This study aims to analyze the economic and social impacts within the environmental context on the sustainability of catfish farming in Basanah Village. Environmental issues such as water quality, land availability, and land-use changes have significantly affected aquaculture practices. Moreover, the social dynamics of the surrounding community, including patterns of cooperation, public perception of catfish farming, and economic impacts on farmers’ income and welfare, are central to the investigation. This research employs a descriptive qualitative approach through field observations, in-depth interviews, and documentation involving local farmers, community leaders, and relevant stakeholders. The findings reveal that economic aspects, such as production cost efficiency and income improvement, are highly dependent on stable environmental conditions and strong social support. Conversely, environmental degradation and weak social participation hinder business development. Therefore, participatory-based policies integrating sustainable environmental management with the strengthening of local socioeconomic capacities are urgently needed. This study offers a significant contribution as a foundation for formulating empowerment strategies for environmentally and socially-based small-scale aquaculture enterprises at the village level.
 

This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from the UNSW-NB15 dataset to create a more balanced training set. By leveraging synthetic data augmentation, our approach mitigates issues related to class imbalance and enhances the generalization capability of the classifier. Extensive experiments demonstrate that RF trained on the combined dataset of real and synthetic data achieves superior detection performance compared to models trained exclusively on real data. Specifically, RF trained solely on the original dataset achieves an accuracy of 97.58%, whereas integrating GAN-generated synthetic data improves accuracy to 98.27%. The proposed methodology is further evaluated through comparative analysis against alternative classifiers, including Support Vector Machine (SVM), XGBoost, Gated Recurrent Unit (GRU), and related studies in the field. Our findings indicate that GAN-augmented training significantly enhances detection rates, particularly for rare attack types, while maintaining computational efficiency. Furthermore, RF outperforms other classifiers, including deep learning models, demonstrating its effectiveness as a lightweight yet robust classification method. Integrating GANs with RF offers a scalable and adaptable framework for intrusion detection, ensuring improved resilience against evolving cyber threats.