Accurate classification of marine species is critical for sustainable fisheries management, biodiversity conservation, and aquaculture optimization. This study leverages remote sensing data and a Kolmogorov–Arnold Networks-enhanced ResNet (ResNet-KAN) model to classify fish, shrimp, and seaweed distributions with high precision. A multi-spectral feature set, including sea surface temperature, chlorophyll concentration, oxygen availability, and salinity gradients, was utilized to enhance classification performance. Through an ablation study, temperature was identified as a key determinant, with its removal causing a 14.3% accuracy drop for fish and shrimp. Similarly, omitting chlorophyll concentration led to an 11.8% increase in seaweed misclassification, highlighting its role in distinguishing autotrophic from heterotrophic organisms. The proposed ResNet-KAN model demonstrated superior performance compared to conventional deep learning architectures, achieving an overall classification accuracy of 94.6%. Computational efficiency analysis revealed a complexity of \(O\left({N}^{2}L+DL\right)\), with processing times scaling predictably across species categories. These findings underscore the importance of integrating remote sensing-derived environmental predictors for robust marine classification. The proposed approach provides a scalable, high-accuracy solution for marine resource monitoring, with potential applications in ecosystem management and precision aquaculture. Additionally, this study includes an exploration of the economic and nutritional implications of the Free Nutritious Meals (MBG) program in Indonesia, assessing alternative strategies for improving its impact on public health and food security.

Purpose: This study aims to investigate the tangible impact of digital technology on improving the livelihoods and income of fishermen in Batam, in alignment with the principles of a sustainable blue economy. Research Methodology: The research employed a quantitative and qualitative approach through direct field surveys conducted with 52 local fishermen in Batam. The data collected focused on digital tool usage, income changes, and perceived benefits and challenges. Results: Findings reveal that digital technologies—such as meteorological forecasting applications and online marketing platforms—serve as crucial drivers for economic transformation. Approximately 80% of the observed income increases are attributable to digital literacy and technology use. Notably, over 50% of respondents reported a direct rise in income after adopting digital marketing strategies. Conclusion: Digitalization significantly boosts fishermen’s income and economic resilience. However, this transition is impeded by limited technological adaptation among older generations and inadequate internet infrastructure in coastal regions. Limitations: The study is limited by its sample size and geographic scope, focusing solely on fishermen in Batam, which may limit the generalizability of the results to other archipelagic regions. Contribution: This research offers critical insights for policymakers by highlighting the importance of accessible technology training and robust internet infrastructure. These are key to ensuring equitable digital transformation and enhancing the global competitiveness of local fishing communities in Batam.

Purpose -This study aims to find out what variables cause problems when people use financial technology. Methodology- This research uses quantitative methods with primary data as a data source. The polls used are all MSMEs registered as mentors in Sidoarjo Regency with data collection techniques using questionnaires with sampling techniques in this study, namely purposive techniques. The data analysis technique used is the Outer Model test including the validity and reliability of the data. Next is the Inner Model to test hypotheses using the SmartPLS program tool version 3.0. Finding-The finding showed that the variable of risk, transaction, revenue has a significant and positive effect on financial technology while the financial inclusion variable does not affect financial technology. Originality- The article is an original article that has never been submitted in any journal and this article is well organized and correct.

Geographically weighted regression analysis of electricity consumption in Indonesian households: aligning with SDG 7