Muhammadiyah University of Yogyakarta

Sustainable tourism villages face complex governance challenges because they must simultaneously support economic development, protect local culture, preserve ecological resources, and respond to changing social conditions. Existing studies have examined sustainable tourism governance mainly from administrative, community-based, or institutional perspectives, but less attention has been given to how distributed authority interacts with nonlinear adaptive dynamics in village-level governance systems. To address this gap, this study develops an integrated framework that combines polycentric governance theory and chaos theory. Using a quantitative explanatory design, survey data were collected from 590 stakeholders involved in tourism village governance across four regencies and one city in Yogyakarta Province, Indonesia. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that institutional diversity has the strongest association with sustainable tourism village governance, followed by decentralization and local autonomy, bifurcation and cosmology, community participation, flexibility and adaptability, edge of chaos and the butterfly effect, and self-organization and strange attractors. The model yields a very high coefficient of determination, which suggests a close within sample fit between the proposed constructs and the observed perception data. This result is interpreted cautiously because the study is cross-sectional and several constructs are conceptually proximate. The study contributes to governance theory by showing that sustainable tourism village governance can be understood as both polycentric and complex-adaptive. Practically, the findings suggest that policymakers and tourism village managers should strengthen multi-actor coordination while maintaining flexibility to respond to uncertainty and local change.

This study investigates the influence of religiosity, self-economy factors, philanthropic and environmental awareness, attitudes toward sustainable consumption, and sustainable disposal behaviour. A more comprehensive research model is proposed to contribute to the literature on sustainable behaviour, which remains highly limited despite its rapid growth in Indonesia. This study's millennials and Generation Z sample was analysed using SPSS 23 and SEM AMOS 29. Based on these results, religiosity and self-economy have no significant effect on attitudes. However, religiosity plays a crucial role in directly influencing sustainable disposal. Philanthropic awareness significantly affected sustainable disposal behaviour through attitude. Meanwhile, environmental awareness has proven to be influential and substantially contributes to determining sustainable disposal behaviour. Finally, it was found that attitudes significantly influence sustainable disposal behaviour directly but fail to mediate the relationship between religiosity, economic factors, and sustainable disposal behaviour. This research provides policymakers and other essential institutions with insight into textile waste reduction. The implications of this study are limited to the millennial and Generation Z samples in Indonesia.

PurposeThis paper aims to address the gap in understanding how artificial intelligence (AI) governance in postcolonial states reproduces historical power structures. This study develops the "algorithmic coloniality" framework to analyze how Indonesia's AI initiatives digitally reconfigure colonial and patrimonial hierarchies through centralized data platforms and uneven infrastructure.Design/methodology/approachUsing qualitative critical discourse analysis, this study examines Indonesia's national AI strategy, smart city blueprints and related policy documents. The study operationalizes the three dimensions of algorithmic coloniality, epistemic, institutional and spatial to trace how these initiatives reinforce historical inequalities.FindingsThis analysis demonstrates that AI governance initiatives marginalize local knowledge systems such as musyawarah mufakat (deliberation and consensus), entrench Java-centric patrimonial power via centralized data platforms and widen spatial inequalities through uneven digital infrastructure, thereby automating rather than mitigating historical inequities.Research limitations/implicationsThis study is limited by its textual focus on policy documents rather than implementation practices. Future research should examine how algorithmic coloniality manifests through lived experiences of communities affected by AI governance systems.Practical implicationsPolicymakers must integrate local wisdom into AI design, decentralize data governance and ensure equitable infrastructure investment to prevent algorithmic systems from reinforcing colonial-era inequalities in Indonesia's digital transformation.Social implicationsAlgorithmic coloniality risks marginalizing communities through epistemic exclusion, reinforcing center-periphery hierarchies and creating digital divides. This perpetuates social injustice by automating historical inequities rather than empowering diverse local voices in Indonesia's AI future.Originality/valueThis study introduces a novel, empirically grounded framework algorithmic coloniality bridging digital governance and postcolonial theory. It offers critical insights for policymakers and scholars seeking to develop decolonial and equitable AI governance in Indonesia and other postcolonial contexts.

The use of deep learning to detect and analyze structural damage in concrete is gaining increasing attention, but exploration of U-Net with various pre-trained backbone models has not been conducted. This study aims to overcome these limitations by developing a U-Net-based concrete crack segmentation framework that utilizes eight modern and lightweight backbones, namely EfficientNetB0, VGG16, VGG19, ResNet34, ResNet50, DenseNet121, MobileNetV2, InceptionV3, and Xception. A total of 458 labeled images from the Concrete Crack Segmentation dataset were used as training and testing data, representing the variety of concrete surface conditions in real environments. Evaluation was conducted using Dice and Intersection over Union (IoU) metrics. The test results showed that the Xception backbone provided the best performance, with a Dice value of 0.8461 and an IoU of 0.7384, surpassing EfficientNetB0 and VGG16/VGG19 which were previously considered stable in segmentation tasks. This finding confirms that the depthwise separable convolution mechanism in Xception is capable of extracting thin crack features more representatively. This study provides an important contribution in selecting the optimal backbone for concrete crack segmentation models, while opening up opportunities for implementing more accurate and efficient structural condition monitoring technology on an industrial and public infrastructure scale.

This study conducts a PRISMA-guided systematic review of Microbially Induced Calcite Precipitation (MICP) and Enzyme-Induced Calcite Precipitation (EICP) for improving the mechanical and hydraulic performance of clayey soils. The review synthesizes 37 peer-reviewed studies published between 2020 and 2025, evaluating relationships among microbial activity, chemical parameters, injection techniques, and soil mineralogy. Findings indicate that MICP generally yields higher unconfined compressive strength and lower permeability than EICP due to more efficient bacterial nucleation and calcite bonding. Controlled injection and electro-osmotic delivery enhance calcium carbonate uniformity, while extended curing promotes crystal densification and strength development. The addition of nano-SiO₂ and hybrid lime–MICP systems further refines microstructure and increases durability. Microstructural analyses using SEM, EDS, and XRD confirm rhombohedral calcite formation bridging clay particles, linking microscopic bonding with macroscopic strength gains. Key influencing factors include urea–Ca²⁺ balance, curing duration, and clay mineralogy. Identified limitations involve non-uniform precipitation, bacterial inactivity, and salinity inhibition, which can be mitigated through staged feeding, optimized ionic strength, and adaptive field protocols. Integrating sustainability assessment through Multi-Criteria Decision Analysis (MCDA) provides a framework for balancing performance, environmental impact, and cost efficiency. Overall, this review establishes mineralogy-aware and process-controlled bio-cementation as a scalable, sustainable alternative to traditional soil stabilization for fine-grained and low-permeability soils.