Research was conducted on the detection of batik wastewater in the batik industry of Pekalongan, which generates liquid waste containing synthetic dyes, heavy metals, and hazardous compounds that can potentially pollute the environment if not properly treated. This study aims to develop a simple detection method based on digital image analysis to identify the color characteristics of batik wastewater. Data were obtained by sampling liquid waste from several affected rivers, which were then analyzed using a digital camera and image processing software to determine the intensity values of the red, green, and blue (RGB) channels. The results show that variations in waste concentration significantly influence the distribution of RGB values, enabling faster, cheaper, and more practical identification of pollution patterns compared to conventional laboratory methods. These findings are expected to serve as the foundation for developing a digital technology-based batik wastewater quality monitoring system as part of efforts to mitigate environmental pollution in Pekalongan.
 
Keywords - Batik wastewater, Digital image analysis, RGB intensity, Environmental pollution, Image processing

Variational Quantum Circuits (VQCs) have emerged as a cornerstone of hybrid quantum–classical algorithms designed to harness the computational potential of near-term quantum devices. By combining parameterized quantum gates with classical optimization, VQCs provide a flexible framework for tackling machine learning, chemistry, and optimization problems intractable for classical methods. This review comprehensively overviews VQC design principles, ansatz structures, optimization strategies, and real-world applications. Furthermore, we discuss fundamental challenges such as barren plateaus, the expressibility–trainability trade-off, and current noisy intermediate-scale quantum (NISQ) hardware limitations. Finally, we highlight emerging directions that could enable scalable, noise-resilient, and physically interpretable variational quantum models for future quantum computing applications

Empirical studies of possible compound corrosion inhibitors require a lot of money, time, and resources. Therefore, we used a machine learning (ML) paradigm based on quantitative structure-property relationship (QSPR) models to evaluate ensemble algorithms as predictors of corrosion inhibition efficiency (CIE) values. Our investigation reveals that the gradient boosting (GB) regressor model outperforms other ensemble-based models. This advantage is evaluated objectively using the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In summary, our research provides a new perspective on how well machine learning algorithms in particular ensembles work to identify organic molecules such as pyridazine that have the potential to prevent corrosion on the surfaces of metals such as iron and its alloys.