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Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in driving economic growth and providing employment in Indonesia. However, many MSMEs are still utilizing digital technology to leverage social media as a marketing tool and strengthen their branding. This study aims to analyze the use of social media to increase product branding and sales at the Warung Kue HJ. Risna MSME, located in Karang Rejo Village, Balikpapan City. The research method used is a descriptive quantitative approach, with data collection techniques through field observations, direct interviews with MSME owners, and direct mentoring regarding social media use and online store creation. The results of this study indicate that improving social media accounts, creating business locations on Google Maps, and installing business banners with more attractive designs and colors can increase business visibility and understanding of the importance of branding through digital technology, positively impacting market expansion and potentially increasing business sales. This activity is expected to become a model for sustainable digitalization-based MSME mentoring to increase competitiveness in the era of digital transformation.

Stroke is a major cause of long-term disability and frequently results in balance impairment that limits functional mobility and increases the risk of falls. Impaired trunk control and postural instability are common consequences of stroke, highlighting the need for effective rehabilitation strategies. This study aimed to analyze the effect of Core Stability Exercise on balance improvement in stroke patients undergoing rehabilitation at the Medical Rehabilitation Polyclinic of RS Adhyaksa Jakarta. A case study design with a pretest–posttest approach was applied to three patients with ischemic stroke who presented with balance disorders. The intervention consisted of Core Stability Exercise, including bridging, bird-dog exercise, and seated balance exercise, conducted three times per week for eight sessions. Balance performance was measured using the Berg Balance Scale before and after the intervention. The findings demonstrated a consistent increase in Berg Balance Scale scores in all patients, indicating improvements in static and dynamic balance as well as a reduction in fall risk. Patients with better initial motor control showed faster and greater improvement, although positive changes were also observed in patients with more severe impairments. These results suggest that Core Stability Exercise contributes to enhanced trunk stability, postural control, and functional balance in stroke patients. The study implies that integrating Core Stability Exercise into routine physiotherapy programs may support more comprehensive and individualized stroke rehabilitation, particularly in addressing balance-related functional limitations.

The Baduy tribe is an indigenous community in Banten Province divided into two main groups, namely Inner Baduy and Outer Baduy, with varying levels of adherence to customary law and exposure to modernization. This article examines various government policies that affect the survival and identity of the Baduy tribe. These policies include the recognition of customary rights through Lebak Regency Regulation No. 32 of 2001, the designation of Kanekes Village as customary land, and the designation of the Baduy area as a tourist destination. Although these policies aim to protect the rights and preserve the culture of the Baduy, their implementation has created a dilemma. On the one hand, tourism opens up new economic opportunities and raises cultural awareness. On the other hand, the influx of tourists triggers significant social, cultural, economic, and environmental changes, especially among the Outer Baduy, including the adoption of modern technology, shifts in consumption patterns, and increased environmental pollution. Thus, preservation policies have ironically become drivers of change that undermine the Baduy community's original identity.

Civil servants (ASN) entering retirement represent a strategic human resource asset for national development; however, their potential is often underutilized due to an institutional vacuum. This study identifies this fundamental problem as manifesting in two dimensions: the absence of a cohesive structure among the retired community and the lack of a structure that systematically connects them to the economic empowerment ecosystem. Consequently, the transition from active service to a productive retirement period presents a significant challenge. The purpose of this research is to design an effective institutional forum model to serve as a facilitative bridge. The analysis employs a framework integrating three pillars: Mochtar Kusumaatmadja's Development Law Theory as the philosophical foundation, Ansell and Gash's Collaborative Governance concept as the governance paradigm, and the Pentahelix model as the operational framework. This study proposes a multi-stakeholder collaborative forum designed to synergize the roles of government, industry, academia, communities, and the media. The primary recommendation is the establishment of a formal entity functioning as an incubator and accelerator. This entity is intended not only to address the economic needs of retirees but also to align with the country's constitutional mandate to achieve sustainable social welfare.

This study to analyze the effect of Non Performing Loan (NPL), Capital Adequacy Ratio (CAR), and Net Interest Margin (NIM) on Return on Assets (ROA) with Operating Expenses to Operating Income (BOPO) as an intervening variable at PT Bank Rakyat Indonesia (Perser) Tbk. This research employs a quantitative approach using secondary data obtained from the annual financial statements of Bank BRI for the period 2015-2024. The data analysis method used in this study is Structural Equation Modeling (SEM) based on Partial Least Squares (PLS), which allows the examination of both direct and indirect relationship among variables in the research model. The independent variables consist of NPL, CAR, and NIM, the intervening variable is BOPO and the dependent variable is ROA. The results indicate that NPL has a positive effect on BOPO, suggesting that higher credit risk leads to increased operational costs. CAR and NIM have a negative effect on BOPO, indicating that adequate capital and effective interest income management improve operational efficiency contributes to increased bank profitability. The findings also confirm that BOPO mediates the relationship between NPL, CAR, and NIM on ROA. This study is expected to contribute to the academic literature on banking financial management and provide practical insights for bank management in enhancing operational efficiency and sustainable profitability.

This study examines the integration of technology in the process of Human Resource (HR) transformation through the perspective of employee data analytics as a strategic approach to modern HR management. The primary focus of the study is to analyze the impact of the simultaneous integration of digital HR systems and organizational digital transformation on improving the efficiency of HR functions, with organizational agility positioned as a moderating variable that strengthens this relationship. In addition, the study explores the potential optimization of Artificial Intelligence (AI) technologies and predictive analytics methods, such as Bayesian Optimization, in predicting workforce dynamics, including employee attrition risk and competency development needs, while also bridging the analytical skills gap among HR practitioners. The research method employed is a systematic literature review of relevant scientific publications from 2021 to 2025, selecting sources that address digital HR transformation, HR analytics, and the application of AI in organizational contexts. The findings indicate that digital HR systems have a strong and significant effect on enhancing operational efficiency and the quality of HR decision-making, and this effect becomes more optimal when supported by a high level of organizational agility. Furthermore, AI and predictive analytics are proven to generate more accurate predictions and simplify technical complexity, making them easier for HR practitioners to adopt. This study concludes that the success of HR transformation requires a holistic approach that aligns the use of advanced technologies with organizational capabilities, human resource readiness, and ethical considerations to create sustainable organizational value.

This study aims to elaborate on the mechanism of product design, word of mouth (WOM), price perception, and product quality on purchase decisions, with brand image serving as a mediating variable. The research focuses on consumers of Ogel-Ogel, a traditional snack product from Pemalang. Utilizing a quantitative descriptive method, data were gathered from 100 respondents through purposive and accidental sampling techniques. Data analysis employed path analysis and the Sobel test to measure the strength of the mediation effect. The results reveal that, partially, all independent variables and brand image exert a positive and significant influence on purchase decisions. The most crucial finding indicates that brand image acts as an effective mediating bridge, reinforcing the transformation of physical product attributes and social recommendations into actual purchasing actions. This underscores that for MSMEs in the culinary sector, visual uniqueness and quality are insufficient without consistent brand reputation management. This research provides strategic contributions for local product managers in integrating traditional marketing elements with brand equity strengthening to navigate an increasingly competitive market.

The growth of the digital economy in the 5.0 era has changed consumer behavior, particularly among students as digital natives. This study aims to analyze the influence of Celebrity Endorsers, Product Quality, Social Media Marketing, and Influencer Credibility on Purchase Intention, with Brand Image as a mediating variable. The research focuses on students in Pemalang Regency who use the Shopee e-commerce platform. A quantitative approach was employed, with data collected from 100 respondents through purposive and accidental sampling. Data analysis was conducted using multiple linear regression, path analysis, and the Sobel test with IBM SPSS 25. The results indicate that all independent variables have a positive and significant effect on Purchase Intention. In addition, Brand Image is proven to mediate the influence of these variables. The findings suggest that enhancing quality perception and selecting credible endorsers aligned with brand values are important strategies for Shopee to increase consumer purchase intention. This study provides empirical contributions to the development of digital marketing literature.

The development of e-commerce and digital financial services has changed consumer behavior, particularly among Generation Z. One emerging innovation is the Paylater service, which offers convenience and flexibility in transactions. While providing easy access, the use of Paylater also has the potential to increase consumer behavior and impulsive purchases. This study aims to analyze the role of financial literacy and lifestyle in the use of Paylater services among Generation Z through the Systematic Literature Review (SLR) method. A total of ten scientific articles published in the last five years were analyzed to examine the relationship between financial literacy, lifestyle, and Paylater usage behavior. The results show that financial literacy has an inconsistent influence and tends to act as a controlling factor, while a consumptive and hedonistic lifestyle is the dominant factor driving Paylater use. In addition, behavioral factors such as impulsive purchases and technological convenience also strengthen the tendency to use these services. This study concludes that increasing financial literacy balanced with controlling a consumptive lifestyle is very influential in encouraging the wise and sustainable use of Paylater.

Human resource quality serves as a crucial pillar for national development, as it significantly influences a country’s progress and competitiveness. Therefore, enhancing human resource capacity has become a strategic priority, particularly at the regional level where local governments play a vital role. The Government of Bojonegoro Regency has introduced several initiatives to support this objective, including the Ten Scholars per Village Scholarship Program, the Scientist Scholarship, and Final Project Assistance. Despite these efforts, several implementation challenges remain, such as cases of manipulated eligibility requirements among applicants, the absence of a digitized system for registration and document submission, and limited communication channels between program administrators and scholarship recipients. This research seeks to examine, explain, and evaluate the role of local government, especially the Education Office, in executing scholarship programs based on the theoretical framework of government roles as regulator, dynamizer, and facilitator. The study employs a descriptive qualitative method, utilizing observation, interviews, and documentation for data collection, followed by data analysis using the Miles and Huberman model. The findings reveal that although the local government has begun implementing scholarship programs, the execution is not fully effective. Supervision during participant selection focuses mainly on administrative completeness rather than document authenticity. Furthermore, direct outreach activities are limited to certain regions, coordination with village authorities remains minimal, and digital transformation has yet to be realized due to unsuccessful collaboration with the Communication and Information Agency despite several coordination meetings.

Decision-making under uncertainty is a major challenge in management, economics, and public policy, where outcomes cannot be accurately predicted due to limited information and environmental dynamics. This article conducts a systematic literature review of risk and probability approaches to decision-making under uncertainty, focusing on rational theory synthesis (such as expected utility theory, decision tree analysis, and Bayesian decision theory) and behavioral perspectives (prospect theory and heuristics). The review covers reputable literature from the last ten years to the present. The results show that the probabilistic approach provides a strong and adaptive rational framework, but has significant limitations due to cognitive biases such as loss aversion, overconfidence, and ambiguity aversion, which cause deviations from normative rationality. The integration of rational approaches with behavioral elements, through hybrid models, has proven to be more comprehensive and realistic for dealing with true uncertainty (Knightian uncertainty). These findings emphasize the need for a multidimensional decision-making paradigm in organizational and policy practices.

Background: The Maternal and Child Health (MCH) Book plays an essential role in enhancing pregnant women's knowledge of pregnancy health and labor preparation. However, its utilization remains suboptimal, which may affect a mother's readiness for childbirth. Objective: This study aims to analyze the relationship between pregnant women's knowledge of the MCH Book and their birth preparedness at the Mamajang Health Center in Makassar in 2025. Method: This research employs a quantitative design using purposive sampling with 86 pregnant women as respondents. Data were collected using a knowledge questionnaire about the MCH Book and a birth preparedness questionnaire. Data analysis was conducted using the Chi-Square statistical test. Results: The majority of pregnant women demonstrated good knowledge of the MCH Book (75.6%) and were categorized as prepared for childbirth (70.9%). Statistical testing indicated a significant relationship between pregnant women's knowledge of the MCH Book and their birth preparedness (p = 0.001). Conclusion: There is a significant relationship between pregnant women's knowledge of the MCH Book and their birth preparedness at the Mamajang Health Center in Makassar. Health workers are expected to enhance education and optimization of MCH Book utilization to support birth preparedness.

This study aims to develop an Indonesian traffic sign detection system using a transfer learning approach to improve road safety and traffic efficiency. The dataset was obtained from Kaggle and consists of 2,100 images across 21 traffic sign classes. The research stages include data collection, preprocessing to reduce noise and normalize image brightness, object detection using YOLOv5, and classification based on transfer learning with ResNet, VGG-16, and MobileNet architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the YOLOv5 model is capable of detecting traffic sign objects; however, the classification performance remains relatively low, with a mean Average Precision (mAP) value of 0.17. These findings suggest that further optimization is required in data preprocessing, dataset quality, and model parameter tuning to achieve better performance. This study demonstrates that transfer learning has significant potential for developing computer vision-based traffic sign detection systems, although further improvements are necessary to ensure robustness under real-world Indonesian traffic conditions.

The foundation of turbine machines in industrial buildings must be able to withstand static and dynamic loads caused by operational vibrations. Generally, reinforced concrete block foundations are used due to their good stability. However, under certain conditions such as limited land, decreased soil bearing capacity, and the need for structural rehabilitation, their application becomes less effective. Therefore, this study aims to analyze the use of Micropile foundations as an alternative for turbine machine foundations at Candi Baru Sugar Factory, Sidoarjo.The research method includes literature studies, soil data collection through Cone Penetration Test (CPT), machine technical data analysis, and the design and analysis of Micropile foundations. The analysis covers bearing capacity, settlement, dynamic response, pile cap design, and cost estimation.The results indicate that Micropiles with a diameter of 0.20 m, a length of 2 m, five piles, and a pile cap size of 2 m × 2 m × 4.5 m are able to safely and stably support the machine load. In addition, this system is considered more efficient in terms of construction time and implementation. Therefore, Micropile foundations are recommended as an effective and economical alternative for turbine machine foundations.

This study aims to analyze the relationship between production techniques, selection of textile materials and the level of sustainable fashion implementation in MSMEs in the fashion sector in Indonesia. The method used in this study is a quantitative approach with an expplanatory descriptive design. Data was collected through a questionnaire distributed to MSME respondents in the fashion sector and collected 100 respondents. The variables tested in this study are knowledge of sustainable fashion, production techniques used by MSMEs and textile materials applied in production. Data analysis used validity, readiness, t-test and multiple linear regression tests. The results of the study show that production techniques have a significant influence on the level of sustainable fashion implementation, with a p-value of 0.041. Meanwhile, textile knowledge and materials did not show a significant influence on the implementation of sustainable fashion with a p-value of >0.05. The value of the determination coefficient (R2) for production techniques is very high at 0.95, which shows that production techniques are the most influential factor in the implementation of sustainable fashion in MSMEs in the fashion sector to prioritize the development of sustainable production techniques as the main step towards increasing sustainability in the fashion sector.

This study examines the seismic performance of slender Air Traffic Control (ATC) towers in high‑hazard regions (PGA > 0.4g), where vertical taper, torsional eccentricity, and top‑heavy cab mass can significantly increase drift, base shear, and collapse risk relative to conventional buildings. Existing studies often rely on linear procedures and outdated provisions, leading to underestimation of nonlinear behaviour and limited guidance for ATC towers designed to SNI 1726:2019. The research aims to quantify these irregularity effects and formulate design recommendations that satisfy Immediate Occupancy, Life Safety, and Collapse Prevention performance targets. The methodology couples response spectrum analysis, using a site‑specific Padang spectrum consistent with SNI 1726:2019 and ASCE 7‑16, with nonlinear pushover analysis interpreted through FEMA/ATC performance‑based criteria. A parametric study is performed on three cab configurations small, medium, and large modelled as 5%, 15%, and 25% mass ratios at the tower head, while keeping a 10 m × 10 m hybrid core–frame shaft constant. Results indicate that larger cab mass produces systematic but moderate increases in global displacement, story drift, and base shear, while plastic hinges localize primarily in the upper stories and cab‑support region, yielding performance levels from Immediate Occupancy to Collapse Prevention. Overall, the tower meets code drift limits and acceptable performance if local strengthening is provided around the shaft–cab interface, offering a calibrated reference for top‑heavy ATC tower design in Indonesian high‑seismic settings and identifying priorities for future time‑history and soil–structure interaction studies.

The construction and infrastructure sectors are shifting toward lighter, low-emission, and sustainable materials in response to the high carbon footprint and excessive weight of common materials such as concrete and steel. One promising alternative widely developed is natural fiber–based composites. However, studies comparing mechanical properties of variations of natural fibers within a single framework remain limited. This study aims to evaluate and compare composite mechanical properties reinforced by sisal fiber, bamboo fiber, and pineapple leaf fiber to determine the optimal fiber type for sustainable infrastructure applications. The research methodology involved fabrication of composite specimens using a unidirectional fiber configuration with a resin matrix, molded following ASTM D638 Type I dimensional and geometrical requirements. Tensile testing was conducted to evaluate mechanical responses, including ultimate tensile behavior, deformation characteristics, and elastic properties, which were presented in tabular and graphical forms. The results show that incorporation of all natural fiber types significantly enhanced composite mechanical properties, exhibiting an average tensile strength of approximately 26 MPa. Pineapple leaf fiber demonstrated balanced mechanical behavior combining strength and ductility, while sisal fiber showed superior tensile resistance and rigidity. Bamboo fiber provided moderate mechanical improvement. Overall, natural fiber–reinforced composites demonstrate strong potential as environmentally friendly alternative materials for infrastructure applications, with mechanical characteristics adjustable based on reinforcing fiber type.

The growing pressure on urban drainage systems caused by blockages and environmental pollution highlights the importance of developing sustainable filtration materials. This study explores the use of coffee waste as an environmentally friendly filler integrated into polyester fabric (PE 24S) to create a green filtration medium for drainage applications. The research focuses on developing and characterizing the composite material through macro-photographic morphology analysis, tensile strength testing, and antibacterial evaluation. The findings demonstrate that polyester fabric modified with coffee waste shows a tensile strength of 54.024 ± 5.498 MPa, elongation of 111.128 ± 6.915%, and a Young’s modulus of 0.486 ± 0.543 MPa, indicating improved flexibility and sufficient mechanical durability for drainage system use. Additionally, antibacterial testing reveals that the composite material can inhibit microbial growth due to the presence of natural bioactive compounds found in coffee waste. Overall, the results suggest that coffee waste–enhanced polyester fabric offers a sustainable and mechanically reliable alternative for environmentally friendly drainage infrastructure while supporting circular economy practices.

This study aims to determine the effectiveness of using Problem Based Learning (PBL) oriented E-LAPD to improve students' metacognitive skills on chemical equilibrium material, especially on the sub-material of factors that influence chemical equilibrium shifts. This study was conducted at SMAS Hang Tuah 4 Surabaya class XII-2 as many as 25 students. The type of research used is research and development (R&D) with a 4-D model that was tested on a limited basis using a one-group pretest-posttest design. Scoring on the pretest-posttest uses N-gain (normalized gain) which aims to determine the increase in metacognitive skills through the results of students' pretest-posttest scores. The results showed that the N-gain value for each component of metacognitive skills, namely planning skills, monitoring skills, and evaluating skills, obtained values ​​​​sequentially, namely 0.85; 0.73; and 0.74 which are included in the high category, from the assessment shows that the use of PBL oriented E-LAPD is declared effective in improving students' metacognitive skills. This is supported by the results of the Metacognitive Awareness Inventory (MAI) questionnaire, which showed a percentage of planning skills of 82.43% with very good criteria, monitoring skills of 81.86% with very good criteria, and evaluating skills of 77.67% with good criteria. These results indicate that the MAI questionnaire can support pretest-posttest data to determine the improvement of metacognitive skills in students.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.