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This study examines the lived faith of Catholic migrants from East Nusa Tenggara (NTT) who have migrated to the Muslim-majority landscape of South Kalimantan, Indonesia. These migrants face a profound crisis of identity as they transition from a dominant religious environment to a marginalized minority status, necessitating a research objective that explores how their faith is reinterpreted amidst such socio-religious pressures. Employing a qualitative phenomenological-hermeneutical method, the research utilizes Paul Ricoeur’s threefold mimesis - prefiguration, configuration, and refiguration - as its primary interpretive framework. The findings reveal a significant narrative shift from an inherited “communal Catholic habitus” to a “refigured faith” characterized by personal agency and reflective commitment. This transformation is sustained through adaptive relational ethics, such as the sanctification of work and collaborative hospitality, which allow migrants to navigate their vulnerability. The study synthesizes these experiences to conclude that internal migration constitutes a vital locus theologicus, wherein the rupture of traditional religious structures does not erode faith but rather matures it into a more resilient, intentional, and relational existential orientation. Consequently, migration emerges as a transformative theological process that redefines the intersection of faith, culture, and minority existence in pluralistic societies.

The exponential growth of digital music catalogs on streaming platforms such as Spotify has made personalized recommendation systems crucial for enhancing user experience. This study develops a hybrid music recommendation system that addresses both warm-user and cold-user scenarios by combining Alternating Least Squares (ALS) collaborative filtering with content-based filtering (CBF) augmented by a popularity component. The dataset consists of 8,549,544 user-track interactions and a master file of 1,204,025 tracks with ten audio features. After preprocessing, users were segmented into 14,880 warm users and 723 cold users based on a five-interaction threshold. The ALS model was trained on the user-item implicit feedback matrix and tuned through grid search over factors, alpha, and regularization. CBF was implemented using cosine similarity on normalized audio features, while popularity scores were applied for new users with insufficient history. Evaluation used Precision@10, Recall@10, and NDCG@10. The final ALS configuration achieved NDCG@10 of 0.1116, representing a 30% improvement over baseline, while the hybrid CBF improved NDCG@10 for cold users from 0.0070 to 0.0201. Findings indicate that adaptive routing among ALS, CBF, and popularity reliably handles different user states, providing a practical foundation for production-grade music recommendation systems.

The limited use of interactive learning media has made it difficult for students to visualize the functions and assembly flow of computer components. This study aims to design and develop an Android-based educational game called “Assemble & Learn” as an interactive medium for computer assembly lessons, specifically for vocational high school students at SMK Negeri 1 Tanjung Jabung Barat. The development process follows the Multimedia Development Life Cycle (MDLC), which includes the stages of concept, design, material collection, assembly, testing, and distribution. The learning content covers several core competencies: KD 3.2/4.2 on computer assembly, KD 3.3/4.3 on assembly testing, KD 3.4/4.4 on BIOS configuration, and KD 3.5/4.5 on operating system installation, with a focus on KD 3.2 and KD 3.5. Research instruments consist of validation questionnaires for subject-matter experts, media experts, and student trials using the System Usability Scale (SUS). Validation results show that the educational game received an average score of 94% from media experts and 100% from subject-matter experts, both categorized as “Highly Feasible.” Meanwhile, student trials indicated strong acceptance, with an average SUS score of 85% (excellent usability). In conclusion, the “Assemble & Learn” educational game offers an innovative solution to boost learning motivation, simplify material visualization, and provide flexible practice opportunities, thereby supporting the achievement of computer assembly competencies in an optimal and effective way.

The shaft is a crucial component in mechanical systems because it serves to transfer power and rotational motion throughout the machine. This research aims to assess the structural strength and operational performance of shafts used in a tire shredding machine through numerical simulation methods in order to achieve a safe and efficient design. The study involved several stages, including the development of shaft geometry models, the determination of boundary conditions, load application, mesh generation, and stress analysis using the finite element method. Two shaft configurations were examined: a 59 mm diameter shaft made from AISI 1045 steel and a 49 mm diameter shaft manufactured from ASTM A36 steel. The simulation results indicate that the 59 mm shaft experiences a Von Mises stress of 8.9 × 10⁻⁵ MPa, with a maximum displacement of 0 mm and a safety factor of 15. Similarly, the 49 mm shaft shows a Von Mises stress of 8.4 × 10⁻⁵ MPa, no measurable displacement, and a safety factor of 15. These findings confirm that both shaft designs are capable of safely withstanding the applied working loads. In addition, cutting system tests revealed that a 24-tooth blade achieved an efficiency of 26.9%, while a 40-tooth blade reached only 22.3%, indicating that the 24-tooth configuration provides better performance.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

This study conceptually examines the influence of assurer type, assurance standards, and assurance level on the breadth of assurance statements in sustainability reports. Moving beyond prior literature that treats assurance as a binary variable (presence versus absence), this paper highlights disclosure breadth as a critical dimension of assurance quality and substance. Drawing on legitimacy theory and complemented by institutional theory, the study argues that the technical configuration of assurance shapes the quality of organizational legitimacy obtained by firms. Specifically, the type of assurer (public accounting firms versus non-accounting providers), the standards adopted (e.g., ISAE 3000 and/or AA1000AS), and the level of assurance (limited versus reasonable) influence the structure, systematic presentation, and comprehensiveness of assurance statements. Firms that engage reputable providers, apply globally institutionalized standards, and select reasonable assurance are more likely to issue broader and more detailed statements. In contrast, weaker institutional pressures may encourage symbolic assurance practices characterized by minimal disclosure. The study contributes theoretically by extending legitimacy theory to the technical dimensions of assurance and positioning disclosure breadth as a proxy for substantive legitimacy. Practically, it suggests that regulators and companies should emphasize transparency and comprehensiveness in assurance statements to enhance credibility and discourage symbolic sustainability reporting practices.

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

The PESIAR Program (Petakan, Sisir, Advokasi, dan Registrasi Map, Screen, Advocate, and Register) is an operational instrument of BPJS Kesehatan designed to accelerate the achievement of Universal Health Coverage (UHC) by increasing National Health Insurance (JKN) membership at the local level. However, the effectiveness of this program’s implementation is strongly determined by the institutional capacity and configuration that support it. This article aims to reposition institutional determination not only as a factor influencing program effectiveness, but as a model of administrative governance in achieving UHC. The study uses a mixed methods approach with a sequential explanatory design. Quantitative data were obtained through a survey of PESIAR Agents in Semarang City, while qualitative data were collected through open-ended questionnaires and interviews. The results show a strong, positive relationship between institutional determination and the effectiveness of the PESIAR Program. Qualitative findings further clarify that issues of program effectiveness largely originate from institutional capacity, the quality of cross-sector coordination, and the role of PESIAR Agents as field implementers. This study concludes that institutional determination deserves to be positioned as an administrative governance model for the PESIAR Program in promoting the achievement of UHC at the local level.

This study aims to analyze the implementation of the Free Nutritious Meal Program through a collaborative governance framework in Kendal Regency using a mixed-methods approach with a convergent parallel design. Quantitative data collected from 34 respondents were analyzed using descriptive statistics and Pearson correlation analysis, while qualitative data obtained from five key informants were analyzed thematically. The results reveal very strong and statistically significant correlations (p < 0.001) among cross-sectoral coordination, stakeholder roles, community participation, and accountability–transparency, indicating that collaborative synergy is a decisive factor in successful policy implementation. The Kruskal–Wallis test indicates consensus across professional groups (p> 0.05). However, qualitative findings uncover a paradox of coordination without communication: implementers internalize coordination as a functional operational reality, while beneficiaries experience informational alienation. The resulting collaborative configuration can be characterized as technocratic–instrumentalist—technically effective yet weak in terms of social legitimacy. This study underscores that implementing social policy requires collaborative governance that is not only coordinative but also communicative and deliberative. Policy implications include strengthening dialogic public communication, transforming consultative participation into deliberative engagement, and promoting participatory transparency to foster shared understanding between bureaucratic systems and program beneficiaries.

This study examines the lived faith of Catholic migrants from East Nusa Tenggara (NTT) who have migrated to the Muslim-majority landscape of South Kalimantan, Indonesia. These migrants face a profound crisis of identity as they transition from a dominant religious environment to a marginalized minority status, necessitating a research objective that explores how their faith is reinterpreted amidst such socio-religious pressures. Employing a qualitative phenomenological-hermeneutical method, the research utilizes Paul Ricoeur’s threefold mimesis - prefiguration, configuration, and refiguration - as its primary interpretive framework. The findings reveal a significant narrative shift from an inherited “communal Catholic habitus” to a “refigured faith” characterized by personal agency and reflective commitment. This transformation is sustained through adaptive relational ethics, such as the sanctification of work and collaborative hospitality, which allow migrants to navigate their vulnerability. The study synthesizes these experiences to conclude that internal migration constitutes a vital locus theologicus, wherein the rupture of traditional religious structures does not erode faith but rather matures it into a more resilient, intentional, and relational existential orientation. Consequently, migration emerges as a transformative theological process that redefines the intersection of faith, culture, and minority existence in pluralistic societies.

The increasing integration of internet technology in educational institutions requires structured network governance to ensure that digital resources support academic activities effectively. Unrestricted access to online platforms often leads to non-academic usage such as online gaming and social media engagement during instructional hours, which may reduce learning concentration and degrade network performance. This research develops and evaluates a network access control simulation using a MikroTik RouterBoard RB951Ui-2HnD device. The system applies firewall filtering mechanisms, hotspot-based authentication, and bandwidth allocation strategies through Simple Queue configuration. Network segmentation is implemented to differentiate teacher and student access privileges. The study adopts a Research and Development (R&D) approach to design, configure, test, and evaluate the proposed system. Testing results indicate that the firewall configuration successfully restricts access to selected online games (Mobile Legends, Clash of Clans, Roblox) and social media platforms (YouTube, TikTok, Shopee, Instagram, Telegram). Furthermore, bandwidth management demonstrates effective traffic prioritization, ensuring more stable allocation for teacher accounts in accordance with configured maximum limits. The findings confirm that structured firewall and bandwidth policies can improve network discipline, enhance performance stability, and support a controlled digital learning environment in schools.

Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.

Pyrolysis is a biomass conversion method into fuel through heating at high temperatures under oxygen-limited conditions. The main factors influencing the pyrolysis process include temperature, residence time, pressure, particle size, reactor design, and the type of pyrolysis employed. This study aims to design an auger-type fast pyrolysis system based on previous research. The design and modeling of the fast pyrolysis equipment were carried out using Autodesk Inventor 2021 software. Based on the calculation and design results, a fast pyrolysis reactor with a multi-stage configuration and a capacity of 5.2 kg was developed. The system consists of a three-stage reactor made of Stainless Steel 304. The reactor is equipped with a screw conveyor for material transport, which is driven by an electric motor. Biomass heating inside the reactor is provided by a clamp heater with an electrical power requirement of 611 W, while biomass cooling is performed using a condenser with a cooling water capacity of 15.586 liters. Based on the structural simulation results, the maximum von Mises stress obtained was 35.4 MPa, the maximum displacement was 0.0528 mm, and the safety factor was 6.07 under loading conditions including an internal reactor pressure of 0.32 MPa, a torsional moment of 1,130 kg·mm, and an operating temperature of 700 °C. These values are within the allowable limits of the material, indicating that the designed reactor is structurally safe and feasible for use.

The increasing demand for electrical energy each year and the high dependence on fossil energy, which has negative environmental impacts, necessitate the development of alternative renewable energy sources. One potential source that can be utilized is mechanical energy from human activities through the application of piezoelectric technology in paving blocks. In addition, studies on the effect of piezoelectric circuit configurations, particularly comparisons between series and parallel circuits in generating electrical power, are still limited. This study employed an experimental method using a piezoelectric paving block prototype, with testing conducted under a static load of 60 kg. The measured parameters included output voltage and current, which were then used to calculate the generated power. The experimental results show that the parallel circuit configuration produced a higher average electrical power of 1.51 mW compared to the series circuit, which generated an average power of 1.37 mW. The increase in power in the parallel configuration was mainly influenced by the higher output current, while the difference in voltage was relatively insignificant. These findings contribute to determining a more optimal circuit configuration for the development of piezoelectric paving blocks as a renewable energy harvesting system based on mechanical pressure.

This research is motivated by the problem of uneven bandwidth distribution in hotspot internet services at Oey Alycia Resto & Cafe, which negatively affects connection quality for users. The purpose of this study is to design and implement a bandwidth limitation-based hotspot internet system by applying a Fair Usage Policy (FUP) using Mikrotik devices, in order to achieve fair and efficient internet access distribution. The research method includes network requirement analysis, network topology design, Mikrotik RouterOS configuration, and system performance testing through direct observation and user questionnaires. The system implementation involves hotspot user authentication, per-user bandwidth allocation, and automatic speed reduction when data usage exceeds the predefined FUP threshold. The results show that the implemented system effectively limits excessive bandwidth usage by individual users, improves network stability, and ensures equitable internet access for all hotspot users. Furthermore, questionnaire results indicate positive user responses regarding improved internet service quality after the application of FUP. This research implies that the proposed system enhances the effectiveness of hotspot bandwidth management and can serve as a practical solution for public network administrators in optimizing bandwidth usage in a fair and controlled manner.

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.

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

The strategic role of SMEs in the Indonesian economy and the phenomena of declining performance as a result of the Covid-19 pandemic motivated author to research this sector. Various internal problems make it difficult for SMEs to improve their performance and competitiveness in a competitive business environment. Using the dynamic capability perspective, this study examines the influence of human resource configuration through employee training and organizational culture which is able to encourage innovation that improves SME performance. This study uses purposive sampling technique and data collection is done through questionnaires and interviews with 122 SMEs in East Java. Data analysis is carried out using PLS SEM. The research results show that employee training does not have a direct effect on SME performance, however employee training is able to encourage innovation which has an impact on improving SME performance On the other hand, organizational culture has a significant direct and indirect effect on SME performance trough innovation. Results of this study can be used as a consideration by SME owners in making business decisions in order to improve their performance. From the theoretical side, the results of this study confirm the dynamic capability theory by Teece and Pisano regarding the key role of innovation and Becker's opinion from a human resource management perspective which states that employee training must be designed systematically according to the long-term needs of the organization in order to have an impact on improving organizational performance.