Purpose: This study aims to determine what variables affect during hydrolysis, and the results of this hydrolysis process will be tested for raw materials in making Bioethanol Research methodology: The research was carried out for 3 months, namely April – June 2023, which was carried out at the Laboratory of the Chemical Engineering Department of the Sriwijaya State Polytechnic, Palembang. Results: The ethanol content obtained from the hydrolysis of cempedak seeds is 28.37%.. Conclusion: The highest brix value of 8.7% was obtained at 100 minutes of hydrolysis with 3% HCl concentration. GC-MS results showed an ethanol content of 28.37% with a refractive index of 1.335. Limitations: One of the main limitations of this study might be related to the laboratory-scale nature of the research. Many studies are conducted on a small scale and have not been applied to industrial-scale production. This can be a constraint because a hydrolysis process that works at a small scale may not be easily translatable to mass production. Contribution: This article contributes to the utilization of agricultural waste, specifically cempedak seeds, which are often discarded or underutilized. This has the potential to reduce waste and add value to a product previously considered useless.

Purpose: This study aims to develop a classification system for the Clerodendrum paniculatum plant (Bunga Pagoda), focusing on its key parts—stems, flowers, leaves, and trees—using the Convolutional Neural Network (CNN) algorithm. The objective is to support conservation efforts and facilitate digital data grouping through technology-based classification. Methodology: The research involved collecting a dataset of images representing different parts of the Clerodendrum paniculatum plant. These images were then used to train a CNN model. The training process included 200 epochs to optimize performance. The model's accuracy and performance were evaluated using a confusion matrix to measure classification success across the plant's various parts. Results: The CNN model achieved its highest accuracy of 97.78% when trained for 200 epochs. The results indicated a significant improvement in evaluation metrics compared to models trained with fewer epochs. The mo   del successfully classified the plant parts with high precision, demonstrating its robustness and reliability for rare plant classification. Conclusions: This study confirms that the CNN algorithm is effective in classifying the parts of the Clerodendrum paniculatum plant. Increasing the number of training epochs substantially enhances the model's performance, making it a practical tool for digital plant conservation initiatives. Limitations: The study is limited by its reliance on a specific dataset, which may not encompass all possible variations of the Clerodendrum paniculatum plant under different environmental conditions. Contributions: This research contributes to digital plant conservation by developing a CNN-based classification system for rare plants. It highlights the importance of deep learning in biodiversity preservation and provides a foundation for future AI-driven botanical studies.

This study aims to synthesize and characterize ultrafiltration membranes based on polyethersulfone (PES) with the incorporation of titanium dioxide (TiO?) as an organic catalyst at varying concentrations (0%, 1%, and 2%). The goal is to evaluate the impact of TiO? addition on the structural and morphological properties of PES membranes to improve water treatment performance. Research/methodology: Membranes were fabricated using the phase inversion technique, employing N,N-dimethylacetamide (DMAc) as the solvent and polyvinylpyrrolidone (PVP) as the pore-forming additive. Characterization techniques included Fourier-transform infrared spectroscopy (FTIR) for functional group identification, scanning electron microscopy (SEM) and atomic force microscopy (AFM) for morphological analysis, contact angle measurement for hydrophilicity, porosity testing, and pure water flux evaluation. Results: The addition of TiO? improved the hydrophilicity of PES membranes, reducing the contact angle from 68.2° to 53.7°, and increased porosity from 48.78% to 67.75%. Morphological analysis showed enhanced pore distribution and surface structure. The membrane with 2% TiO? achieved the highest water flux of 5.77 L/m²·h. Although this value is below the typical ultrafiltration standard (10–50 L/m²·h), the improvements indicate the potential of TiO? to enhance membrane performance. Conclusions: Incorporating TiO? into PES membranes significantly enhanced their hydrophilicity, porosity, and structural uniformity. The optimal performance was observed at a 2% TiO? concentration. Limitations: The study is limited by the non-uniform dispersion of TiO? particles and was conducted under laboratory-scale conditions without real-world application testing. Contribution: This research provides insight into the role of TiO? in improving PES membrane characteristics and serves as a practical reference for developing advanced ultrafiltration membranes for water treatment applications.