The traditional Riau Malay beverage, Laksamana Mengamuk, is a cultural heritage asset facing existential challenges amidst modernization. This study explores its potential as an educational tool in urban public settings. The research method involved direct public socialization at the Car Free Day on Jenderal Sudirman Street, Pekanbaru, featuring outreach, a making-of demonstration, and product sampling. The enthusiastic and positive public response indicates that a direct educational approach combined with a sensory (taste) experience is highly effective for building emotional and cultural connections to local culinary heritage. These findings confirm that direct sensory engagement is an effective method for enhancing cultural awareness. Therefore, strengthening collaboration among stakeholders is recommended for the sustainable preservation of local culinary heritage within public spaces.

Indoor positioning technology based on smartphones plays an important role in the current technological development context. Especially in applications such as warehouses, supermarkets, hospitals, or buildings. While the global positioning system (GNSS) is popular and effective outdoors, it has several limitations when operating in enclosed spaces, such as indoors, due to the complexity of these environments. Smartphones have many built-in sensors (such as light sensors, sound sensors, gyroscopes, accelerometers, and magnetic sensors) and support the connection of various types of wireless communication technologies such as Wi-Fi and Bluetooth. However, such sensors were not initially developed for positioning applications. This study addresses the positioning problem using the MUSIC technique in conjunction with the Time of Arrival (ToA) method. The effectiveness of the positioning solution is evaluated through the signal-to-noise ratio (SNR) index. The absolute error and squared error indices are evaluated through the cumulative distribution function (CDF) to indicate the effectiveness of the proposed solution. Additionally, we propose a Pedestrian Dead Reckoning method to determine a person's position in indoor environments continuously. Based on the segmentation of the moving process by turns, the direction measurements in each segment are processed using a Kalman filter, which is designed to enhance the results achieved by the system. We also discuss the challenges and some future research directions in the field of smartphone-based indoor positioning.

Indoor positioning technology plays an important role in improving efficiency and automating industrial processes such as warehouse management, production lines, and mobile robot navigation. However, existing RFID-based and odometry-only localization methods still suffer from limited accuracy, drift, and dependence on predefined infrastructure. To address these challenges, this work proposes a lightweight sensor fusion framework that combines wheel encoder data and phase-based RFID signals using an Extended Kalman Filter (EKF) for accurate indoor localization of mobile robots. The proposed method does not require prior knowledge of the tag map and enables convergence even when the robot starts outside the reader's range. Simulation results demonstrate that the fused method achieves an average positioning error of 5.4 cm and a final error of less than 8 cm. An ablation study comparing odometry-only, RFID-only, and fusion scenarios confirms the superiority of the integrated approach in terms of accuracy and robustness. The system is suitable for real-time implementation in cost-effective embedded platforms and has potential for deployment in smart warehouses and logistics environments.

This study aims to describe elementary school teachers’ perceptions of the use of STEM-based instructional media as an effort to strengthen 21st-century skills. The research employed a descriptive qualitative approach with the subjects being elementary school teachers in the regions of Tasikmalaya City and Regency. The population included all elementary school teachers in both areas, with a purposive sample of 50 participants. The research instrument was a Likert-scale questionnaire developed based on three indicators of STEM literacy: (1) awareness of the role of STEM in modern society, (2) familiarity with basic STEM concepts, and (3) foundational fluency in STEM application. Data were collected using an online questionnaire via google forms, while the data analysis involved calculating the average score for each indicator. The results showed that for the first indicator, teachers in Tasikmalaya City had higher awareness (average score of 4.05) compared to those in the Regency (3.93). However, in the second and third indicators, teachers from the Regency scored higher, at 3.39 and 3.67 respectively, compared to city teachers who scored 3.30 and 3.59. These findings suggest that although urban teachers have stronger conceptual awareness, rural teachers demonstrate greater mastery in understanding and applying STEM practically in classroom learning. This difference is suspected to be influenced by contextual experience and more frequent implementation of hands-on learning approaches in the Regency.

The Ministry of Social Affairs, as a public institution, holds a strategic role in managing budgets for social welfare programs that directly impact the community. This study analyzes the performance of the Ministry’s financial reports using the Balanced Scorecard (BSC) approach, which encompasses four key perspectives: financial, customer, internal business processes, and learning and growth. The main objective of this research is to evaluate the extent to which the financial reports reflect the effectiveness and efficiency of budget management as a form of public institutional accountability. A descriptive qualitative method with a case study approach was employed, collecting data through in-depth interviews with relevant stakeholders and an analysis of published financial report documents. The data analysis was conducted interpretively through stages of theme categorization, coding, meaning interpretation, and validation using data triangulation, which were then synthesized into the Balanced Scorecard framework as a supporting evaluation tool. The findings indicate that, from the financial perspective, budget realization aligns with targets, though efficiency can still be improved. From the customer perspective, the public appreciates the social assistance but criticizes inaccuracies in recipient targeting. The internal business process perspective reveals that aid distribution functions relatively well, despite technical obstacles in the field. Meanwhile, the learning and growth perspective highlights the need to enhance human resource capacity. Overall, the Balanced Scorecard offers a comprehensive view of the Ministry’s financial performance and social policy implementation.

This study aims to analyze the impact of information overload on decision postponement, with customer confusion as a mediating variable, in the context of consumer behavior on e-commerce platforms in Indonesia. The study adopts a quantitative approach, conducted through a survey method using Google Forms as the data collection instrument, involving 150 active e-commerce users as respondents. Data analysis was carried out using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. The findings reveal that information overload has a positive and significant effect on both customer confusion and decision postponement. Moreover, customer confusion is found to significantly mediate the relationship between information overload and decision postponement. These results support the Stressor–Strain–Outcome (SSO) theory and indicate that excessive information load can lead to confusion, which in turn causes consumers to delay decision-making. This study contributes to the enrichment of theoretical literature in the field of digital consumer behavior and provides practical implications for e-commerce practitioners in managing the information presented to consumers.

Penelitian ini mengeksplorasi proses pengambilan keputusan investor dalam reksa dana ETF. Hasil penelitian menunjukkan bahwa perusahaan secara aktif meningkatkan pemahaman investor melalui berbagai saluran edukasi, termasuk media sosial, webinar, dan sosialisasi langsung. Pendekatan ini sejalan dengan prinsip-prinsip Keuangan Perilaku, yang mengakui bahwa investor sering mengandalkan heuristik dan dipengaruhi oleh bias yang dapat dikurangi dengan informasi terstruktur. Namun, investor juga menghadapi tantangan dalam pengambilan keputusan, seperti volatilitas pasar, misinformasi, kurangnya kepercayaan diri, dan pilihan yang berlebihan, yang mencerminkan bias perilaku umum seperti penghindaran ambiguitas. Untuk mengatasi tantangan ini, perusahaan mendukung investor dengan menawarkan layanan profesional yang tepercaya dan produk investasi yang jelas dan terstruktur, yang membantu menstabilkan perilaku investor dan membangun kepercayaan.

Many mechanical systems with elastic and damping properties are prone to resonant phenomena at natural frequencies under the action of external forces. Resonance phenomena can also be preserved in control systems of such objects closed by negative feedback, which reduces the quality of the functioning of these systems. The paper under review deals with the practically important problem of suppressing oscillations at natural frequencies in control systems for a linear oscillator, which is traditionally considered a dynamic object described by a second-order differential equation with complex roots of the characteristic equation. The focus of the work is the question of the roughness of the system in relation to inaccurate knowledge of the resonant frequency of the controlled object (which is typical for many technical applications).

learning, communication, and data management activities. This study aims to design a LAN network topology that suits the needs and environmental conditions, both at home and in small to medium-scale educational institutions. The methodology used includes hardware requirements analysis, network topology selection, and simulation using software such as Cisco Packet Tracer. The design results show that the use of a star topology for home networks and a combination of star and extended star topologies for schools provide optimal performance in terms of speed, scalability, and ease of maintenance. This design also takes into account aspects of security, cost efficiency, and ease of future development. The development of information technology drives the need for reliable and efficient computer networks, especially in home and school environments. The design of Local Area Networks (LANs) is an important solution in supporting integrated

The study presents a comprehensive framework for optimizing customer retention budget by integrating clustering, classification, and mathematical optimization techniques. The study begins with the IBM Telco dataset, which is prepared through data cleansing, encoding, and scaling.  In the preliminary phase, customer segmentation is performed using K-Means clustering, with k = 3 and k = 4 identified as optimal based on the elbow method and Silhouette score. The configurations produced three (Premium, Standard, Low) and four (Premium, Standard Plus, Standard, Low) customer segments based on purchase preferences, which served as input features for churn prediction. In the second phase, the dataset was divided into training and test sets in an 80:20 ratio, followed by data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN). Multiple classification algorithms were evaluated, including Naive Bayes (NB), Random Forest (RF), Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) using F1-score as the performance metric. CatBoost and LightGBM, with k values of 3 and 4, respectively, were the highest-performing classification models, with only minimal differences in performance.    Ultimately, customer segmentation established customer prioritization, whereas churn prediction assessed customer churn likelihood. Four distinct configurations were assessed utilizing mixed-integer linear programming (MILP) to optimise retention budget allocation within uniform budget constraints, discount amounts, and churn thresholds. In both the k=3 and k=4 scenarios, CatBoost surpassed LightGBM, with CatBoost at K=3 effectively discounting 66% of at-risk consumers across all three segments, hence improving the intervention's efficacy and budget allocation, making it the ideal choice for maximizing customer retention. The results demonstrate the importance of segmentation in enhancing retention budgeting and budget optimization, particularly concerning parameter sensitivity.