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Fish bekasam is one of Indonesia's traditional fermented products that involves the activity of microorganisms, especially lactic acid bacteria (LAB), to produce unique organoleptic, chemical, and microbiological characteristics. The fermentation process of bekasam plays a significant role not only in extending the shelf life of fish but also in creating a distinctive sour taste and texture that differs from other fish products. This study aims to analyze the microbiological community involved in the fish bekasam fermentation process through observation and identification of bacteria using Safranin Violet and Iodine staining techniques. This study employs a literature review approach, examining various references related to the fermentation process, the dominant bacteria species, and the environmental factors that affect the quality of bekasam. The analysis revealed that lactic acid bacteria such as Lactobacillus plantarum, Lactobacillus casei, and Pediococcus spp. play an important role in the fermentation of bekasam fish. These bacteria produce lactic acid, which contributes to the sour taste and plays a role in forming the product's texture. The Safranin Violet and Iodine staining techniques were effective for identifying bacteria based on their cell wall characteristics, with Gram-positive bacteria dominating the fermentation process. Environmental factors such as the fermentation time, salt concentration, and the addition of ingredients like carbohydrates or turmeric extract influence the dynamics of bacterial populations in the fermentation of bekasam. This study also emphasizes the importance of controlling pathogenic bacterial contamination, such as Salmonella spp. and Escherichia coli, to ensure food safety in bekasam products. The findings provide a deeper understanding of the microbiological dynamics in fish bekasam fermentation and the relevance of staining techniques in microbiological analysis, which can be used to improve the quality and safety of traditional fermented products. This study also opens opportunities for developing safer and higher-quality bekasam products.

Wireless Medical Sensor Networks (WMSNs) are a key component of modern Healthcare Internet of Things (IoT) systems, enabling continuous and real-time monitoring of patients’ physiological parameters. These networks support timely medical intervention, improve patient outcomes, and facilitate remote healthcare delivery. However, due to the open and resource-constrained nature of WMSNs, they are highly susceptible to various security threats, particularly during the authentication phase. Existing authentication protocols have been found vulnerable to a range of attacks, including impersonation, session key disclosure, and gateway database compromise, which can lead to severe privacy breaches and potentially life-threatening situations. To address these issues, this paper proposes a secure and lightweight three-factor authentication protocol tailored for WMSNs in healthcare IoT environments. The proposed protocol integrates Elliptic Curve Cryptography (ECC) for strong public key-based security with minimal computational overhead, fuzzy extractors to securely handle biometric information and ensure resistance against biometric template compromise, and session-based randomness to achieve forward secrecy and prevent replay or key-compromise impersonation attacks. Security analysis demonstrates that the proposed protocol successfully mitigates prominent threats such as impersonation attacks, man-in-the-middle attacks, session key leakage, and database compromise. In addition, the protocol ensures mutual authentication between the user, the gateway, and the sensor nodes, while maintaining data confidentiality and integrity. Performance evaluation indicates that the protocol offers significantly reduced computational cost and communication delay compared to existing schemes. Its low energy consumption and minimal storage requirements make it suitable for deployment in resource-constrained medical devices and large-scale IoT healthcare networks. The results highlight the protocol’s scalability, energy efficiency, and robustness, making it a practical and secure solution for safeguarding patient data and ensuring trustworthy communication in WMSNs-based healthcare IoT systems.

This study aims to analyze the factors that influence students' learning motivation in the Lathe Engineering subject in class XI of the Department of Machining Engineering at SMK Negeri 1 Padang. Several variables studied include learning interest, ideals, friendship environment, and family environment, each of which acts as an independent variable, while learning motivation functions as a dependent variable. The method used in this study is quantitative with a correlational approach, where the research sample consisted of 51 students selected using a saturated sampling technique, namely sampling that covers the entire population in the class. The instrument used in this study was a questionnaire with a Likert scale that has been tested for validity and reliability to ensure that the measuring instrument used is able to extract accurate and consistent data. The data obtained were then analyzed using multiple linear regression techniques with the help of SPSS version 26 to determine the effect of each variable on student learning motivation. The results of the study indicate that partially, learning interest and friendship environment have a significant influence on student learning motivation. Students who have a high interest in the Lathe Engineering subject tend to have better learning motivation, and positive relationships with their friends also increase their enthusiasm for learning. In contrast, the variables of ideals and family environment did not show a significant influence on students' learning motivation in the context of this study. Although ideals and family support are often considered important factors in education, the results of this study indicate that external factors such as friendships are more dominant in influencing students' learning motivation. Simultaneously, all four variables significantly influenced students' learning motivation, with the friendship environment being the most dominant factor.

This study aims to utilize data mining techniques, specifically the K-Nearest Neighbors (KNN) algorithm, to classify leaf diseases in sugarcane (Saccharum officinarum). Early and accurate detection of leaf disease types is a crucial step in prevention and control strategies, thereby reducing potential crop losses caused by pathogen attacks. Leaf diseases in sugarcane, such as leaf scald, rust, and mosaic virus, are known to affect photosynthesis, inhibit growth, and reduce the quality and quantity of sugarcane produced. The classification process in this study was carried out through image analysis of infected sugarcane leaves, where features such as color, texture, and shape were extracted using digital image processing techniques. The KNN algorithm was chosen because of its non-parametric nature, ease of implementation, and its ability to provide accurate classification results even with limited data size. The working principle of KNN is to determine the class of a new sample based on the majority class of its k nearest neighbors in the feature space, making it very suitable for the case of leaf disease image classification. In addition to building a classification model, this study also examines disease prevention strategies based on the identification results. These strategies include the use of disease-resistant sugarcane varieties, the implementation of appropriate planting patterns, land moisture management, regular plantation sanitation, and the measured and environmentally friendly use of pesticides or fungicides. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics to assess model effectiveness across various data scenarios. The results of this study are expected to not only contribute to the development of decision support systems for farmers and related parties but also support the application of artificial intelligence-based technology in the agricultural sector.

Indonesia is recognized as one of the world’s richest countries in terms of biodiversity, including a wide variety of traditional medicinal plants. One of the lesser-known yet promising local species is matoa (Pometia pinnata J.R. Forst & G. Forst), whose leaves have been traditionally used by local communities to treat various ailments, including diarrhea and seizures. This study aims to investigate the pharmacological effects of matoa leaf extract, with a particular focus on its anticonvulsant activity, while also reviewing its potential chemical constituents as a basis for herbal medicine development. The research was conducted using a combination of literature review and experimental testing on mice (Mus musculus) induced with strychnine to trigger seizures. Key observed parameters included seizure onset time and survival duration (dead time) following the administration of either synthetic drugs or matoa leaf extract. The results demonstrated that the matoa leaf extract exhibited significant anticonvulsant effects, as indicated by prolonged seizure onset and increased survival time in test animals. These pharmacological effects are presumed to be related to the presence of active compounds such as flavonoids, tannins, and alkaloids, which may contribute to the stabilization of the central nervous system. Compared to conventional synthetic anticonvulsants, matoa leaf extract may offer a safer, plant-based alternative with potentially fewer side effects. The findings of this study provide valuable scientific evidence supporting the potential of Pometia pinnata leaves in the development of Indonesian herbal phytopharmaceuticals. Furthermore, they highlight the importance of further research, including preclinical and clinical trials, to validate efficacy, determine optimal dosages, and ensure safety for human use.

This study aims to analyze the differences in various extraction techniques and their relationship to the yield and biological activity of phytochemical compounds from natural materials. The research was conducted through a systematic literature review from various reliable scientific sources over the last decade. The results indicate that extraction methods such as maceration, soxhlet extraction, microwave-assisted extraction (MAE), and ultrasound-assisted extraction (UAE) have varying effectiveness. The effectiveness of these methods is highly dependent on the type of material, solvent used, and process parameters such as temperature, time, and solvent-to-material ratio. Modern extraction methods such as sonication, MAE, and UAE tend to produce higher yields and better biological activity. These modern methods have the advantage of being more time-efficient and requiring fewer solvents, thus allowing the extraction of active compounds more optimally. Additionally, technologies like microwave and ultrasonic waves help break down the cell walls of natural materials, enhancing the release of phytochemical compounds more effectively and rapidly. However, conventional methods such as maceration and soxhlet extraction remain relevant, especially for materials containing thermolabile compounds that may degrade at high temperatures. These methods are still preferred to maintain the stability of active compounds sensitive to thermal degradation. Choosing the appropriate extraction method is crucial in producing high-quality extracts that can potentially be used as raw materials for phytopharmaceuticals, supplements, or other natural products. This study provides a strong theoretical foundation for further experimental research and guidance in selecting extraction methods based on the required efficiency and effectiveness for industrial applications. Thus, this study contributes to the development of more efficient and high-quality natural products.

Breast milk (ASI) is the most perfect source of nutrition for infants, especially during the first six months of life. However, many postpartum mothers experience obstacles in producing breast milk optimally. One non-pharmacological alternative to increase breast milk production is the consumption of mung bean extract, which contains lactagogues and B-complex vitamins. This study aims to determine the effect of mung bean extract on breast milk production in postpartum mothers. The method used was a quasi-experimental design with a one-group pretest-posttest approach involving 10 postpartum mothers at PMB Bidan SRI, Selesai Subdistrict, Langkat Regency. The intervention was carried out for seven consecutive days, and data were analyzed using the Wilcoxon Signed-Rank test. The results showed a significant increase in the average breast milk production score from 1.20 to 2.90 after the intervention, with a p-value of 0.004. This indicates that mung bean extract has a positive effect on increasing breast milk production. These findings are expected to serve as a basis for healthcare providers to recommend mung bean consumption in lactation management for postpartum mothers.

Arginine deiminase (ADI) is a promising enzyme with significant therapeutic potential, particularly for its anticancer effects through the depletion of arginine in cancer cells that are auxotrophs. In this study, we aimed to optimize the production of ADI using clinical Escherichia coli isolates and to evaluate its antioxidant activity. A total of 25 E. coli isolates were obtained from 45 hospital samples collected in Wasit Province, Iraq. Optimization of ADI production was performed by systematically testing various factors including culture media, pH, carbon and nitrogen sources, incubation temperature, and time. The antioxidant activity was assessed using the DPPH radical scavenging assay. The highest ADI production was achieved using a modified M9 medium supplemented with 1% w/v sucrose as the carbon source and 5% w/v yeast extract as the nitrogen source. The optimal enzyme activity of 1.6 U/mg protein was observed at pH 7.0, 37°C, and after 24 hours of incubation. The crude ADI extract exhibited high antioxidant activity, with 79.28 ± 1.06% DPPH scavenging at 200 µg/mL, comparable to ascorbic acid, which showed 86.11 ± 1.45% DPPH scavenging. The study successfully optimized the conditions for enhanced ADI production based on clinical E. coli isolates, demonstrating its potential as both an anticancer enzyme and an antioxidant. The dual therapeutic potential of ADI warrants further research, including clinical trials, to explore its application in cancer therapy and as an antioxidant in medical treatments, offering promising avenues for future drug development and improved therapeutic strategies, particularly for targeting cancer and oxidative stress-related diseases.

Pain is an unpleasant sensation that can interfere with an individual's quality of life and requires effective management. Synthetic analgesics such as ketorolac, codeine, etoricoxib, and celecoxib are commonly used to relieve pain, but long-term use can cause adverse side effects, including gastrointestinal disturbances, dependence, and cardiovascular risks. Therefore, it is important to find safer, natural alternatives. One promising alternative is the extract of the dragon’s tail leaf (Rhaphidophora pinnata), a plant traditionally used in Indonesian medicine as a pain reliever. This study aims to evaluate and compare the analgesic effects of Rhaphidophora pinnata leaf extract with several synthetic drugs using the writhing test on male mice (Mus musculus). The mice were induced with pain by injecting a 0.5% acetic acid solution. The treatments included ketorolac, codeine, etoricoxib, celecoxib, and Rhaphidophora pinnata leaf extract. The results showed that all treatments, including both synthetic drugs and Rhaphidophora pinnata leaf extract, produced significant analgesic effects compared to the negative control (Na CMC 0.5%). Ketorolac showed the highest effectiveness, followed by codeine, etoricoxib, and celecoxib. Although the Rhaphidophora pinnata leaf extract showed potential as a natural analgesic, its effectiveness varied among individual test animals. Some mice showed a better response to the leaf extract, but overall, the analgesic effect was still lower than that of synthetic drugs. These findings support the use of medicinal plants as a safer alternative to synthetic analgesic drugs. Moreover, this study provides a foundation for further research aimed at isolating active compounds from Rhaphidophora pinnata leaf extract to develop more effective and safer pain-relieving medications.

Improvement amount Distributed Denial of Service (DDoS) attacks in cloud infrastructure and edge computing demands solution adaptive, distributed, and efficient detection in a way computing. Research This propose an optimized Federated Learning (FL) based DDoS detection model using Centroid Opposition-Based Bacterial Colony Optimization (COBCO) to training the Elman Neural Network (ENN). The proposed architecture consists of of two components Main: on the edge node side, a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) model is used to extraction feature local from traffic data network, while on the server side, model parameters from each node are collected and used for training an optimized ENN with COBCO. Approach This aim increase accuracy detection at a time maintain efficiency local data communication and privacy. In progress experimental, model tested use three benchmark datasets: NSL-KDD, CICIDS2017, and CICDDoS2019. The preprocessing process includes feature encoding categorical, normalization numeric, class balancing using SMOTE, as well as validation cross (k-fold). Initial results show that combination of FL, CNN–GRU, and COBCO–ENN produces improvement significant in accuracy and time convergence compared to approach conventional such as PSO, GA, and non- federative models. In addition, the proposed model capable maintain performance detection tall although executed in edge environment with limitations source Power.  Study This give contribution important in development system scalable, privacy-preserving, and adaptive intelligent DDoS detection to dynamics Then cross modern network. Integration of FL and COBCO in ENN training shows potential big for used in implementation real in cloud-edge infrastructure. In addition, the proposed model demonstrates strong scalability and adaptability, making it highly suitable for dynamic and evolving network environments.

This study compares the performance of two deep learning models, namely Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network (LRCN), in the task of recognizing human activity from videos. Human activity recognition is an important field in computer vision with many applications, such as security monitoring, human-computer interaction, and social media-based video analysis. ConvLSTM is a model that combines convolution operations with long-term memory LSTM, thus capable of capturing spatial and temporal information simultaneously. This approach is ideal for processing video data sequences that have spatial and temporal dimensions. On the other hand, LRCN combines the power of spatial feature extraction from Convolutional Neural Network (CNN) and temporal sequence modeling through Recurrent Neural Network (RNN), specifically LSTM, to understand movement patterns in videos. The study used the UCF50 dataset consisting of 50 activity classes, but was limited to five classes for the focus of the experiment. The dataset was divided into 80% for training and 20% for testing, and the model was drilled for 50 epochs using early stopping to prevent overfitting. The results show that both models have high training performance. ConvLSTM achieved a training accuracy of around 98% and a validation accuracy of 90%, while LRCN achieved a training accuracy of 99.5% and a validation accuracy of 88%. Although ConvLSTM demonstrated good stability on the validation data, further testing using TikTok videos as real-world data showed that LRCN had a higher confidence level in recognizing activities, with most predictions achieving confidence scores above 80%. This difference in performance indicates that while ConvLSTM excels in generalizing on training data, LRCN is more robust to real-world data variations.

Ultraviolet (UV) radiation has been known to cause various adverse effects on the skin, including erythema (commonly referred to as sunburn), premature aging, and, with prolonged exposure, the risk of developing skin cancer. To counteract these harmful effects, sunscreen products are commonly used to protect the skin from UV radiation. The development of natural-based sunscreens is gaining attention due to their potential safety and antioxidant benefits. One promising natural ingredient is thyme (Thymus vulgaris L.), a medicinal herb that contains several secondary metabolites such as flavonoids, alkaloids, and tannins. These compounds possess antioxidant and free radical scavenging properties, making them suitable candidates as active ingredients in sunscreen formulations. This study aims to determine the Sun Protection Factor (SPF) value of thyme herb extract using the UV-Vis spectrophotometric method. A qualitative approach was used to assess the photoprotective potential of the extract. The SPF values were measured at three different concentrations of the extract: 0.5%, 0.75%, and 1.0%. The absorbance of each concentration was recorded at wavelengths ranging from 290 nm to 400 nm using a UV-Vis spectrophotometer. The SPF values were then calculated based on the absorbance data processed using Microsoft Excel. The results revealed that the thyme herb extract exhibited high SPF values at all tested concentrations. Specifically, the SPF values obtained were 36.027 for 0.5%, 36.244 for 0.75%, and 36.516 for 1.0% concentration. All three concentrations fall under the "ultra protection" category, indicating strong UV protective potential. These findings suggest that thyme herb extract can be effectively utilized as a natural active ingredient in the formulation of sunscreen products. Further studies are recommended to explore its stability, safety, and efficacy in topical applications.

Virgin Coconut Oil (VCO) is a high-quality coconut oil that is extracted from fresh coconut meat without the use of high heat or chemicals, preserving its natural nutrients and beneficial properties. The demand for VCO has increased in recent years due to its various health benefits, such as boosting the immune system, improving digestion, and providing antimicrobial effects. The oil is rich in medium-chain fatty acids, particularly lauric acid, which is known for its positive effects on cholesterol levels and cardiovascular health. This study focuses on the production of VCO using a natural fermentation method, a traditional technique that is gaining attention for its simplicity and environmental friendliness. In this method, mature coconut meat is grated and squeezed to extract the coconut milk, which is then fermented at room temperature for 24–48 hours. The fermentation process allows the separation of the oil from the milk, forming a clear oil layer on top, which retains the characteristic aroma of coconut. The quality of the produced VCO was analyzed by determining its acid value and saponification number. The saponification value, which indicates the amount of alkali required to saponify the fat, was found to be 50.49. The acid value, which reflects the free fatty acid content, was measured at 5.61. These results suggest that the VCO produced through natural fermentation is of relatively good quality, with a favorable acid value indicating lower rancidity. Overall, the study demonstrates that natural fermentation is an efficient, eco-friendly, and cost-effective alternative for VCO production. This method not only preserves the natural properties of the coconut oil but also offers a sustainable approach to producing high-quality oil for various health and cosmetic applications.

Shallot (Allium cepa L. var. aggregatum) is a horticultural plant that holds significant economic value and therapeutic potential. This study aims to examine the potential of shallots as an immunomodulatory and anticancer agent based on a literature review of various experimental studies. The main bioactive compounds in shallots, such as quercetin, flavonoids, saponins, and sulfur-containing compounds, have been shown to modulate the immune system and inhibit cancer cell growth. As an immunomodulator, shallot extracts have been found to enhance phagocytic activity, stimulate immune cell proliferation, and balance the Th1/Th2 immune response. On the other hand, its anticancer activity is realized through cell cycle inhibition, apoptosis induction, and cytotoxic effects on various cancer cell types, including breast, colorectal, and prostate cancer. This review was conducted by analyzing experimental in vitro and in vivo studies published between 2014 and 2024. The synthesis results indicate that shallots hold strong potential as a supportive therapy for immune-related diseases and cancer. However, further clinical trials and toxicological studies are needed to confirm their efficacy and safety as a natural-based therapeutic agent.

This study aims to extract and purify secondary metabolite compounds from Coffea arabica leaves using various separation techniques, such as maceration, column chromatography, thin layer chromatography (KLT), and preparatory KLT, and followed by flavonoid content analysis using UV-Vis spectrophotometry. The process begins with drying the arabica coffee leaves, then extraction is carried out using 96% ethanol for three days. After that, the solvent is evaporated with a rotary evaporator at 60°C to obtain a viscous extract. The extracts were further separated using column chromatography with a mixed solvent of n-hexane: ethyl acetate (3:1), resulting in 12 fractions that showed color differences, which was an indication of the diversity of the compounds in the extract. Advanced identification was carried out with KLT using methanol solvents: chloroform (9:1) at UV wavelengths of 254 nm and 366 nm, indicating the presence of compounds with medium to high polarity. Furthermore, the isolated compounds were further separated using the preparative KLT (KLTP) method. To determine the flavonoid content, UV-Vis spectrophotometry was performed at a wavelength of 431 nm, using quercetin as standard. The results of the analysis showed that Arabica coffee leaf extract had a yield of 7.35% and a total flavonoid content of 19.0605 mgQE/g. These findings show that arabica coffee leaves are a source of flavonoid compounds that have the potential for the development of herbal or phytopharmaceutical products. The flavonoid content found may provide health benefits, particularly in increasing antioxidant activity, which can be used in the treatment of oxidative stress-related diseases. The results of this study also open up opportunities for the development of plant-based products, especially Arabica coffee, in the pharmaceutical and cosmetic industries as an efficacious natural active ingredient.

This study investigated the phytochemical screening and antibacterial activity against various microorganisms including E. coli, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, and Candida albicans. Active compounds were isolated and identified from Aleppo Oak Gallnut, obtained from the Iraqi plant Quercus Infectoria L. Plant extracts were prepared using a continuous extraction apparatus, Soxhlet, with a successive solvent system based on polarity differences, including petroleum ether (60-80°C), ethanol (78°C), and hot aqueous extracts. Acid hydrolysis was performed on the raw ethanol and hot water extracts to obtain free phenolic compounds, including Gallic acid, Apigenin, Rutin, Kaempferol, Chlorogenic acid, and Caffeic acid, using high-performance liquid chromatographic (HPLC) techniques. The inhibitory activity of Aleppo Oak Gallnut extracts (Ethanol and Hot Aqueous) after acid hydrolysis was tested using four concentrations (25%, 50%, 75%, 100%) against various microorganisms. The Ethanol extract exhibited a significant inhibitory effect on Enterococcus faecalis. The Hot Aqueous extract showed a modest inhibitory effect on Enterococcus faecalis. The Ethanol extract demonstrated strong inhibition against Klebsiella pneumoniae. The Hot Aqueous extract at 100% and 75% showed high inhibition. The Ethanol extract exhibited significant inhibitory effects against Staphylococcus aureus at 100%. The Hot Aqueous extract at 75% and 100%  demonstrated weaker inhibition against Staphylococcus aureus. The Ethanol extract demonstrated weaker inhibition against E. coli, and the Hot Aqueous extract showed no effect at 50%  and 25%, and slight inhibition at 100%  and 75%. For Candida albicans, the Ethanol extract showed minimal inhibition at all concentrations and no effect at 25%. The Aqueous extract had a slight effect at 100%  and no inhibition at 75%, 50%, and 25%.

This research aims to develop a recommendation system that can help retail business owners design more effective, data-driven promotional strategies. This system utilizes data mining techniques and the Apriori algorithm to extract association rules from consumer transaction data, thereby identifying more specific and accurate consumer purchasing patterns. Based on these patterns, the system can provide relevant promotional recommendations, such as product bundling, buy-one-get-one offers, or special discounts, which can attract consumer interest and increase sales. The system's implementation process is presented in the form of an interactive dashboard, which allows business owners to upload their transaction data, adjust analysis parameters, and visualize the promotional recommendation results in a way that is easier to understand and can be directly applied to their marketing strategies. This system not only provides well-structured promotional recommendations but also enables retail business owners to make more informed and efficient decisions in determining the type of promotion to implement, based on insights gained from analyzing their own transaction data. By utilizing this system, business owners can optimize their promotional strategies more efficiently and effectively, because they can quickly identify promotions that best suit consumer purchasing patterns. This can increase impulse sales, as relevant promotions will encourage consumers to purchase more products. Furthermore, this system shows great potential in increasing consumer engagement, as the promotions provided are more personalized and tailored to each consumer's preferences. Therefore, the implementation of this recommendation system has the potential to drive significant sales growth and help retail business owners achieve greater profits, as well as accelerate their business decision-making process. This system, ultimately, not only benefits business owners but also enhances the consumer shopping experience with promotions that are more tailored to their needs and preferences.

The betel nut (Areca catechu) is a tropical plant that thrives in Southeast Asia, including Indonesia. This plant has long been known in traditional medicine and local culture for its content of various beneficial active compounds. One form of its utilization that has rarely been scientifically studied is fresh betel nut juice. Therefore, this study aims to conduct a preliminary assessment of the characteristics of fresh betel nut juice, particularly from a physical, chemical, and microbiological perspective. The juice production process is carried out simply by squeezing fresh betel nuts without the addition of chemicals or heating. Based on initial observations, the betel nut juice exhibits a distinctive reddish-brown color and a sharp, pungent aroma. This color and aroma likely originate from the phenolic and alkaloid compounds naturally present in the betel nut. pH measurements indicate that the juice has a fairly high acidity level, which can affect the stability of microorganisms within it. Qualitative tests of the chemical composition of the betel nut juice indicate the presence of bioactive compounds, particularly tannins and alkaloids. Tannins are known to have antimicrobial activity, while alkaloids act as physiologically active compounds that can affect the nervous system. However, the presence of these compounds does not completely inhibit the growth of microorganisms. Through simple isolation of microorganisms from the fresh juice, bacteria from the genus Lactobacillus spp., which are typically associated with natural fermentation processes, were found. The presence of these bacteria indicates that fresh areca nut juice can be a potential growth medium for certain microorganisms, particularly lactic acid bacteria. This finding opens up opportunities for further research into the use of areca nut juice as a natural fermentation agent that may have functional and probiotic value.

Pedada leaves (Sonneratia caseolaris), a type of mangrove plant that grows in tropical and subtropical coastal areas, have high ecological and pharmacological value. In addition to functioning as coastal protection from abrasion and seawater intrusion, this plant is also known to contain various secondary metabolite compounds such as flavonoids, tannins, saponins, and alkaloids. These compounds have been widely reported to have important biological activities, one of which is as an antimicrobial agent. This study aims to identify the effectiveness of pedada leaf ethanol extract in inhibiting the growth of pathogenic microorganisms, especially bacteria that cause infections. The extraction process was carried out using 96% ethanol solvent through the maceration method, namely soaking the material in the solvent for a certain time to optimally obtain active compounds. The obtained extract was then tested for its antimicrobial activity using the disc diffusion method against test bacteria, both Gram-positive and Gram-negative. The inhibition zone formed around the disc was measured as an indicator of the antibacterial effectiveness of the extract. The results showed that pedada leaf extract was able to produce inhibition zones with varying diameters depending on the concentration used. The higher the extract concentration, the larger the diameter of the inhibition zone formed. This indicates a positive relationship between extract concentration and its antibacterial strength. This activity is believed to originate from the ability of compounds such as flavonoids and tannins to damage bacterial cell walls or disrupt microbial metabolic processes. This study provides initial evidence that pedada leaves have potential as an environmentally friendly and sustainable source of natural antibacterials. This potential is highly relevant in efforts to develop alternative antibacterial materials based on local plants, especially amidst increasing bacterial resistance to synthetic antibiotics.

Silica gel is one of the most commonly used adsorbents in the fractionation of bioactive compounds from medicinal plants. This is due to its ability to separate compounds based on differences in polarity, thus simplifying the purification process of active components. Fractionation itself is a crucial step in phytochemical research, as it aims to obtain active compounds in a purer, standardized form, and ready for further testing, both biologically and pharmacologically. This study aims to review the effectiveness of silica gel in the fractionation of active compounds from various types of medicinal plants. The study was conducted through a systematic literature review method with a qualitative-descriptive approach to 35 scientific articles published in the last five years. The articles analyzed were those that presented primary data on the use of silica gel in the fractionation process of medicinal plants. The results of the study showed that silica gel was proven effective in separating various polar compounds such as flavonoids, alkaloids, and phenolic compounds from crude extracts of medicinal plants. Some studies have even succeeded in isolating pure compounds that have significant biological activities, such as antioxidants, antimicrobials, and anti-inflammatory. The effectiveness of fractionation with silica gel is influenced by several important factors, such as the type and composition of the solvent, the elution technique (gradient or isocratic), and the support of advanced analytical methods such as thin-layer chromatography (TLC) and spectroscopy (UV-Vis, FTIR, and NMR). However, the use of silica gel still has several limitations, including the relatively high solvent requirements and long elution times. From the results of this study, it can be concluded that silica gel has great potential in supporting the fractionation process and isolation of bioactive compounds from medicinal plants. Therefore, optimization of silica gel-based fractionation techniques is important to support research and development of more efficient, standardized, and sustainable phytopharmaceutical products.