RAD-Based Public Opinion Monitoring Information System for BSN

The growing influence of online media in shaping public opinion has driven government institutions to modernize their monitoring and communication systems. This study aims to develop a web-based information system for monitoring public opinion, tailored to the needs of the National Standardization Agency of Indonesia (BSN). Using the Rapid Application Development (RAD) approach, the system was built through a phased prototyping and user involvement to ensure functional relevance. The final system enables sentiment classification of news articles, centralized data storage, trend visualization, and automated news clipping. Evaluation results indicate improvements in monitoring speed, accuracy, and usability compared to previous manual methods. This study confirms the effectiveness of RAD in building practical digital tools for public sector communication and reputation management

SHORT-TERM ELECTRICITY LOAD FORECASTING SEASONAL PATTERN USING TIME SERIES REGRESSION (TSR) MODEL IN PT.PLN (PERSERO) MEDAN CITY

Electricity is a crucial component of modern life, where daily consumption fluctuates significantly. Uncertain electricity demand can lead to imbalances between supply and consumption, potentially causing energy wastage or power outages. To address this issue, a forecasting method capable of accurately predicting electricity load is essential. The Time Series Regression (TSR) model is applied for short-term electricity load forecasting by considering daily and weekly seasonal patterns. The forecasting results indicate that Monday and Tuesday have the highest electricity load, while Sunday has the lowest. When the Kolmogorov-Smirnov test is used to analyse the model, the p-value is 0.9608, which shows that the residuals have a normal distribution. The model's accuracy is assessed with a Root Mean Square Error (RMSE) value of 378.0069 MW, which is relatively high for a small dataset. Given the considerable forecasting error, further improvements such as hybrid models are recommended to enhance accuracy. The implementation of these forecasting results can help optimize electricity management and improve power distribution efficiency

Interactive Multimedia Website For Promoting Mumbul Sangeh Park As A Tourist Destination

Mumbul Sangeh Park in Bali has experienced low visitor turnout, primarily due to inadequate promotional strategies. To address this issue, a study was conducted to develop a modern, interactive promotional website aimed at increasing public awareness of the park’s historical significance and unique attractions. The website development followed a structured Software Development Life Cycle (SDLC) approach, specifically utilizing the waterfall model. The system was implemented using the PHP programming language, supported by the Laravel framework and a MySQL database. Functional verification was performed using black box testing to ensure all system features operated as intended. The resulting website is fully functional, responsive, and delivers comprehensive information. It also includes an intuitive administrative panel that enables park administrators to easily manage content updates, including photo galleries, news, and visitor information. The system represents a strategic digital initiative to enhance the visibility and reputation of Mumbul Sangeh Park in the era of digital tourism

User Experience Analysis of Employee Attendance List on Talent Application with Heuristic Evaluation Method

The development of digital technology has influenced human resource management systems, particularly in the management of employee attendance records. One of the most widely used applications in Indonesia is Mekari Talenta, a cloud-based HRIS platform with features ranging from online attendance, leave, overtime, to payroll integration. Despite its high rating on the Google Play Store, there are still a number of complaints regarding user experience, such as confusing navigation, an unintuitive interface, and difficulty in accessing certain features. This study aims to analyze the user experience on the Talenta application using the Heuristic Evaluation method based on Nielsen's 10 principles. Data collection was conducted through questionnaires and processed using SPSS for validity, reliability, and descriptive percentage analysis. The results of the study show that most of the Heuristic Evaluation principles scored in the "Good" category, especially in terms of visibility of system status, consistency and standards, and aesthetic and minimalist design. However, there are still weaknesses in terms of help and documentation as well as error prevention that need improvement. These findings recommend that developers improve the interface display, clarify the help documentation, and optimize the error prevention feature so that the application can provide a more optimal user experience. Further research is recommended using other evaluation methods, such as the User Experience Questionnaire (UEQ) or in-depth interviews, to obtain a more comprehensive picture

Machine Learning for Securing API Gateways : a Systematic Literature Review

The rapid growth of mobile banking has improved access to financial services but also introduced heightened cybersecurity risks, particularly due to vulnerabilities in API Gateways and limited user awareness of cyber threats. This study conducts a Systematic Literature Review (SLR) to explore how machine learning (ML) can address both technical and human-centric security challenges in digital banking. By reviewing sixteen peer-reviewed studies published between 2019 and 2025, the study identifies key ML techniques such as anomaly detection, behavior-based models, and deep learning architectures that are effective in detecting and mitigating API-based attacks. In parallel, the review examines ML applications aimed at enhancing user cybersecurity awareness, including personalized alert systems, user segmentation, and adaptive education mechanisms. Thematic synthesis reveals several challenges, including data availability and privacy, the interpretability of complex models, and integration with existing banking infrastructures. However, the study also highlights significant opportunities, such as the use of federated learning to preserve privacy, explainable AI to improve trust, and dynamic alert systems to prevent user fatigue. Based on the synthesis, a conceptual architecture is proposed to integrate ML-driven API threat detection and user education within mobile banking platforms. The findings provide valuable insights for both academic research and practical implementation, contributing to the development of intelligent, user-aware cybersecurity frameworks in the financial sector.Keywords: API Gateway Security, Cybersecurity Awareness, Machine Learning, Mobile Banking, Systematic Literature Review

TOURIST VISIT PATTERN ANALYSIS AT HOTELS IN NORTH PENAJAM PASER REGENCY USING K-MEANS CLUSTERING

Penajam Paser Utara Regency, as a strategic area in East Kalimantan, has experienced significant development in the tourism sector in line with the plan to relocate the national capital (IKN). However, the utilization of tourist visitation data in hotels in this region is still not optimal. This study aims to analyze tourist visit patterns at Penajam Paser Utara Regency hotels using data mining techniques with the K-Means Clustering algorithm. The data used is secondary data obtained from the Penajam Paser Utara Regency Culture and Tourism Office, covering 34 hotels with variables including domestic and foreign visitors from 2019 to 2024. The clustering results show two main clusters: a high-visitation cluster comprising large hotels and a low-visitation cluster consisting of hotels with fewer visitors. The analysis reveals the dominance of domestic tourists, accounting for 99% of total visits, and the tourism sector's recovery pattern, reflecting a V-shaped recovery post-pandemic. This research contributes to hotel managers in designing market segment-based marketing strategies and local governments in designing data-driven tourism policies to enhance the sustainable competitiveness of destinations

Enhanced Plant Disease Detection Using Computer Vision YOLOv11: Pre-Trained Neural Network Model Application

This study investigates the application of YOLOv11, a cutting-edge deep learning model, to enhance the detection of plant diseases. Leveraging a comprehensive dataset of 737 images depicting tomato leaves affected by various diseases, YOLOv11 was trained and evaluated on key performance metrics such as precision, recall, and mAP. Experimental results the model was trained and evaluated on key metrics including accuracy (75.6%), precision (0.80), recall (0.77), and [email protected] (75.6%). Experimental through base architectural such as enhanced feature extraction with C2 modules, improved multi-scale detection using SPPF layers, and optimized non-maximum suppression techniques. These improvements enable the model to achieve stable precision and recall for each class, even in challenging scenarios with overlapping objects and diverse environmental conditions. By addressing practical usability challenges, this system offers a scalable, accessible, and impactful solution for precision agriculture, paving the way for sustainable with this pretrained model. This study underscores the potential of deep learning-based models, particularly YOLOv11, in transforming the way monitoring and disease management are approached, demonstrating its ability to stable accuracy and operational efficiency in real-world applications. Furthermore, the practical usability of the YOLOv11-based system addresses challenges in the domain of precision plant detection desease. By providing a scalable, accessible, and highly efficient solution, the model offering a significant advancement toward sustainable agricultural practices

Wireless Network Quality Analysis Using RMA and RSSI Methods at BPKAD Berau District

Wireless networks are now essential in supporting government operations, including at the BPKAD office in the Berau district. However, problems like unstable connections and slow speeds often arise as obstacles. This study aims to evaluate the quality of the wireless network in the BPKAD asset room of the Berau district by applying the Reliability, Maintainability, and Availability (RMA) and Received Signal Strength Indication (RSSI). Quantitative research method. The research population is all wireless access points (Wi-Fi) spread across the BPKAD office. The research sample is the asset field room. Data collection methods through observation, RMA measurement, and RSSI measurement. The data that has been collected will be analyzed using the RMA (Reliability, Maintainability, and Availability) and RSSI (Received Signal Strength Indication) methods. The results obtained show that most of the measurement days recorded network availability (availability) of 100%. However, there was a decrease on August 26, 2024 (99.58%) and September 3, 2024 (97.05%) due to the increased frequency of system failures. The analysis of RSSI showed that the signal quality fell into the excellent category with an average of -36.6 dBm. However, a decrease was recorded on August 30, 2024, with a value of -44 dBm. The results of this study underscore the importance of regular maintenance and upgrades to the network infrastructure in anticipation of possible deterioration. Recommendations include improving security systems, hardware updates, and technical training for IT staff to strengthen the network's support of activities at the BPKAD Office of Berau Regency

Evaluation of Cybersecurity Awareness and Training for Digital Branch Frontliners at Bank XYZ

The digital transformation in the banking sector has driven a shift in operations, including the establishment of digital branches that rely on information technology to deliver services to customers. However, the increased use of technology brings significant information security risks, particularly those stemming from human factors. This study aims to evaluate the level of cybersecurity awareness among frontliners at Bank XYZ’s digital branch using the ISO/IEC 27002:2022 framework and to develop training recommendations based on NIST SP 800-50. The research was conducted using both quantitative and qualitative methods, involving questionnaires and observations of 36 frontliners. The evaluation results revealed that several controls, particularly Response to Information Security Incidents (ID 5.26), still showed low levels of understanding (60%), indicating the need for training intervention. Training recommendations were designed based on the Cybersecurity and Privacy Learning Program (CPLP) principles from NIST SP 800-50, which include visual approaches, role-based training, and digital learning media. The implementation of these recommendations for one of the controls showed a significant improvement in post-test scores (average >= 93), exceeding the 85% threshold. This indicates that the CPLP-based approach is effective in enhancing frontliners’ cybersecurity awareness. This research is expected to serve as a reference for other banks in developing adaptive information security training strategies aligned with international standards

Bank Customer Decision Prediction on Term Deposit Products Using Random Forest Algorithm on Bank Marketing Campaign Data

This study investigates the relationship between Variable A, Variable B, and Variable C through a series of statistical analyses, including descriptive statistics, ANOVA, correlation, and multiple regression. The background of this research stems from the growing interest in understanding how these variables interact, particularly in practical applications involving behavioral or performance outcomes. The main objective of this study is to identify whether Variable A and Variable B significantly predict Variable C and whether there are significant differences across groups. Data were collected from a sample of 100 participants and analyzed using standard statistical techniques. Descriptive analysis provided a summary of the key variables, while ANOVA showed a statistically significant difference between Group 1 and Group 2, indicating the relevance of group membership. Pearson correlation revealed a moderate positive relationship between Variable A and Variable B, suggesting a tendency for these variables to increase together. In the multiple regression analysis, Variable A emerged as a significant predictor of Variable C, whereas Variable B did not contribute significantly. These findings highlight the importance of Variable A in predictive modeling and provide valuable insights for future research and application. The results align with the research expectations, though further studies are encouraged to explore additional predictors and refine the models. This study contributes to a deeper understanding of the statistical and practical relationships among the investigated variables and offers a foundation for applied strategies in relevant fields