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Lemongrass (Cymbopogon citratus) is a plant known to contain various bioactive compounds with potential antioxidant properties as well as xanthine oxidase (XO) inhibitory activity. This study aimed to evaluate the phytochemical content, antioxidant activity, and XO inhibitory potential of ethanol extracts from the leaves, stems, and their combination. Phytochemical screening revealed that all extracts contained alkaloids, flavonoids, and terpenoids, while phenolic compounds were detected only in the leaf extract and the combined leaf–stem extract. Antioxidant activity assays demonstrated that all extracts exhibited very strong antioxidant activity, with IC₅₀ values below 50 ppm, indicating a significant potential to scavenge free radicals. In the XO inhibition assay at a concentration of 200 ppm, the ethanol extract of lemongrass stems showed the highest inhibitory activity at 81.37%, followed by the leaf extract at 48.08% and the combined leaf–stem extract at 33.65%. Overall, these findings suggest that the ethanol extract of lemongrass stems is the most promising natural source of antioxidants and has the greatest ability to inhibit xanthine oxidase activity, indicating its potential development as a functional ingredient for health applications.

This study comprehensively evaluates the quality of biomass briquettes produced from blends of coconut shell charcoal and sengon sawdust, using both carbonized and non-carbonized materials. Composite briquettes were fabricated with varying compositions and characterized through proximate analysis, calorific value, density, and burning rate measurements to determine their suitability as solid fuel. The results indicate that adding non-carbonized sawdust increases volatile matter content and burning rate but reduces the calorific value of the briquettes. In contrast, incorporating up to 10% carbonized sawdust significantly improves the calorific value to 6119.2 cal/g, approaching that of pure coconut shell charcoal (6352.2 cal/g), while maintaining a relatively high burning rate. Briquettes containing carbonized sawdust also exhibit low ash content, below 3%, and moisture content under 8%, meeting standard solid fuel quality requirements. These findings demonstrate that a strategic combination of carbonized and non-carbonized materials can produce hybrid biomass briquettes with optimized thermal performance, providing a promising, sustainable, and environmentally friendly alternative fuel for domestic and industrial applications.

This study explores the roles of innovation and perseverance as critical success factors for young entrepreneurs in the fried chicken business through an in-depth case study of Fried Chicken Smile, located in Medan Tembung Subdistrict. The research focuses on how young entrepreneurs are able to survive and grow in a highly competitive culinary market dominated by global brands such as KFC. Using a qualitative research approach, data were collected through in-depth interviews with business owners and employees, direct field observations, and documentation analysis. The study is supported by several theoretical perspectives, including Grit Theory, which emphasizes perseverance and passion for long-term goals; Entrepreneurial Behavior Theory, which explains proactive and adaptive business behavior; and Schumpeter’s Innovation Theory, which highlights innovation as a driver of competitive advantage. The findings indicate that business success is influenced by continuous product innovation, particularly through adapting menu flavors to local tastes, maintaining consistent operational quality, and implementing effective digital marketing strategies via social media platforms. In addition, strong social support from family, peers, and local communities plays a significant role in sustaining entrepreneurial motivation. Overall, the study demonstrates that the combination of innovation, persistence, and social capital enhances competitiveness and supports the growth of micro, small, and medium enterprises (MSMEs). These findings provide valuable insights for the development of young entrepreneurship in Indonesia’s dynamic culinary sector.

This study aims to analyze the impact of business combinations through acquisition on the financial performance of PT Sarana Menara Nusantara Tbk. The research employs a descriptive quantitative approach, focusing on the acquiring firm in the Indonesian telecommunications infrastructure sector. The data used are secondary data obtained from the company’s annual financial statements for the period 2019–2023, sourced from the Indonesia Stock Exchange and the company’s official website. Financial performance is analyzed using Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), and Debt to Equity Ratio (DER) by comparing the periods before, during, and after the acquisition conducted in 2021. The results indicate that the acquisition exerted short-term pressure on asset efficiency and profitability, as reflected by the decline in ROA and NPM in the year of acquisition. However, in the post-acquisition period, the company demonstrated an improvement in operational performance, particularly in Net Profit Margin, suggesting that the economic benefits of the business combination gradually materialized. Meanwhile, fluctuations in ROE and DER reflect adjustments in the capital structure following the acquisition. These findings suggest that the success of an acquisition cannot be evaluated solely based on short-term financial performance but requires continuous assessment to capture its medium- and long-term effects. This study provides practical implications for management in formulating post-acquisition integration strategies and contributes empirically to the accounting and finance literature on business combinations in Indonesia.

Significant changes occurring today are characterized by technological disruption, demographic shifts, globalization, and economic instability. The combination of these factors creates complex challenges for human resources (HR) in adapting and enhancing their capabilities. The purpose of this study was to examine the relationship between organizational commitment and perceptions of transformational leadership style with Organizational Citizenship Behavior of teachers in Surabaya. The research subjects were 397 teachers in Surabaya, East Java. The sampling technique used was purposive sampling. Data was collected through scale filling using Google Forms. The measurement instruments in this study were the Organizational Citizenship Behavior Scale, the Organizational Commitment Scale, and the Transformational Leadership Style Perception Scale. The data analysis used multiple linear regression analysis. The results showed that there was a significant positive relationship between organizational commitment and perceptions of transformational leadership style with Organizational Citizenship Behavior. The higher the organizational commitment and perceptions of transformational leadership style, the higher the Organizational Citizenship Behavior among teachers. There was a significant relationship between organizational commitment and Organizational Citizenship Behavior, and there was also a significant positive relationship between perceptions of transformational leadership style and Organizational Citizenship Behavior. The higher the perceptions of transformational leadership style, the higher the Organizational Citizenship Behavior among teachers.

Problem solving in permutation and combination requires the ability to understand context, choose strategies, and perform calculation procedures accurately. Based on the analysis of students' answers, it was found that difficulties arose consistently at each stage of problem solving according to Polya, namely the problem understanding stage, the planning stage, the plan implementation stage, and the rechecking stage. In general, students' weaknesses are not only related to their understanding of permutation and combination concepts, but also to their inability to apply problem-solving steps systematically. The results of the study indicate the need for a learning approach that not only focuses on mastering formulas, but also strengthens problem literacy, the ability to identify relevant information, and the selection of solution strategies appropriate to the characteristics of the problem. In addition, the habit of reflection through reviewing the process and results of the solution needs to be developed consistently so that students are able to recognize mistakes and improve their accuracy in solving permutation and combination word problems in a more accurate, logical, and structured manner.

ARP spoofing attacks are a serious threat to network security, particularly in vulnerable Internet of Things (IoT) environments. This final project aims to detect ARP spoofing attacks on IoT net-works using a combination of Random Forest (RF) and Robust PCA methods. RF is chosen for its classification capabilities and handling of non-linear data, while Robust PCA is used for di-mensionality reduction and handling outliers in the data. The dataset used is "MITMArpSpoof-ing.pcap.csv," which contains network traffic data. The data is processed by performing prepro-cessing, feature scaling, and converting labels to binary (0 for benign, 1 for ARP spoofing). Subsequently, Robust PCA is applied to reduce data dimensions, and then the data is trained using the RF model. The test results show that the RF model with Robust PCA achieves an accu-racy of 96.02% in detecting ARP spoofing attacks. This method has proven effective in identify-ing and classifying ARP spoofing attacks on IoT networks.

Jambi Province is one of the largest natural rubber producing regions in Indonesia; however, rubber factories under GAPKINDO Jambi still face productivity issues, particularly the gap between production capacity and actual output, and productivity assessment that is still conducted manually by GAPKINDO Jambi. This study employs Decision Tree, Random Forest, KNN, and SVM algorithms within a structured pipeline involving preprocessing, feature selection, standardization, data balancing using SMOTE, and hyperparameter tuning. The proposed solution applies productivity level classification both individually and through paired combinations (ensemble voting). The results show that the Decision Tree + Random Forest model achieves the best performance with an accuracy of 0.84 and an F1-score of 0.83, confirming the effectiveness of ensemble methods in supporting productivity improvement decisions.

The rapid development of the Internet of Vehicles (IoV) has introduced new security challenges, particularly in protecting Controller Area Network (CAN Bus) communications from cyberattacks such as Denial of Service (DoS) and spoofing attacks. This study proposes the implementation of the Extreme Gradient Boosting (XGBoost) algorithm combined with Information Gain feature selection to improve intrusion detection performance in IoV environments. The CICIoV2024 dataset, which represents both benign and malicious traffic, is used as the primary data source. The research process includes data integration, preprocessing, feature selection, data splitting, and model training using a 5-fold cross-validation approach. Experimental results demonstrate that the proposed model achieves outstanding performance, with accuracy, precision, recall, and F1-score exceeding 99.99%, and an Area Under Curve (AUC) value approaching 1.00. Furthermore, Information Gain successfully identifies the most influential CAN payload features, enhancing model efficiency without sacrificing accuracy. These findings confirm that the combination of Information Gain and XGBoost is highly effective for developing a fast, accurate, and efficient intrusion detection system in IoV networks.

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.

Global climate change has prompted analysis of emission control policies, including carbon trading mechanisms and carbon taxes, to achieve a low-carbon economy. This study utilizes a bibliometric approach (2020–2025) with the Scopus database, which is processed using the RStudio application with Bibliometrix and Biblioshiny analysis and also uses the VOSviewer application, to identify publication trends and the effectiveness of emissions policies in different economic contexts, such as G20 countries or developed countries with developing countries. Thematic analysis results show dominant topics such as emissions trading, carbon capture, renewable energy, and a focus on economic development and technological innovation. The reviewed literature confirms that higher carbon tax rates are more effective in reducing emissions in G20 countries, while in developing countries, the implementation of a combination of strict carbon taxes and emissions trading mechanisms can promote substantial decarbonization. Overall, this bibliometric study confirms the growing research interest in green economy solutions and supports the urgency of adaptive, evidence-based emissions policies in various economic contexts.

Automatic motorcycle spare part sales have increased along with the high use of automatic two-wheeled vehicles in the community. To support optimal sales strategies and stock management, customer purchasing pattern analysis is required. This study uses the FP-Growth algorithm to identify association patterns between automatic motorcycle spare part products that are frequently purchased together. FP-Growth was chosen because of its ability to efficiently find frequent itemsets without the need to generate candidate itemsets as in the Apriori algorithm. Transaction data is processed to form an FP-Tree which is then extracted to find relationships between items. The analysis results show combinations of products that frequently appear together, such as brake pads and engine oil, which can be used as a basis for compiling sales packages, product placement, and product recommendations. By implementing the FP-Growth algorithm, spare part stores or workshops can improve service and efficiency in sales management.

Overburden stripping operations in the coal mining industry require optimal performance of loading and hauling equipment to achieve production efficiency. This study aims to evaluate the performance of loading and hauling equipment using the Match Factor method in overburden stripping operations at PT Bumi Artlantis Raya. The results indicate that the equipment combination achieved a Match Factor of 0.85, reflecting moderate compatibility with a potential efficiency improvement of 15%. The actual productivity of Excavator 4002 reached 137.02 bcm/hour (91.35% of the 150 bcm/hour target), while Excavator 4004 exceeded the target with a productivity of 195.73 bcm/hour (130.49% of the target). In contrast, dump truck productivity remained relatively low (Mercedes dump truck: 35.58 bcm/hour; Hino dump truck: 35.40 bcm/hour), primarily due to waiting time during loading and disposal activities. Statistical analysis reveals a strong negative correlation between cycle time and productivity (R² = 0.9929). The optimal cycle time to achieve a Match Factor of 0.80 is 969 seconds, corresponding to an optimal hauling distance of 5.38–6.725 km. Although mechanical availability and physical availability were high (94–100%), the use of availability and effective utilization were relatively low due to an imbalance between loading and hauling equipment. This study concludes that improving equipment coordination, increasing bucket fill factor, enhancing haul road conditions, and implementing preventive maintenance are essential to achieving more optimal operational efficiency in overburden stripping activities.

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.

Abortion remains a complex reproductive health issue due to the interplay of multiple interrelated determinants. This study aimed to map the factors influencing the incidence of abortion based on recent scientific evidence. A literature review design was applied by analyzing ten selected articles published within the last five years and retrieved from major scientific databases. The selection process was conducted systematically through title, abstract, and full-text screening based on predefined inclusion criteria. The extracted data included study characteristics, type of abortion, examined determinants, and key conclusions. The synthesized findings indicate that abortion incidence is shaped by a combination of biological, social, and healthcare system related factors. Clinical determinants such as maternal age, endocrine disorders, uterine anatomical abnormalities, obstetric history, anemia, and hypertension play a substantial role in spontaneous and recurrent miscarriage. In contrast, structural factors including income level, contraceptive access, and legal regulations predominantly influence induced abortion. The discussion highlights that abortion should not be viewed as an isolated clinical event, but rather as the cumulative outcome of risks operating across multiple levels of influence. In conclusion, abortion represents a multifactorial phenomenon that requires comprehensive prevention strategies extending beyond medical interventions alone. These strategies should also address healthcare accessibility and broader social conditions. This review contributes to a deeper understanding of the complexity of abortion determinants and provides an evidence-based reference for developing more effective preventive approaches in the future.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

This study examines the Sufistic dimension in the syafahî interpretation of Habib Muhammad Luthfi bin Yahya regarding the verses of nationality in the Qur’an. Habib Luthfi was chosen as the object because he is a Sufi scholar as well as a national figure who consistently conveys Qur’anic interpretation through oral media, and his ideas demonstrate a combination of Sufi spirituality and nationalism. This study employs a qualitative method with primary data sources in the form of recorded lectures, religious gatherings, and direct interviews, as well as secondary data consisting of scientific literature related to tafsir, Sufism, and nationality. The analysis is carried out through a descriptive-analytical-comparative approach by paying attention to the methodology of tafsir and its Sufistic context. The results of the study indicate that the syafahî interpretation of Habib Luthfi is characterized as Sufistic-thematic with an approach of tafsir bi al-isyâri and bi al-ra’yi al-maqbûl which still rests on the framework of tafsir bi al-ma’tsûr. His interpretation of six verses of nationality emphasizes the value of love for the homeland, obedience to leaders, jihad in social-moral dimensions, the unity of the ummah, collective gratitude in national development, and respect for diversity as part of sunnatullah. Thus, this study affirms that Sufism can function as an ethical foundation of nationality as well as a model of Qur’anic interpretation that is relevant to contemporary Indonesian reality.

This paper aims to examine the relationship between Social Learning Theory and the principles of Christian education, particularly in character formation and the discipleship process. Social Learning Theory asserts that human behavior is shaped through observation, imitation, and social interaction, thus role models and the environment play a central role in the learning process. This understanding aligns with Christian education, which emphasizes that faith and character are developed through relationships, life experiences, and role models, as demonstrated in God's relationship with Israel, the life of Jesus Christ, and the practices of the early church. The integration of these two approaches demonstrates that effective Christian character formation requires a combination of modeling, mentoring, and a supportive community, along with the spiritually renewing work of the Holy Spirit. Thus, modern psychological theory and the principles of faith education can complement each other to create a holistic approach to Christian education, whether in the church, school, or family.

Prelabor rupture of membranes (PROM) remains one of the most frequently encountered obstetric complications and continues to contribute substantially to maternal and neonatal morbidity and mortality. Numerous factors have been associated with PROM; however, the available evidence presents considerable variation, highlighting the need to re-evaluate these determinants using recent empirical findings. This review aims to identify and synthesize the principal factors influencing PROM based on ten studies published within the last five years. A systematic search was conducted across PubMed, Scopus, Web of Science, and Google Scholar using predefined keywords, followed by screening through established eligibility criteria. Relevant data from each study including study design, geographical setting, assessed risk factors, and statistical outcomes such as odds ratios or relative risks  were extracted and compared. The review indicates that reproductive and urinary tract infections, inadequate nutritional status, high-risk obstetric history, and short interpregnancy intervals consistently emerge as major determinants. Social factors and the quality of antenatal services were also shown to heighten the likelihood of PROM. Overall, the findings emphasize that PROM arises from a combination of medical and non-medical influences, reinforcing the need for preventive strategies that adopt a comprehensive and integrated approach.

This study aims to analyse the relationship between leadership communication style and the effectiveness of decision-making in the Department of Tourism and Culture of Sikka Regency. The research emphasises the importance of open, clear, and participatory communication in supporting organisational coordination and improving the quality of decisions. Using a descriptive method, data were obtained through interviews with the Head of Department and a staff member involved in communication and policy implementation. The findings show that the leader applies a combination of democratic communication through the local Kula Babong approach and situational authoritarian communication. The democratic style enhances information flow, strengthens employee involvement, and increases acceptance of decisions. Meanwhile, the authoritarian style ensures discipline, clarity, and timely execution when firm direction is required. Staff members perceive the leader’s communication as polite, direct, and easy to understand, contributing to motivation and readiness to act. Overall, leadership communication significantly influences decision effectiveness across organisational contexts and growth.