The Urabá region, known for its banana production, faces significant challenges due to seasonal droughts that affect crop productivity. The implementation of innovative technologies, such as efficient irrigation systems, is presented as a potential solution to improve the sustainability and profitability of plantations. This study validates the implementation of an irrigation system in a banana (Musa spp.) plantation located in the region of Urabá, in order to meet the water needs of the crop during periods of drought. A case study was carried out in a banana plantation in the region of Urabá, considering the maximum and minimum monthly losses due to drought, and a random sample was used to measure the weight before and after the implementation of the irrigation system, in order to carry out an economic analysis. The study shows that the implementation of a sprinkler irrigation system increases the average weight of the harvested bunches by 20%, which is reflected in an annual increase of 30.3% of exported boxes, obtaining satisfactory results in terms of internal rate of return, cost-benefit ratio and return on investment. The implementation of irrigation systems makes it possible to increase competitiveness in international markets, especially in regions such as Urabá, where the use of these technologies is still incipient.

How can social enterprises implement Total Quality Management (TQM) to tackle urgent social issues within their organizational framework while also ensuring their continued viability? To address this question, this study aims to explore the organizational approach to the adoption and implementation of TQM practices and their efficacy in mitigating pressing social challenges and maintaining financial sustainability. It adopts a qualitative multiple-case research design involving 3 social enterprises to explore the research phenomenon. Following qualitative research analysis process using NVivo, our findings highlight a prevalent, short-term outlook in managing TQM, hindering the full potential of TQM to achieve both social impact and organizational sustainability. More specifically, they expose a significant dissonance within the case organizations’ TQM implementations: the contrast between the current state, indicative of what it is, and the ideal state, indicative of what it should be. Altogether, the study advocates leveraging TQM for long-term excellence and alignment in social enterprises (as opposed to short-term mediocrity and disarray), thereby facilitating the achievement of both social impact and financial sustainability.

Tangerang City is characterized by its dense residential, commercial, and industrial activities and strategic proximity to Jakarta. This study aims to evaluate the strategic planning and implementation of innovative city initiatives in Tangerang, Indonesia, focusing on integrating blockchain, Internet of Things (IoT) big data technologies and innovation in urban development. This study has employed explanatory survey data from a structured questionnaire distributed to a diverse Tangerang community sample, including users and non-users of the “Smart City Tangerang Live” application. The survey was conducted for 2-months March to April 2022, included 71 and the sample included individuals across 13 districts, utilizing cluster sampling to ensure representativeness. The findings reveal a positive community response towards the smart city initiatives, with significant Engagement and interaction with the “Tangerang Live” application. However, technology access and usage disparities among different community segments were noted. The study highlights the critical role of intelligent technologies in transforming urban infrastructure and services, improving the quality of life, and fostering sustainable urban development in Tangerang. The implications of this study are multifaceted. For urban planners and policymakers, the results underscore the importance of strategic planning in innovative city development, emphasizing the need for inclusive and accessible technological solutions. The study also suggests potential areas for improvement in community engagement and public awareness campaigns to promote the adoption and efficient use of smart technologies.

This research is based on the condition of the ever-rampant events of illegal logging perpetrated by companies in various areas in Indonesia and Malaysia. The issue of corporate illegal logging happened due to a concerning level of conflict of interest between companies, the government, and local societies due to economic motives. this paper aims to analyze the law enforcement on corporate illegal logging in Indonesia and Malaysia as well as the law enforcement on corporate illegal logging that is based on sustainable forestry. this research used the normative legal approach that was supported by secondary data in the forms of documents and cases of illegal logging that happened in Indonesia and Malaysia. this paper employed the qualitative analysis. Results showed that Indonesia had greater commitment and legal action than Malaysia because Indonesia processed more illegal logging cases compared to Malaysia. But mere commitment is not enough as the illegal logging ratio in Indonesia compared to timber production is 60%. meanwhile, in Malaysia, it is 35%. This shows that the ratio of law enforcement in Malaysia is more effective when comparing the rate of illegal logging and timber production. The phenomenon of forest destruction in Indonesia happened due to a disharmonic situation or an improper social relationship between society, the regional government, the forestry sector, business owners, and the law-enforcing apparatus. The sustainable forest-based law enforcement concept against corporate illegal logging is carried out through the integration approach that involves various parties in both countries.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.