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White shrimp (Litopenaeus vannamei) is an introduced species cultivated in Indonesia and plays an important role in increasing national shrimp production and exports. However, infectious diseases such as Enterocytozoon hepatopenaei (EHP) and Acute Hepatopancreatic Necrosis Disease (AHPND) pose a serious threat to the shrimp farming industry. These two diseases cause a decrease in shrimp growth and production performance. The Polymerase Chain Reaction (PCR) diagnostic technique is an effective method for detecting EHP and AHPND infections, because the results are fast and accurate. This research was carried out at the Juanda Fish Quarantine Laboratory, Animal, Fish and Plant Quarantine Center (BKHIT) East Java from July 1 to August 29 2024. This research aims to find out examination techniques and examination results for Enterocytozoon hepatopenaei (EHP) and Acute Hepatopancreatic Necrosis Disease (AHPND) during the examination process at BKHIT East Java. The test method used is a descriptive method by collecting primary and secondary data. The EHP examination at BKHIT East Java uses the single step PCR method, while the AHPND examination uses the Nested PCR method which consists of extraction, amplification, electrophoresis and visualization of the results. Based on the results of examinations during the activity, 4 samples were declared positive for EHP and no samples were declared positive for AHPND out of 51 samples of white shrimp (Litopenaeus vannamei).

Malaria remains a significant public health issue in Indonesia, particularly in endemic areas such as Keerom Regency, Papua. Efforts to control this disease face challenges due to Plasmodium resistance to synthetic drugs, highlighting the need for safer and more effective alternative treatments. This study aims to identify the secondary metabolite content of several local plants traditionally used as antimalarial remedies. An experimental laboratory study was conducted using phytochemical screening methods. The plants examined included turmeric rhizome (Curcuma longa), meniran leaves (Phyllanthus niruri), papaya leaves (Carica papaya), sambiloto (Andrographis paniculata), and brotowali (Tinospora crispa). Extraction was carried out using the maceration method with 96% ethanol as the solvent. The results showed that all samples contained secondary metabolites such as flavonoids, alkaloids, tannins, and saponins, which are known for their antiplasmodial activity. Extract yields ranged from 18% to 24%, with turmeric rhizome showing the highest yield. These findings suggest that local plants from Keerom Regency hold potential as raw materials for the development of herbal antimalarial drugs. This study serves as a preliminary step for further in vitro and in vivo bioactivity testing.

This study explores the integration of the Grounded Theory (GT) approach, developed by Anselm Strauss and Barney Glaser, into thematic hadith studies (maudhū‘ī) as a methodological alternative for extracting meaning and constructing theory from hadith texts. This approach is considered capable of producing more contextual, relevant, and applicable understandings of contemporary social issues such as justice, leadership, and environmental ethics. By employing GT’s systematic stages open coding, axial coding, and selective coding hadith research goes beyond mere thematic classification and evolves into a theorization process directly rooted in hadith data. While GT offers advantages in flexibility and contextual depth, its application also faces epistemological and technical challenges, particularly in terms of data validity, interpretive bias, and the integration of modern qualitative methods with the principles of ulūm al-ḥadīth. Therefore, an interdisciplinary approach is necessary, involving collaboration between hadith scholars and social researchers, alongside strong mastery of qualitative methodology. In conclusion, GT is not a replacement for classical methods, but a complementary tool that broadens the horizons of hadith studies. By combining the strengths of the Islamic scholarly tradition and contemporary methodologies, this approach has the potential to enrich Islamic epistemology and to present hadith as a living, dynamic, and relevant source of values for modern society.

Background: Chinese betel (Peperomia pellucida L.) is an herbal plant rich in bioactive compounds such as flavonoids, alkaloids, and tannins that have the potential to be antioxidants, antimicrobials, and antidiabetics. However, the stability and absorption of its active compounds in the body is still a challenge. Microencapsulation technology and  a floating drug delivery sistem (FDDS) can improve the effectiveness and stability of herbal preparations. Objective: This study aims to formulate and evaluate floating microencapsulation preparations containing Chinese betel herb ethanol extract using modern drug delivery technology. Methods: Ethanol extracts were obtained by maceration method and formulated by ionic gelation technique using a combination of sodium alginate and chitosan at three concentration variations (0.25%, 0.5%, and 0.75%). The evaluation included morphological analysis with SEM, ex vivo buoyancy test on the stomach of Wistar rats, determination of moisture content, and physical stability test using the cycling test method. Results: Microencapsulation with a concentration of 0.75% showed the most compact and delicate morphology, the lowest moisture content (0.64%), and the best physical stability. All formulations are capable of floating, with the longest buoyancy time at a concentration of 0.25% for 8 hours. There were no significant physical changes after six cycles of extreme temperatures. Conclusion: Microencapsulation preparations of Chinese betel ethanol extract in a natural polymer-based floating system show good stability and potential as an innovative herbal formulation to extend retention time in the stomach.    

The policy of granting Mining Business Licenses (IUP) to universities under Article 51A of the Minerba Bill raises legal, ethical, and sustainability concerns. This study applies a qualitative approach through literature review to examine the provision from the perspectives of Indonesian law and Islamic teachings. The findings reveal that allowing universities to manage mining permits risks diminishing their academic role, compromising scholarly independence, and fostering potential conflicts of interest. From the standpoint of positive law, this policy contradicts the precautionary principle, social justice, and educational objectives enshrined in the 1945 Constitution, the Higher Education Law, and the Environmental Protection Law. In Islamic thought, irresponsible exploitation of natural resources is classified as fasād (corruption), strictly prohibited, as reflected in Hadith No. 479 narrated by Bukhari. The study concludes by rejecting the provision and recommends repositioning universities as guardians of sustainability values rather than participants in extractive industries.

Nowadays, groundwater in the world has been widely exploited along with the increasing population. One of the impacts of massive groundwater extraction is the decline in groundwater levels. This potential could occur at one of the airports in East Java which is one of the most crowded airports in Indonesia. The large number of passengers at the airport has increased groundwater extraction due to the increasing need for sanitation, airport facilities, and other operational needs. This study aims to determine the potential for groundwater level decline at one of the airports in East Java. The research method used is a quantitative method by projecting secondary data from the results of groundwater level sampling carried out every 6 months. The results of the study showed a decline in groundwater levels at one of the airports in East Java in the next 20 years due to the increase in passengers each year. The results of this study are expected to be one of the preventive steps for all of us to maintain the sustainability of groundwater resources.  

This study presents an advanced AI-powered framework to detect and prevent cyberbullying across diverse social media platforms using a multiclass classification approach. Addressing the growing complexity and linguistic diversity of online abuse, the research integrates various machine learning (RF, LR, SVM) and deep learning (Bi-LSTM, BERT) models trained on a balanced dataset covering bullying categories based on religion, age, ethnicity, gender, and neutral content. Data preprocessing, tokenization, feature extraction via TF-IDF and CountVectorizer, and class balancing using SMOTE were applied to enhance model accuracy. The proposed system further supports real-time detection through social media APIs, offering dynamic monitoring and intervention capabilities. Among the tested models, Random Forest and BERT achieved the highest classification performance with 94% accuracy. Despite its robust architecture, limitations include dependence on English-language datasets, exclusion of multimodal data (e.g., memes, audio), and API restrictions that challenge scalability. Future development will focus on incorporating vision-language models and optimizing the system for real-time, multilingual, and multimodal environments. This study contributes to digital safety efforts by proposing a scalable and adaptive detection system suitable for safeguarding users from evolving forms of online harassment.

Galangal (Kaempferia galanga L.) is a herbal plant known to have anti-inflammatory, antibacterial, and antioxidant activities. The use of galangal extract in topical pharmaceutical preparations such as ointments, gels, and creams requires attention to the physical stability of the preparation to ensure the effectiveness and safety of the product. This study aims to examine the physical stability of various forms of galangal extract-based preparations through literature studies from three relevant journals. The method used is a descriptive literature study by analyzing the results of physical stability tests (organoleptic, pH, homogeneity, spreadability, adhesion, and viscosity) of ointments, gels, and creams. The results of the study showed that the gel form with a carbopol base provided the best overall physical stability, followed by an ointment with a water-washed base. Meanwhile, sunscreen cream made from galangal and seaweed showed quite good stability, although its spreadability and adhesion still need to be improved. This study provides an overview that the form and composition of the preparation greatly affect the physical stability of galangal extract, as well as the importance of choosing the right base in the formulation.

Wounds are damage to body tissues caused by various factors, both external and internal, which can disrupt the normal function of these tissues. Many studies have focused on using natural ingredients as adjunctive therapy to accelerate wound healing. One plant that shows therapeutic potential is Garcinia mangostana L. This study aims to assess the therapeutic potential of Garcinia mangostana L. stem bark and fruit peel extracts in accelerating the healing of mice (Mus musculus) incision wounds. The study used a completely randomized design (CRD) with six treatments and three replicates, namely negative control mice (K-), positive control (K+), groups of mice given fruit peel and stem bark extracts of G. mangostana concentrations of 25, 50, 75 and 100%. All mice were cut with a wound diameter of 1 cm. The wounds were then left for 13 days with an interval of 3 days for measurement. The results showed that the administration of G. mangostana fruit peel and stem bark extracts had an effect in accelerating the healing of mice incision wounds. From these results, Garcinia mangostana L. has the potential as an additional therapy in accelerating wound healing in mice, which can be used as a basis for further research related to its application in humans.

Coconut is a plantation plant that grows and is widely spread in various regions of Indonesia. The processing of coconut fruit (Cocos nucifera L) into VCO (Virgin Coconut Oil) through the extraction process is a method used to obtain high-quality oil. health benefits, as well as antibacterial and antioxidant properties. This study aims to examine the extraction process in processing coconut into VCO. The process begins with the selection of fresh coconuts, then the coconuts are grated and squeezed to produce coconut milk. Extraction is carried out without heating. The extraction results will produce high-quality VCO, which is characterized by clarity, a typical coconut aroma, and optimal fatty acid content. This study shows that the extraction method is effective in producing VCO with good characteristics, and can be an alternative to efficient coconut processing and economic value. Processing coconuts into VCO by extraction makes a positive contribution to the utilization of coconut natural resources and can increase the added value of processed coconut products.

Automatic object detection is one of the crucial aspects in the field of digital image processing that plays a vital role in various modern applications, ranging from security systems to pattern recognition in medical and industrial fields. This study aims to implement an automatic object detection method with a digital image representation-based approach using MATLAB software. The main focus is directed at the pixel-based image processing process, where each image element is processed to extract relevant visual information. In this study, stages are carried out starting from image acquisition, color conversion, image quality enhancement, threshold-based segmentation, to extraction of targeted object features. Digital images are analyzed through transformation into grayscale and binary forms to facilitate the detection process. The use of MATLAB provides flexibility in numerical and visual data processing, and supports various efficient image processing libraries.

Stunting remains one of the major nutritional problems in Indonesia. According to the 2024 Indonesian Health Survey (SKI), the prevalence of stunting in Papua Province reached 16.8%. A child’s nutritional status is strongly influenced by dietary intake, particularly protein, which plays a crucial role in growth. One potential source of animal protein is Indian mackerel (Rastrelliger kanagurta), a type of fish commonly found in Papua. Indian mackerel is rich in protein and omega-3 fatty acids but is highly perishable, limiting its shelf life. To address this issue, the fish is processed into flour. Additionally, green spinach is used as a source of fiber and minerals, although it is also prone to spoilage. The spinach is processed by extracting its juice, which is then incorporated into noodle products. Dried noodles were chosen as a medium for fortification because they are widely consumed across age groups and often used as a substitute for rice.This study aimed to determine the effect of adding Indian mackerel flour and green spinach extract on the chemical properties and sensory acceptance of dried noodles. The research methods included nutritional content analysis and organoleptic testing. The sensory evaluation was conducted by 25 semi-trained panelists who were nutrition science students

One chemical substance that can enhance the function and activity of the immune system is an immunomodulator. Immunomodulators can be found in various plants, including sembung leaves (Blumea balsamifera). This study aims to evaluate the potential of sembung leaf extract as an immunomodulator in experimental animals using a systematic literature review method. Articles were collected based on the keywords “immunomodulatory,” “sembung leaves,” and “body immunity” from the Google Scholar, Science Direct, and PubMed databases. The results of animal studies indicate that the administration of ethanol extracts of Sembung leaves at various doses has potential as an immunomodulator with anti-inflammatory, antipyretic, diuretic, and antidiarrheal effects. Additionally, Sembung leaf extracts have been shown to effectively inhibit bacterial growth and act as anti-inflammatory, diuretic, antipyretic agents, and anti-diarrheal.

Anemia is a common health issue among adolescent girls, primarily due to increased iron demands during growth and menstruation. This literature study aims to synthesize existing research on complementary therapies for managing anemia in this population. The study employed a literature review design with a systematic search of scientific articles published between 2018 and 2025. Data were collected from multiple electronic databases, including PubMed and Google Scholar, using keywords such as "anemia," "adolescent girls," "complementary therapy," "iron deficiency," "herbal medicine," and "nutritional intervention." Only peer-reviewed articles, clinical trials, systematic reviews, and meta-analyses published in English were included. The review found that natural interventions like Moringa oleifera leaf extract, beetroot juice, dates with lime juice, and educational programs are effective, affordable, and culturally acceptable for improving hemoglobin levels and anemia prevention. However, many studies have limitations such as small sample sizes and short intervention durations. Future research should focus on more rigorous designs with larger samples and longer follow-ups. Combining nutritional supplementation with health education offers a promising holistic approach to managing anemia in adolescent girls.

Electronic money laundering has become a critical issue as a form of cybercrime. Advances in technology demonstrate that digital forensic applications, particularly those utilizing smartphones, can be employed to uncover digital traces of money laundering transactions. This study aims to analyze the relevance of existing legal frameworks, the effectiveness of smartphone forensic techniques, and the challenges faced in their implementation in Indonesia. Based on a review of the literature and regulatory analysis, it was found that legal frameworks such as UU No. 8 of 2010 on the Prevention and Eradication of Money Laundering (TPPU) and UU No. 19 of 2016 on Information and Electronic Transactions (ITE) provide an adequate legal foundation. However, gaps in implementation, such as limited human and technological resources, remain significant obstacles. Forensic techniques, including metadata analysis and device extraction, have shown great potential in identifying the flow of illicit funds. Nonetheless, their application is hindered by data confidentiality and the lack of uniform technical standards. Given the increasing complexity of digital crimes, an integrated approach is required, combining regulatory strengthening with technological capacity building, to enhance the effectiveness of smartphone forensics in addressing electronic money laundering.

This study aims to analyze market sentiment towards Gold Financing Products (PKE) in Islamic banking before and after the Trump Effect phenomenon using the text mining method. This technique involves extracting information from unstructured text data to then be visualized and analyzed using the Natural Language Processing (NLP) approach and a RoBERTa-based classification model. Data was collected through web scraping from the X application with the help of API and processed using Google Colab. From a total of 4,074 tweets analyzed, it was found that the majority of public sentiment was neutral (59%), followed by negative (24%) and positive (17%). This reflects the public's tendency to discuss informatively rather than emotionally, although there was a spike in negative sentiment in certain periods indicating sensitivity to global dynamics, especially the impact of the Trump Effect on gold prices. The resulting wordcloud reveals key topics such as gold prices, buying and selling activities, and institutions such as Pegadaian Syariah and BSI. Terms such as "sharia", "riba", and "principles" emphasize the importance of Islamic financial values ​​in public perception. The results of this study indicate that text mining-based sentiment analysis is effective in capturing the dynamics of public opinion in real-time and can be a strategic tool for Islamic financial institutions in responding to market changes.

A Caesarean section, or C-section, is a surgical procedure where a baby is delivered through an incision made in the mother’s abdomen and uterus. To aid the healing of post-Caesarean wounds, mothers need not only antibiotics but also a diet that provides high-quality nutrition and adequate calories. The research question being investigated is: "Do Age, Parity, Education, and Occupation Affect the Healing Process of Post-Caesarean Wound Stitches?" This study uses a quasi-experimental design with a Post-test Only Control Group Design. The study population includes mothers who had a C-section at Pemalang Hospital. Participants were divided into two groups: 49 received catfish extract tablets (the experimental group), and 49 received standard wound care (the control group). Sampling was conducted using purposive sampling techniques. Data were analyzed through both univariate and bivariate methods. The results showed a p value of 0.000 for the experimental group and 0.046 for the control group (both p values < 0.05), indicating that catfish extract tablets significantly influence the healing process of post-Caesarean wound stitches..

The rapid development of financial technology (fintech), particularly digital wallet applications like OVO, has significantly transformed transaction patterns in society. However, issues such as server instability and unsatisfactory user experiences frequently emerge on social media platforms. This study aims to analyze user sentiments toward OVO on platform X (formerly Twitter) by comparing the performance of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Data were collected through web scraping from 1,000 Indonesian-language tweets containing the keyword "OVO." The research methodology included text preprocessing (data cleaning, tokenization, stopword removal), feature extraction using TF-IDF, and sentiment classification (positive, negative, neutral). Evaluation results demonstrated that SVM achieved the highest accuracy of 85.2%, while Naïve Bayes reached 78.5%. SVM also outperformed in precision (87%) and recall (83%) due to its ability to handle non-linear data. These findings provide actionable recommendations for OVO developers to enhance server stability and features based on user feedback. Additionally, this study serves as a reference for future sentiment analysis research employing algorithmic comparisons.

Background: Structural Health Monitoring plays a critical role in ensuring the safety, reliability, and sustainability of high performance composite structures used in aerospace, civil infrastructure, and mechanical systems. Conventional externally mounted sensors often face challenges related to environmental interference, maintenance complexity, and long term stability. Objective: This study aims to develop and validate an integrated smart composite monitoring system with embedded sensing capabilities that enhances damage detection accuracy and operational durability under varying mechanical stress conditions. Method: Smart composite specimens were fabricated by embedding fiber optic and piezoelectric sensors within fiber reinforced polymer laminates, followed by tensile, fatigue, and vibration testing. Signal processing techniques including time frequency analysis were applied to extract damage sensitive features, which were then classified using machine learning algorithms to distinguish healthy and damaged structural states. Results: The experimental findings demonstrate high damage detection capability, stable sensor performance under cyclic loading, improved reliability compared to conventional monitoring approaches, and consistent monitoring accuracy throughout the fatigue life of the specimens. The integration of embedded sensing and data driven analytics significantly enhances structural response interpretation and supports predictive maintenance strategies.

Red roses (Rosa damascena Mill.) are known to contain active compounds such as flavonoids, tannins, and vitamin C, which have potential antioxidant and anti-inflammatory properties. However, the utilization of rose flower waste after extraction remains limited. This study aims to formulate and evaluate gel preparations based on red rose extract and post-extraction rose flower waste as an innovation in natural-based topical formulations. This research is an experimental laboratory study using a post-test only design. Three concentration variations were formulated for both the extract (F1, F2, F3) and rose flower waste (F4, F5, F6) using HPMC as the gel base. Physical evaluations included organoleptic testing, homogeneity, pH, adhesion, spreadability, and viscosity. Data were analyzed descriptively and statistically using the Kruskal–Wallis test to determine the effect of concentration variation on physical parameters. All formulations showed good organoleptic and homogeneity results. The pH values were within the safe range for skin (4.75–5.92). Adhesion times met the criteria of >1 second, with a tendency to increase alongside higher concentrations of active ingredients. Spreadability fell within the acceptable range (3–5 cm), decreasing as viscosity increased. The viscosity values ranged from 20,150 to 20,182 cps. There was a significant effect of concentration variation on pH (p < 0.05), adhesion in extract-based gels (p = 0.021), and spreadability in waste-based gels (p = 0.024). In conclusion, gel preparations made from red rose extract and its waste can be successfully formulated with good physical stability and meet standard criteria for topical formulations. Rose flower waste has potential as an alternative active ingredient in the development of natural-based gel products.