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The Secret of Loyalty: Management Strategy and Citizen Behavior in Public Services
No full textIn an era of dynamic social and economic transformation, public institutions are increasingly expected not only to provide services but also to build long-term relationships based on public trust. This book emerges from an in-depth study on marketing strategy, customer behavior, satisfaction, and trust within the context of public service delivery.
The author believes that management strategy in the public sector is no longer a mere administrative tool, but a strategic approach emphasizing the value of relationships, satisfaction, and user loyalty. This research bridges the gap between traditional management theory and public service management practice in Indonesia. Employing an empirical analysis using Structural Equation Modeling (SEM), the findings demonstrate that the implementation of management strategy and citizen behavior significantly influence satisfaction and trust, which in turn have strong implications for participant loyalty.
This book is written to broaden academic and public understanding of the importance of trust and satisfaction in strengthening public service systems. Readers will be guided to see how modern management concepts such as customer relationship, trust-building, and service quality can be effectively applied in public institutions. Therefore, this work is expected to contribute both conceptually and practically to the development of a more human-centered, professional, and sustainable model of public service management.
The author expresses sincere gratitude to Universitas Sangga Buana Bandung and all parties who have supported the completion of this research and publication. It is the author’s hope that this book serves as a valuable reference for scholars, policymakers, and practitioners committed to improving the quality of public services in Indonesia and across the ASEAN region
Physicochemical Characteristics of Smoked Dumbo Catfish Sausage Due to Differences in Liquid Smoke Concentration and Immersing Duration
No full textDumbo catfish (Clarias gariepinus) is currently in high demand among consumers. Product diversification is essential to increase its market value and competitiveness. One of the processing methods for dumbo catfish is smoked sausage production using liquid smoke. Smoked sausage is reported to contain antioxidants derived from smoke components. This study aimed to determine the physicochemical characteristics of smoked dumbo catfish sausage processed with liquid smoke. The research employed a factorial randomized block design with two treatment factors. The first factor was liquid smoke concentration (15%, 20%, and 25%), while the second factor was immersion time (15, 30, and 45 minutes). The parameters analyzed included moisture, fat, protein, phenol, and total acid content. The variation in liquid smoke concentration and immersion time significantly affected phenol and total acid content, while moisture, protein, and peroxide values were not significantly different. Moisture content ranged from 61.52% to 62.68%, protein content from 42.96% to 43.88%, phenol levels from 234.92 to 366.81 ppm (equivalent to 0.023–0.037%), and total acid content ranged from 5.72 to 7.25 mg/100 g. Overall, the analysis indicated that smoked dumbo catfish sausages produced with different combinations of liquid smoke concentration and immersion time still met the standards of SNI 01-3820-1995 on Fish Sausage and SNI 2725:2013 on Smoked Fis
Design, Simulation, and Implementation of a Low-Cost Digital System for Crowd Management
No full textThis paper presents a low-cost, modular, LDR sensor-based crowd management system. The system utilizes dedicated entry and exits detection modules, each incorporating an LDR–laser setup and LM358 comparator circuits to generate digital count pulses. These pulses are processed by independent controllers driving seven-segment displays for real-time entry and exit counts. A central controller calculates net occupancy, compares it against a user-defined threshold, and triggers the entrance gate to close, currently implemented as an LED indicator when the limit is exceeded. The system was simulated in Proteus with varying crowd flow rates to evaluate accuracy. The results show that the system can count to three people per second. The entrance gate responds within 445 µs, corresponding to the total measured propagation delay from input trigger to gate activation while consuming only 1.8 W of power. These findings demonstrate the system’s reliability, fast performance, and adaptability for integration with automated gates. Unlike existing high-cost solutions, this design uses simple, low-power components and a dual-controller architecture to maintain accurate counts with minimal processing overhead. The proposed solution is particularly suited for event venues, transport hubs, and other controlled-access environments where real-time crowd regulation is essential
Assessing Community Resilience against Climatic Disasters: A Case Study on Local Adaptation Strategies in the Barguna District
No full textNatural disasters driven by climate change significantly impact the socio-ecological system,
particularly in vulnerable regions like the Barguna District of Bangladesh. This study uses
quantitative and qualitative methods to examine community resilience and local adaptation
strategies against climate-induced disasters. Data were collected from 300 households,
complemented by focus group discussions and key informant interviews. Geographic
vulnerabilities, insufficient disaster education, and limited stakeholder representation exacerbate
the challenges that Barguna communities face. The study reveals that 52.5% of respondents hold
a negative outlook on their community’s future resilience to climate-induced disasters, with only
10.8% expressing a very positive perception and 4.9% being extremely optimistic about adaptation
and recovery. These findings underscore critical gaps in policy implementation, resource
allocation, and community engagement that hinder effective resilience-building. Analysis reveals
that local coping mechanisms, while effective to some extent, require substantial enhancement to
address the frequency and severity of disasters. Traditional methods such as crop diversification,
soil management, and early warning systems are underutilized due to inadequate infrastructure and
awareness. The study proposes an enhanced disaster management framework that integrates local
knowledge with government policies, addressing the 40.67% lacking understanding of the Disaster
Management Framework, the 45% advocating for community representation, and the 77%
emphasizing the urgent need for targeted strategies to manage recurring cyclone threats in
Barguna. This framework aims to strengthen community resilience, mitigate disaster impacts, and
support sustainable development in Barguna. By bridging the gap between policy and practice, the
research underscores the need for inclusive and context-specific disaster management strategies.
The findings provide actionable recommendations to improve preparedness and reduce the
socioeconomic and environmental repercussions of climate-induced disasters
Quantifying Public E-Participation through Social Media in Government Decision Making
No full textThe evolution of Information and Communication Technology (ICT) has enabled governments to implement online services, enhancing citizen participation in decision-making. Within e-government initiatives, e-participation plays a critical role in shaping public policies. However, a significant gap remains—the lack of a reliable instrument to measure and assess public e-participation through social media in government decision-making. Existing studies have explored e-participation factors but have not provided a validated tool for systematically evaluating this engagement. This study aims to bridge this gap by developing a reliable instrument based on a pilot study. A structured survey instrument was designed and validated through expert review and a pilot study involving 35 respondents. Statistical validation confirmed high construct reliability (Cronbach’s alpha > 0.6), ensuring the instrument's suitability for further research and practical application. The findings contribute to both academic literature and policymaking by providing a validated framework for assessing and enhancing citizen engagement in e-government
Automated Nutritional Guidance for Obesity Management: Insights from Machine Learning, Naïve Bayes, Random Forest
No full textObesity is a growing global health concern linked to numerous chronic diseases, requiring effective and personalized nutritional interventions. This study presents an automated nutritional guidance system designed to support obesity management through personalized diet recommendations. The system leverages user-specific data, including age, weight, height, activity level, and health goals, to generate tailored dietary plans using machine learning algorithms and nutrition databases. By integrating real-time feedback, food tracking, and adaptive meal suggestions, the platform aims to enhance user adherence and improve long-term outcomes. Preliminary evaluations suggest that automated guidance can offer scalable, cost-effective support while reducing reliance on continuous in-person consultations. The proposed system represents a promising advancement in digital health tools for obesity management. Obesity continues to pose a significant public health challenge worldwide, contributing to a range of non-communicable diseases such as type 2 diabetes, cardiovascular disorders, and certain cancers. Effective nutritional management is a cornerstone of obesity intervention, yet traditional approaches often face limitations related to accessibility, personalization, and long-term adherence. This paper presents the design and development of an Automated Nutritional Guidance System aimed at enhancing obesity management through intelligent, user-centered dietary recommendations. The system utilizes a combination of machine learning algorithms, nutritional databases, and user input to provide personalized dietary plans aligned with individual health goals, dietary preferences, and lifestyle patterns. Key features include real-time meal suggestions, nutrient tracking, behavior monitoring, and adaptive feedback mechanisms
Stock Price Prediction of Bank Rakyat Indonesia Using an Ensemble Stacking Model of K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)
No full textNeed for future price forecasts by investors faces difficulties in achieving accurate predictions because market changes exist. Standard single models do not accurately model stock market behaviors because of their complex nature. The problem solution implemented by the study involves combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) to create ensemble stacking. Research personnel collected Bank Rakyat Indonesia's (BRI) historical stock price data using KNN and SVM models. Studio performance delivers superior predictive results with lower error rates than KNN and SVM models that operate individually. Study results demonstrate stacking technology produces the most desirable results for stock market price prediction
The Role of Strengthening Exercises in Managing Osteoporotic Spine in Postmenopausal Women: A Comprehensive Review
No full textOsteoporosis is a chronic disease that occurs in postmenopausal women characterized by low bone mineral density (BMD) and elevated spinal fracture risk. Resistance exercise is routinely advised, but the best protocols are still under research. A literature review from 2017 through 2025 was performed. Twelve trials were selected, comparing the impact of resistance training on spinal BMD and spinal fracture risk in postmenopausal women. Supervised moderate- to high-intensity resistance training improved BMD as much as 6–9% and functional mobility 10–15%. High-impact training produced extra lumbar spine gains in BMD and adding exercise to drugs like zoledronate improved results further. Supervised long-term programs were superior to short-term or home-based programs. Resistance exercise is an important part of osteoporosis treatment in postmenopausal women. More research is needed to optimize exercise prescriptions for long-term bone health
Public Perception and Consumption Rate of Water Spinach (Ipomoea aquatica) among Malaysian Adult Population: Basis for Future Human Health Risk Assessment
No full textUnderstanding the consumption rates of water spinach is vital for accurately assessing its impact on health and nutrition among various demographic groups in Malaysia. This research provides an updated exploration of water spinach consumption patterns and public attitudes across the country. The study was conducted using a self-administered online questionnaire distributed to Malaysian adults (aged 18 and above), resulting in 191 complete responses collected between May 2023 and March 2024. Consumption rate data, initially derived from the monthly frequency and serving size reported by participants, was converted into weight-based measurements (where 84g is the standard weight of a vegetable serve) and subsequently weighted by ethnicity to improve demographic representativeness. A recent public survey revealed that the average daily intake of water spinach stands at 69.26 grams overall, with distinct gender differences: males consume an average of 80.52 grams, while females consume 36.90 grams. These variations highlight how dietary habits may differ between men and women, possibly influenced by factors such as cultural preferences or lifestyle choices. Consumption rates also differ significantly across ethnic groups. The Malay ethnic group reported an average consumption of 74.44 grams (males at 93.20 grams and females at 38.67 grams), showing a notable gender gap. In contrast, the Chinese community exhibited lower and more uniform rates, with overall consumption at 25.76 grams (males at 25.35 grams, and females at 26.12 grams), suggesting minimal gender disparity. The Indian community, however, reported the highest rates, with an overall average of 156.56 grams (males at 136.60 grams, and females at 43.04 grams), indicating significant variation within the group. The survey further revealed that while Malaysians possess a general awareness of water spinach—commonly valued for its affordability and versatility in local cuisine—this understanding lacks depth. Many may not fully grasp its nutritional benefits, such as its rich iron and vitamin content, or the best preparation methods to preserve these qualities. To address this knowledge gap, educational initiatives are recommended. These could include public health campaigns, community workshops, and school programs, designed to enhance awareness of water spinach’s health benefits, safe handling practices, and optimal cooking techniques, ensuring consumers are well-informed about this staple vegetable
From Prompts to Progress: Leveraging Generative AI in College English Writing Instruction
No full textWith the rapid rise of large language models like ChatGPT, generative AI (GenAI) is transforming language education. This study investigates prompt engineering in a college English writing course, showing how structured prompts scaffold the writing process. By aligning prompt types with stages of academic writing, the framework systematizes GenAI use and extends theories of scaffolding and meta-cognition. Findings indicate that prompts enhance engagement, support evidence-based argumentation, and enable recursive writing via real-time feedback. Compared with conventional teacher feedback, the model fosters autonomy, reflection, and frequent revisions, offering theoretical and practical insights for AI-supported L2 writing and sustainable English teaching