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This study aims to analyze the errors made by eleventh-grade students of SMA Swasta Eria in solving word problems related to quadratic equations and quadratic function graphs using Newman’s error taxonomy. The research employed a descriptive qualitative method involving 25 students who were given essay-type tests to measure both conceptual understanding and problem-solving skills. Data were collected from students’ written answers and analyzed according to Newman’s stages, which include reading, comprehension, transformation, process skills, and encoding. The findings reveal that errors occurred across all stages, with transformation errors and encoding errors being the most dominant. These mistakes generally stemmed from students’ inability to extract key information into correct mathematical models, inaccuracies in arithmetic operations, and insufficient understanding of fundamental quadratic concepts and their graphical representations. The results highlight the importance of instructional approaches that emphasize conceptual understanding, varied practice problems, and proper mathematical notation. Regular application of Newman’s error analysis is expected to help teachers provide more targeted feedback and improve students’ problem-solving abilities.

Plant growth regulators are organic or synthetic compounds that function to regulate and stimulate plant growth, both in the vegetative and generative phases. The administration of plant growth regulators (PGRs) is an effort to provide hormones to plants for optimal growth. The purpose of this study was to determine the growth and yield of expired corn (Zea mays L.) plants after the administration of various plant growth regulators. The research using a randomized block design consisted of 10 treatments where treatment A = new seeds + Aquades, B = new seeds + 500 cc / L ZPT bean sprout extract, C = new seeds + 500cc / L ZPT young coconut water, D = new seeds + 0.2 cc / L Auxin, E = new seeds + 0.2 cc / L Gibberellin, F = expired seeds + Aquades, G = expired seeds + 500 cc / L ZPT bean sprout extract, H = expired seeds + 500 cc / L young coconut water, I = expired seeds + 0.2 cc / L Auxin, J = expired seeds + 0.2 cc / l Gibberellin. Each treatment was repeated 3 times, until 30 experimental units were obtained. Based on the results of the study, the highest plants were found in treatment I = expired seeds + 0.2 cc / L auxin at plant age 6 MST with an average of 257,667. The observation of the highest number of leaves was found in treatment I = expired seeds + 0.2 cc / L auxin at plant age 6 MST with an average of 14,530 strands, the best stem diameter was in treatment F = expired seeds + Aquades, at plant age 5 MST with an average stem diameter of 28,733, observations on the weight of the cob with husk and the best weight of the cob without husk were found in treatment F = expired seeds + Aquades with an average of 287,200 grams, and 251,533 grams, observations on the length of the best cob in treatment F = expired seeds + Aquades with an average length of 19,900 cm.

This study examines the interplay between administrative ethics and bureaucratic decision-making within the governance landscape of Pulang Pisau Regency through a structured literature analysis. Anchored in the theoretical frameworks of Administrative Ethics Theory, Bureaucratic Decision-Making Theory, and Good Governance, this research synthesizes classical and contemporary scholarly works to identify the determinants, challenges, and institutional dynamics that influence ethical decision processes in local bureaucracy. The method employs a qualitative narrative literature review, involving systematic identification, screening, quality assessment, and thematic extraction of relevant academic sources published in the last two decades. Findings indicate that ethical norms, integrity, organizational culture, leadership morality, and institutional control mechanisms significantly shape the rationality, consistency, and legitimacy of bureaucratic decisions. Furthermore, the literature reveals that external pressures, conflict of interest, inadequate regulatory enforcement, and limited administrative capacity remain persistent barriers to the implementation of ethical governance in regional administrations. The study also highlights the essential role of participatory governance, digitalization, and internal accountability frameworks in strengthening ethical consistency across bureaucratic processes. These insights position administrative ethics as a crucial foundation for promoting transparency, fairness, and public trust in local government institutions. The paper concludes that strengthening administrative ethics requires integrated institutional reforms, long-term commitment, and the internalization of ethical values at both organizational and individual levels. The implications of this research extend to policy formulation, bureaucratic capacity-building, and future empirical studies on ethical governance in decentralized contexts.

Application of Multi-Layer Perceptron neural network to fault classification in high-voltage transmission lines is demonstrated in this paper. Different fault types on protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of Discrete Wavelet Transform energy features combined with zero sequence current magnitude as input features for neural network classifier. The proposed method uses eight extracted features to learn hidden relationship in fault signal patterns. Using proposed approach, fault detection and classification of all 11 fault types could be achieved with high accuracy. Improved performance is experienced once the neural network is trained sufficiently with 1188 fault samples, thus performing correctly when faced with different system conditions. Results of performance studies show that proposed neural network-based classifier achieves 96.18% average accuracy, which demonstrates that it can improve the performance of conventional fault classification algorithms, which in turn can provide more efficient solutions in the management and protection of high voltage electrical systems.

Hawthorn extract has been used for ameliorating cardiac disorders and pulmonary hypertension. The main chemical constituents of hawthorn flavonoid extract (HFE) include flavonoids (1-2%), oligomeric proanthocyanidins (1-3%), and other bioactive components (e.g., triterpene acids, organic acids, sterols, and cardioactive amines). These compounds are reported to have many pharmacological effects, including neuroprotective, hepatoprotective, cardioprotective, and nephroprotective effects. This study was aimed the analysis Crataegus oxyacantha based on a bioinformatic study and toxicity test on the chicken embryo. This method consisted of analysis of the three-dimensional structure of Crataegus oxyacantha Protein, Epitope and Allergen Proteins, Crataegus oxyacantha Proteins that were antigens and toxins and toxicity test on the chicken embryo. The results of research conducted on 3 three-dimensional structures of Crataegus oxyacantha protein, GQME value and QmeanDisCo value. In addition, this study also obtained the results of proteins that are epitope, antigenic, non-allergenic and non-toxic and toxicity test on the chicken embryo was 250 ng/egg..Morphological description of the embryo on the 21st day after injection, at a concentration of 250 ng of Crataegus oxyacantha /egg product, an abnormal embryological picture was obtained. Chicken Embryo Weight and Body Length Measurements were carried out in chicken embryos. Need research for other species of plant.

Free-range chicken eggs are a source of high-value animal protein but are susceptible to quality deterioration during storage. This study aimed to evaluate the effect of Averrhoa bilimbi L. leaf extract concentration and storage duration on the internal quality (Egg White Index, Egg Yolk Index, Haugh Unit, yolk color) and pH of free-range chicken eggs. An experimental method was employed using a Completely Randomized Design (CRD) with a 4 x 4 factorial pattern. The first factor was leaf extract concentration (0%, 5%, 10%, 15%), and the second factor was storage duration (7, 14, 21, and 28 days). The results showed that immersion in 5% Averrhoa bilimbi L. leaf extract significantly maintained internal egg quality compared to the control up to 14 days of storage. This treatment produced optimal values for the Egg White Index, Egg Yolk Index, Haugh Unit, and yolk color score, while also inhibiting the pH increase indicative of egg spoilage. However, an excessively high concentration (15%) resulted in physical quality degradation. It is concluded that Averrhoa bilimbi L. leaf extract has potential as an effective natural preservative for maintaining the freshness of free-range chicken eggs, with the best concentration at 5% for short- to medium-term storage.

This study explores the intellectual and thematic evolution of green sukuk research within Islamic sustainable finance from 2015 to 2025. Using a hybrid methodological design that integrates the PRISMA-guided Systematic Literature Review with Watase Uake network analysis, the study identifies 17 core Scopus-indexed articles that collectively define the field’s conceptual and empirical development. Results reveal a three-phase evolution: (1) a formative stage emphasizing ethical legitimacy and Sharia compliance; (2) a transitional phase integrating pricing efficiency, market risk, and policy frameworks; and (3) a maturity phase characterized by econometric modeling, behavioral-finance integration, and sustainability governance. Thematic clusters extracted from bibliometric mapping include financial performance and market dynamics, institutional legitimacy and policy frameworks, behavioral intention and investor psychology, and technological innovation and ESG disclosure. Despite methodological advancement, the literature remains geographically concentrated in Malaysia and Indonesia and exhibits theoretical fragmentation across behavioral, financial, and institutional models. Findings highlight key research gaps involving contradictory evidence on yield–risk relationships, inconsistent behavioral determinants of investment intention, and insufficient integration of moderating or mediating mechanisms. The study advances theoretical pluralism by connecting the Theory of Planned Behavior (TPB), Institutional and Legitimacy Theory, and Resource-Based View (RBV) into an integrated model explaining how legitimacy, behavior, and strategic capability jointly drive green sukuk adoption. Policy implications emphasize the need for harmonized regulation, behavioral incentives, and digital transparency to strengthen credibility and accelerate sustainable-finance transformation in line with SDGs 7 and 13.

Antibiotic resistance has emerged as a significant global health challenge, prompting the exploration of alternative antimicrobial agents. This study focuses on the synthesis and antibacterial potential of plant-based nanoparticles, specifically silver nanoparticles AgNPs, synthesized using neem leaf extract Azadirachta indica. The research aims to assess the effectiveness of these green-synthesized nanoparticles against Escherichia coli E. coli), a common pathogen responsible for numerous infections, including those resistant to conventional antibiotics. The synthesis of AgNPs was performed using neem leaf extract as a reducing and stabilizing agent, following a green synthesis approach that is environmentally friendly and avoids harmful chemicals. The synthesized nanoparticles were characterized using UV-Vis spectroscopy, Transmission Electron Microscopy TEM, and X-ray Diffraction XRD, ensuring the particles’ size, shape, and crystalline structure were in the desired range. Antibacterial activity was assessed using the agar diffusion method, comparing the inhibition zones formed by the nanoparticles with those of traditional antibiotics. The findings revealed that the silver nanoparticles displayed significant antibacterial activity against E. coli, with inhibition zones comparable to conventional antibiotics, indicating their potential as an effective alternative in combating antibiotic-resistant bacteria. Moreover, these nanoparticles exhibited high stability and biocompatibility, making them a promising candidate for further biomedical applications. The results suggest that neem-based AgNPs could serve as an eco-friendly solution for addressing antibiotic resistance. Future research is recommended to explore the broad-spectrum activity of these nanoparticles against other bacterial pathogens and to assess their safety and efficacy in clinical settings.

This study aims to analyze the influence of academic achievement, internship experience, competence, and work motivation on the work readiness of Gunadarma University Management students in the Depok region. The research employed a quantitative method using primary data collected through questionnaires. A total of 250 valid responses were obtained using a non-probability sampling technique with purposive sampling. Data analysis was conducted using Smart PLS 4 software with several testing stages, including convergent validity, Average Variance Extracted (AVE), composite reliability, Cronbach’s alpha, discriminant validity, R-square test, predictive relevance (Q²), and hypothesis testing. The results indicate that academic achievement, internship experience, competence, and work motivation significantly influence students’ work readiness, both directly and indirectly. These findings highlight the importance of enhancing practical experience and internal motivation among students to better prepare them for the increasingly competitive job market.

Millions of people suffer from malaria, one of the most serious parasitic diseases that threatens human life and causes high rates of morbidity and mortality, particularly in tropical and subtropical regions. Traditional diagnostic methods, such as blood smear examination, which can be performed using a microscope, face many challenges due to the inaccuracy of manual analysis and the reliance on individual skills. Therefore, the use of machine learning or deep learning algorithms to automate malaria detection offers promising solutions to improve accuracy, reduce diagnosis time, and enhance scalability. In this paper, a multi-class convolutional neural network (CNN)-based model is designed to classify cells infected with Plasmodium falciparum (P. falciparum) and Plasmodium vivax (P. vivax) and uninfected cells from blood smears, as most severe cases and deaths are caused by P. falciparum and P. vivax. This is achieved by building and training a CNN from scratch, rather than using transfer learning from pre-trained models. The proposed network was trained and tested on the Kaggle dataset, which consists of 27,558 images of infected and uninfected individuals. These images were divided into 13,779 images of uninfected individuals, 6,890 images of individuals with P. falciparum malaria, and 6,889 images of individuals with P. vivax malaria. The images were preprocessed using several operations, including blurring, denoising, and morphological processing. The proposed model achieved the best evaluation accuracy when compared with other deep learning algorithms, with an accuracy rate of 96.5%, a sensitivity rate of 95%, a specificity rate of 97.6%, and an F1-score rate of 96.5%. These results demonstrate the effectiveness of the proposed model as a tool to assist clinicians in malaria diagnosis, reducing reliance on manual analysis.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

This study focuses on the weight of broiler chicken carcass components. This study aims to see the extent to which this treatment has an effect on the weight of broiler chicken carcass components. The study used 80 chickens, with feed given CP 11 and CP 12. This study with a Completely Randomized Design includes 4 treatments 5 replications. Treatments are P0 = P0 = Level 0% in 1 L of drinking water, P1 = Level 7.5% in 1 L of drinking water, P2 = Level 15% in 1 L of drinking water. P3 = Level 22.5% in 1 L of drinking water. The study used Analysis of Variance (ANOVA) to see significance (P <0.05) and continued with the Duncan test to see significant differences between treatments. The results of the study statistically provide a significant effect (P <0.05) on the breast weight variable, but not significant (P>0.05) on the thigh weight, wing weight, and back weight variables. It can be concluded that the addition of ginger extract has a positive effect on breast weight, thigh weight, wing weight, and back weight of broiler chickens.

Bajakah stem (Spatholobus littoralis Hassk) is one of the plants often used as traditional medicine. This plant is often used by the people of the interior of Kalimantan as a traditional medicine. The Dayak people know the bajakah stem plant as a medicine to increase stamina and cure various diseases, boiled water from bajakah stems can be used to cure diarrhea, dysentery, aches and pains, wounds, and other diseases and is even considered to cure cancer. This study aims to identify saponin compounds in ethanol extracts and n-hexane fractions of bajakah tampala stems (Spatholobus littoralis Hassk) using the Thin Layer Chromatography (TLC) method. From the results of this study, it can be concluded that the thin layer chromatography profile shows that the ethanol fraction produces seven spots with varying Rf values ​​(0.93; 0.80; 0.77; 0.75; 0.68; 0.63; and 0.56). Meanwhile, the n-hexane fraction only produced three spots with narrower Rf values ​​(0.56, 0.68, and 0.63). The comparison compound, sapogenin, had an Rf value of 0.98. This study concluded that ethanol was more effective than n-hexane in extracting bioactive compounds, especially saponins

The efficiency of investment decisions is one of the core axes in the success of organizations and the sustainability of their business, especially in light of the dynamic and complex business environment. In this context, the integrated role of both accounting and financial management systems is highlighted, as the harmony between them is a key pillar in providing accurate, real-time, and analytical data that supports the investment decision maker and reduces the degree of uncertainty and risks associated with investments. This research aims to analyze the impact of the integration between accounting systems and financial management on the quality and efficiency of investment decisions within institutions, with a focus on the nature of the causal relationship between the two variables. A conceptual model has been built that illustrates the interaction between the financial information generated by the accounting system and the analytical tools provided by the financial department, which contributes to raising the efficiency of strategic decisions related to investment. To achieve the objectives of the study, a descriptive-analytical approach supported by a standard analysis using a simple linear regression model was adopted on field data extracted from an intentional sample of financial officials in the banking and investment sector. The results showed that there is a statistically significant positive effect of the integration of accounting and financial management systems in enhancing the efficiency of investment decisions, as the model showed that integration contributes more than 50% to the explanation of changes in the quality of investment decisions. The study reached a number of important findings, the most prominent of which is that the lack of integration or poor coordination between accounting and financial management leads to delays in decisions or making them based on incomplete or contradictory information. Effective integration enables organizations to allocate resources more efficiently and evaluate investment alternatives in a thoughtful manner. The study concluded with a set of recommendations, most notably the need to develop the digital infrastructure of accounting and financial systems, adopt a unified system for data exchange, enhance the culture of teamwork between accounting and financial management units, in addition to activating the use of predictive financial analysis techniques to raise the level of accuracy in investment decisions.

Catfish dregs fish bone gelatin extraction still has the potential to be utilized by processing it into flour. Catfish dregs fish bone gelatin extraction can be used as an additional source of calcium in mango fruit leather products. This study goals to know the effect of adding catfish bone meal on the chemical also organoleptic characteristics of mango fruit leather. This study used a CRD with a single factor, the addition of catfish bone gelatin waste flour with 5 levels of treatment, including; P0 (control), P1 (2.5 grams), P2 (5 grams), P3 (7.5 grams), also P4 (10 grams). The variables observed included water content, reducing sugar content, vitamin C content, calcium content also organoleptic properties (color, aroma, texture, taste also overall). The results of adding catfish bone flour had a significant effect on fruit leather moisture, vitamin c, also calcium levels (p <0.05). Treatment P4 produced the best results, especially in terms of water content, vitamin C content also calcium content, while P2 was the best treatment in terms of organoleptic parameters. The results of the chemical characteristics with the best treatment had a vitamin C content of 51.3 mg/100, a water content of 12.6% (ww) and a calcium content of 1.82% and from organoleptics an overall score of 3.77.

Trigona sp. propolis simplicia is widely used by the community as a traditional medicine that has many benefits so that this propolis can treat sore throats, flu, and support the immune system. Propolis cultivation is increasingly developing, but there is no scientific data regarding the standardization of raw propolis simplicia. This study aims to determine the standard parameters of Trigona sp. propolis simplicia from Balikpapan. Specific parameters measured on Trigona sp. propolis simplicia include organoleptic tests, identification of chemical compounds, and tests of water-soluble and ethanol extract levels. Non-specific parameters include water content tests, total ash content tests, and acid-insoluble ash content tests. Trigona sp. propolis simplicia in organoleptic tests has a solid form, a distinctive aromatic odor, a blackish brown color, and a slightly bitter sour taste. Trigona sp. propolis simplicia was positively identified as containing compounds of the alkaloid, flavonoid, triterpenoid/steroid, and tannin groups, while saponins were identified negatively. Trigona sp propolis simplicia has ethanol and water soluble extract content values ​​of 10% ± 0.008 and 14.4% ± 0.003 respectively, the water content value obtained is 1.3% ± 0.002, the total ash content and acid insoluble ash content values ​​are 3.67% ± 0.008 and 1.5% ± 0.005 respectively.

Bajakah tampala (Spatholobus littoralis Hassk.) is a medicinal plant that is traditionally used by the Dayak people to overcome various diseases, including indigestion such as diarrhea and dysentery. This plant is known to contain secondary metabolites, including flavonoids, tannins, saponins, alkaloids, and phenolics, which have the potential to have antibacterial activity. This study aims to evaluate the antibacterial activity of pulp extract of patchy bajakah with n-hexane solvent against Escherichia coli ATCC 25922. The extract was obtained through the maceration method and tested using the well diffusion method at concentrations of 10%, 20%, and 30%. The results showed a relatively low yield of the extract (0.10–0.14%) with a reddish-orange color. The inhibition zones formed ranged from 4.28–5.55 mm with significant differences between concentrations (p = 0.000), but the antibacterial activity was relatively low. These findings indicate that the active compounds of the patch stalk are likely to be more polar or semi-polar, so they are less optimally extracted using the nonpolar solvent n-hexane. Therefore, further research with different solvent variations, particularly those that are polar, is urgently needed to uncover the greater antibacterial potential of these plants.

Enteric bacterial infectious diseases such as Escherichia coli are still a serious health problem in many countries, mainly due to the increasing cases of antibiotic resistance that cause the effectiveness of conventional therapies to decline. This condition encourages the search for safer and more effective natural alternatives, one of which is propolis produced by the Trigona sp. bee Trigona sp. Propolis is known to contain a variety of bioactive compounds, including flavonoids and phenolic acid, which act as antimicrobials. This study aims to test the antibacterial activity of propolis extract against E. coli by the sumpray diffusion method. The results of the study showed that there was an inhibition zone that varied according to the concentration of the extract. At a concentration of 10%, an average inhibition zone of 19.85 mm (strong category), a 20% concentration of 22.35 mm (very strong), and a concentration of 30% reaches 26.62 mm (very strong). In comparison, the positive control of ciprofloxacin produced 21.25 mm of resistance, while the negative control (DMSO) showed no activity. ANOVA analysis confirmed significant differences between treatments (p < 0.05). These findings confirm that propolis extract has the potential to be developed as an effective natural antimicrobial agent.

The development of information technology has encouraged institutions, including hospitals, to adopt digital systems to improve operational efficiency. One important aspect is the employee attendance system, which previously relied on fingerprints. This method has limitations, such as difficulty detecting when fingers are not in ideal condition and causing queues during peak hours. This research aims to design and implement an Android-based attendance system using the Eigenface facial recognition method as a faster, safer, and more accurate alternative. Eigenface works by extracting facial features using Principal Component Analysis (PCA), thus being able to efficiently recognize individual identities. The system was developed with MySQL database integration and tested on employees of Khalishah General Hospital. The implementation results showed that the system can recognize faces with a good level of accuracy and increase the effectiveness of attendance recording. Furthermore, the use of facial-based attendance can minimize the potential for fraud and increase user comfort because it does not require physical contact. Thus, the Eigenface method has proven feasible to be implemented as a modern attendance solution to support employee attendance management in hospital work environments and other institutions.

Digital transformation and sustainability demands are driving the need for the integration of E-HRM and Green HRM to achieve sustainable organizational performance. This integration serves as a solution to the challenges of resource efficiency, carbon footprint reduction, and enhanced employee engagement in the digital era. This research aims to analyse the role of E-HRM in enhancing Green HRM practices and its impact on sustainable organizational performance. The research methodology employs a Systematic Literature Review (SLR), utilising the Scopus database, with selection based on inclusion-exclusion criteria, data extraction, and thematic analysis of selected journals. The anticipated impact of E-HRM is its support for Green HRM through the digitalization of HR processes that reduce resource usage, the establishment of digital platforms for environmental awareness, and data-driven impact measurement. The integration of both significantly enhances sustainable performance across three dimensions: environmental (a 20-30% reduction in carbon footprint), economic (15-25% cost savings), and social (30-40% increase in employee engagement). Key implementation factors include technological readiness, management commitment, and alignment with ESG strategies. This research contributes a conceptual framework for the integration of E-HRM and Green HRM, along with practical recommendations for achieving sustainable competitive advantage in the digital age.