in digital Wayang performances. However, significant challenges related to technological limitations, cultural sensitivity, and involvement of indigenous communities still need to be overcome. This study recommends a multidisciplinary and collaborative approach to developing AR Wayang, enabling authentic and sustainable cultural preservation. These findings are expected to serve as the basis for inclusive digital cultural innovation, which will have a positive impact on preserving Wayang's cultural heritage

Agile-Scrum Methodology for Hospital Information System Development

Hospitals face significant challenges in managing large and complex data, and Hospital Information Systems (SIRS) are essential for supporting hospital operations. However, many SIRS projects experience delays and failures due to rigid development approaches. Agile-Scrum is proposed as a more flexible and adaptive solution, emphasizing collaboration and iterative processes to enhance the quality of healthcare services. This qualitative case study, conducted in a hospital with an internal development team, used observations, document analysis, and semi-structured interviews with 10 participants, including developers, a Scrum Master, and key hospital stakeholders. The findings indicate that implementing Agile-Scrum led to a 35% increase in team collaboration, a 40% improvement in responsiveness to changing requirements, and a 30% boost in overall project efficiency. The study highlights the effectiveness of Agile-Scrum in managing the complexities of SIRS development, especially through backlog organization, sprint planning, and stakeholder feedback. The study suggests further research to assess the long-term impact of Agile-Scrum in other information system development contexts

Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction

Employee turnover refers to the replacement of employees within an organization, which can lead to losses such as recruitment costs and decreased productivity. Predicting turnover is crucial for companies to anticipate and take appropriate actions to retain potential employees. This study aims to optimize the employee turnover prediction model by integrating hash encoding techniques and machine learning. The dataset used in this study is an open-source dataset obtained from Kaggle dataset. It consists of 14,994 rows and 10 columns (features) representing employee-related information such as satisfaction level, evaluation score, number of projects, average monthly hours, and whether the employee left the company. Among these features, some are of object data type. Since machine learning algorithms generally cannot work directly with object-type features, the use of hash encoding is proposed. This technique converts object-type data into numerical data. It is part of the preprocessing stage, aiming to reduce memory usage, speed up data preprocessing, and improve model performance. After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. The evaluation is conducted using accuracy, recall, precision, and F1-score metrics, which yielded results of 0.988, 0.961, 0.988, and 0.974, respectively. These results indicate that the integration of hash encoding techniques and machine learning can produce a well-performing model for predicting employee turnover

Mitigating Cybersecurity Risks in E-Waste: A Study on Secure Disposal Practices in Tanzania’s Public Institutions

The growing volume of electronic waste (e-waste) in Tanzanian public institutions poses serious cybersecurity risks, as discarded devices often contain sensitive data vulnerable to unauthorized access. This study examines these risks across 11 public institutions, involving IT staff, e-waste handlers, policymakers, and environmental officers. It applies Routine Activity Theory, a framework that explains risks as arising when cybercriminals exploit unsecured e-waste due to weak regulations. Through interviews and focus group discussions, the research identifies key vulnerabilities: data leakage from improper sanitization, regulatory gaps, and risks from informal disposal methods like auctions. These findings highlight the need for stronger oversight to prevent data breaches. The study proposes a framework that categorizes devices by risk level and integrates secure sanitization protocols, such as data wiping or destruction. Policymakers and institutions must urgently adopt these protocols to protect sensitive data and promote sustainable e-waste management in Tanzania’s public sector

A Qualitative Study of Researchers Perspective on the Use and Risks of Open Government Data

Open government data has the potential to improve transparency, accountability, public participation, business innovation, and research quality. However, this openness also poses various opportunities for losses or even risks, especially related to low data quality, personal data security issues, data translation errors, and misuse of information. This study aims to review the potential risks of data openness on government data portals from the perspective of researchers as one of the important actors who use data. Using qualitative method with structured interviews, this study involved five potential researchers who actively used open data between May and August 2023. The results of the interviews showed that high data quality, such as accuracy, completeness, and currency, can increase researchers' trust in the data. At the same time, obstacles in accessibility and bureaucracy or data administration requirements can slow down the research process or stages. Security and privacy issues are also important parameters, with strict security policies and good audit processes can reduce the risk of data misuse. Data openness and transparency play a major role in increasing the use of data for public policy and evidence-based research. In addition, data standardization is essential to ensure the efficiency of data use by researchers. This study concludes that to optimize the benefits of data openness, there needs to be proper and measurable management in order to consider data quality, accessibility, security, and standardization

Strategic Framework for Cybersecurity Policy Compliance in Namibian Organizations

The Internet and its transformative technologies have become essential to both emerging and established businesses. While organisations benefit from connectivity, they are also increasingly vulnerable to cyber-attacks, underscoring the need for robust monitoring systems and comprehensive cybersecurity policies. In Namibia, many organisations have cybersecurity policies, yet employees are often unaware of existence of such policies. This study aimed to examine the complexities of cybersecurity policies within Namibian organisations and provide a tailored roadmap for developing, implementing, and ensuring compliance with these policies to suit the unique landscape of Namibian businesses. Using a qualitative approach guided by design science research, data was collected from 21 participants, including Information Technology (IT) and security managers as well as employees from five organisations across various sectors in the country.  The findings indicated that Namibian organisations are commitment to cybersecurity through comprehensive policies aligned with international standards. However, organisations face impediments that underscore the need for targeted strategies to overcome barriers to policy enforcement. From these finding a framework was designed with strategies and action plans and evaluated by industry experts.  The CSPIC framework was considered Good (rating 2) in most areas by the experts. Gaps in existing frameworks such as usability, adoptability, and budget prioritization were addressed by the proposed CSPIC framework. The Cybersecurity Policy Implementation and Compliance (CSPIC) framework's uniqueness lies in its local adaptability, actionable strategies, and emphasis on leadership and employee engagement