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The practice of religion in the media-facilitated virtual world has become a trend related to religiosity on the one hand and social life on the other. This study involves twenty-three pieces of literature in the form of journal papers and book chapters that discuss the topic of digital religion. All literature has undergone the PRISMA protocol's inclusion-exclusion criteria selection stage. The research method used was a Systematic Literature Review (SLR). Four research questions were posed to be researched using the SLR method, namely searching in the literature for the definition of digital religion, the shifts that occur in the practice of digital religion regarding sacred space, authority, identity, online worship, communion, and sacraments; theological views on digital religion; and sociological views on digital religion. It is concluded that digital religion is the implementation of religious practices online so that the offline-online boundary cannot be distinguished. Both are fused and complementary. As a result of being mediated by the internet and technology, the digitalization of religion results in a shift in form, implementation, and reconstruction in terms of new sacred space, new concepts of authority, digital identity, online worship, digital fellowship, and the performance of sacraments. Theologically, digital religion is a new way of presenting the kingdom of God beyond time and space in a networked society. Sociologically, digital religion represents not only an online religious entity but also a new culture in social devices with a social nature. This is due to the internet-based nature of digital religion, which can connect anyone in the same network to benefit the religious context. The digital world has become human daily life and needs revitalization of religiosity and human spirituality to fit the times.

With the shift from the industrial era to the digital era and the increasing importance of digital transformation (DT) in organizations, a new role in the top management team of organizations emerged, that of the chief digital officer (CDO). This emerging phenomenon relates to a Chief (C) - level executive position, focused on the development and implementation of the digital strategy in organizations. To provide a better understanding of the nature of this phenomenon, as well as to develop the basis from which scholars to be based on for further developing this research stream, we conduct a systematic review of 29 articles published in highly esteemed business journals. By synthesizing the review findings, we identified emerging themes and core areas of inquiry, and clarified the nature, typologies and role of CDOs in organizations. Moreover, based on the synthesis and gaps identified in this review, as well as via the transfer of theory from the CEO-TMT literature, several research propositions are developed for scholars to test in the future and a new concept is emerged, that of the Multifunctional CDO Leadership. In conclusion this study provides various implications to both research and management practice.

Analyses of Landsat TM and SPOT multispectral data were performed with a very detailed description of the vegetation cover in the field to get a relevancy and consistency of digital image classification in a semi-automatic approach. Three main vegetation types, i.e. primary forest, logged-over forest and secondary forest after clear cut were analyzed and the microclimatic parameters were also measured to describe the ecological condition of the vegetation. Spectral and textural analysis of data obtained from field measurements and spectral reflectance values of the remote sensing data are the main topic of this report as one aspect of study on the Digital Method of Detection and Monitoring on Forest Ecosystem Change Using High Resolution Satellite Data funded by the Indonesian National Research Council. This study shows that spectral reflectance values alone cannot differentiate the logged-over forest from the primary forest, but it is very sharply distinguished from the secondary forest. As for the texture analysis, it is possible to distinguish the logged-over forest from the primary forest, as shown by different values of degree of Entropy, although spatially, it is still doubtful. Key words: Indonesia/Jambi/Tropical rain forests/lowland areas/Remote sensing/Vegetation Analysis/ Logged-over forests/Primary forests/Secondary forests.

This study is aimed to get lipid producing microalgae as feedstock for biofuel production. The microalgae were isolated from 355 collected water samples which  represented many distinct ecosystems such as paddy fields, rivers, agricultural dams, ponds, swampy areas and unique ecosystem of volcano and mud-volcano craters in West- and Central Java, Indonesia. A total of  267 strains of microalgae were isolated from the samples of which  221 strains of them have capability to produce lipid. There were four promising strains that produce lipid between 14.7 – 45.7 percent dry weight in optimal condition that were identified as Chlamydomonas sp. KO-7267 and PK-7195, Chlorella sp. KS-7300 and Desmodesmus sp. BK-7291.  Keywords: microalgae, lipid, biofuel, Indonesian ecosystems

This article aims to explore the supply chain (SC) resilience capabilities of firms, focusing on their ability to respond to the COVID-19 pandemic disruption. Based on two cases each in the pharmaceutical and mineral water industries, this article identifies various strategies managers mobilized to tackle SC disruptions during the COVID-19 pandemic. This article contributes to theory through a proposed double-helix framework showing the dimension of disruptions and the capabilities concept to mitigate COVID-19. In addition, an in-depth investigation of the perceived importance versus actual SC resilience capabilities deployed is discussed and validated with practitioners. The findings of this article also address a critical gap in the SC operations management literature and provide a practical approach for managers to better manage future pandemic disruptions. IEEE

New digital technologies, combined with the challenges faced by the pandemic, have prompted several organizations, including small and medium sized enterprises (SMEs), to innovate their business models. We extent this article by studying the impact of strategic leaders’ entrepreneurial persistence on digital transformation and business model innovation (BMI). We also examine the role of market-sensing dynamic capability in these relationships. To test our hypotheses, we use survey data from 229 owners/CEOs and senior managers of SMEs based in United Arab Emirates. The results show that entrepreneurial persistence positively influences the adoption of digital technologies and BMI. Moreover, the adoption of digital technologies has a positive effect on BMI and mediates the direct effects of entrepreneurial persistence on BMI. The mediating influence is positively moderated by market sensing dynamic capability.

Water presents challenges in swiftly and accurately assessing its quality due to its intricate composition, diverse sources, and the emergence of new pollutants. Current research tends to oversimplify water quality, categorizing it as potable or not, despite its complexity. To address this, we developed a water quality prediction system (WaQuPs), a sophisticated solution tackling the intricacies of water quality assessment. WaQuPs employs advanced machine learning, including an ensemble learning model, categorizing water quality into nuanced levels: potable, lightly polluted, moderately polluted, and heavily polluted. To ensure rapid and precise dissemination of information, WaQuPs integrates an Internet of Things (IoT)-based communication protocol for the efficient delivery of detected water quality results. In its development, we utilized advanced techniques, such as random oversampling (ROS) for dataset balance. We used a correlation coefficient to select relevant features for the ensemble learning algorithm based on the Random Forest algorithm. Further enhancements were made through hyperparameter tuning to improve the prediction accuracy. WaQuPs exhibited impressive metrics, achieving an accuracy of 83%, precision of 82%, recall of 83%, and an F1-score of 82%. Comparative analysis revealed that WaQuPs with the Random Forest model outperformed both the XGBoost and CatBoost models, confirming its superiority in predicting water quality.

Indonesia needs a daily monitoring system due to its frequent fires and, more importantly, to assist stakeholders in the field in taking action to mitigate disasters. Our method simplified the number of hotspots for field-based purposes and was verified by comparing the point-based (point-HS) VIIRS (Visible Infrared Imaging Radiometer Suite) 375m-derived temperature anomalies (hotspots) and clustered-based hotspots (cluster-HS, our suggested method). Using Euclidean clustering, we calculated the distance between hotspot points and applied specific criteria to reduce the number of hotspots while aligning them closely with fire incidents. We evaluated accuracy at different fire sizes, burned areas, peatlands, and distances from the reported burn center. We found that the accuracy increases at 1.5 km from the center of the fire for both point- and cluster-HS at 52% and 53%, respectively. For areas larger than 14 ha, both types of hotspots yielded superior results of 83%. Cluster-HS performs better on peatlands than non-peatlands (62% vs. 57%). Without diminishing the precision of the hotspot observation, this study indicates that our method is reliable for assisting field stakeholders in the field in taking actions. Therefore, this product could be implemented into Indonesia’s daily hotspot monitoring.