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This study aims to analyze and synthesize research findings related to the integration of Buddhist values in the development of interactive learning media. The research employed a qualitative approach using a systematic literature review method on fifty selected articles published between 2018 and 2025. Data were collected through a structured process of identification, screening, and extraction from primary sources consisting of accredited national journals and reputable international journals. The data were analyzed using thematic and comparative synthesis techniques to identify patterns, effectiveness, and research gaps. The results show that interactive learning media based on Buddhist values can enhance students’ motivation, moral understanding, and engagement through the use of technologies such as gamification, educational animation, augmented reality, and mobile applications. The effectiveness of these media is strongly influenced by the alignment between Buddhist ethical principles, instructional design, and the cultural context of learning. Furthermore, the study reveals that the successful integration of spiritual values into digital media depends on educators’ readiness, digital literacy, and technological infrastructure support.

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.

Early mobilization is an important component of postpartum midwifery care to prevent complications, accelerate physical recovery, and improve maternal comfort after delivery. However, not all mothers are able to perform early mobilization optimally due to fatigue, pain, and decreased energy after delivery. Sukari date palm juice (Extractum Phoenix dactylifera) is known to contain simple carbohydrates, minerals, and bioactive compounds that have the potential to increase energy and accelerate maternal recovery. This study aims to analyze the effect of consuming Sukari date palm juice on accelerating early mobilization of postpartum mothers from day 1 to day 3 in the working area of ​​the Sikui Community Health Center (UPT). The study used a quasi-experimental design with a pretest–posttest control group approach. The study sample consisted of 30 postpartum mothers divided into an intervention group (n=15) and a control group (n=15). Early mobilization ability was assessed based on the time the mother was able to sit, stand, and walk. The results showed that postpartum mothers in the intervention group experienced a significant acceleration in early mobilization compared to the control group, particularly in walking ability, with a time difference of up to 8–12 hours faster. The results of the Mann–Whitney statistical test showed a significant effect of date palm juice consumption on the acceleration of early mobilization (p < 0.001). Thus, Sukari date palm juice has been proven to be effective in accelerating early mobilization and can be recommended as a supporting nutritional intervention in postpartum midwifery care.

Regional development faces dynamic challenges amid rapid economic growth driven by natural resource extraction. This study aims to identify leading economic sectors, analyze structural economic transformation, and evaluate the role of these sectors in regional development. The research employs a quantitative method with a descriptive approach. Secondary data consist of Gross Regional Domestic Product (GRDP) at constant prices over the past five years. The analytical techniques applied include Location Quotient analysis to identify base sectors, Shift-Share analysis to assess structural changes as well as comparative and competitive advantages, and Klassen Typology to classify sectoral growth patterns. The results reveal a structural shift from primary sectors, such as agriculture and fisheries, toward secondary sectors, including mining and manufacturing. Despite challenges related to development equity, these leading sectors serve as key drivers of regional economic growth. To maximize the contribution of leading sectors to broader regional development, this study recommends that government policies prioritize the strengthening of intersectoral linkages.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The rapid development of digital technology has increased the intensity of social media use among adolescents, particularly Instagram, leading to a decline in social interaction, low emotional regulation, and an increased tendency toward addictive behavior. Within the context of guidance and counseling, art possesses therapeutic potential, enabling students to express themselves and manage digital behaviors more adaptively. This study is a literature review analyzing seven selected scientific articles regarding the utilization of art in group counseling services. The analysis was conducted through data extraction of article characteristics, including research design, instruments, findings, and implications. The results indicate that art plays a significant role in supporting group dynamics, enhancing emotional regulation, and providing alternative activities capable of diverting students' attention from excessive Instagram use. Structured art activities also facilitate the development of self-awareness and self-control, thereby reducing the frequency of Instagram usage. These findings suggest that art-assisted group counseling is an effective and relevant strategy for guidance and counseling teachers to implement in addressing digital challenges within the school environment.

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.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

The ecological crisis affecting Central Kalimantan reveals systemic environmental degradation, ranging from deforestation and river pollution to peatland destruction caused by massive extractive activities. The impact of this crisis extends beyond ecological damage, disrupting the social, cultural, and spiritual order of the Dayak Indigenous communities. This article aims to reflect on Dayak communal spirituality as a path toward ecological conversion in the light of the encyclical Laudato Si’. Employing a qualitative approach through theological–contextual hermeneutics and library research, this study examines the dialogue between Dayak cosmology, communal life values, and the Catholic Church’s vision of integral ecology. The findings indicate that Dayak communal spirituality affirms a reciprocal relationship among humans, nature, the community, and the Creator, which resonates with the call for ecological conversion articulated in Laudato Si’. This article argues that integrating Dayak communal spirituality into the Church’s pastoral praxis holds transformative potential in fostering ecological awareness, strengthening the inculturation of faith, and encouraging the active participation of the faithful in caring for our common home in a sustainable manner.

In the contemporary digital marketplace, consumers increasingly face diverse product choices and brand communications. Understanding the mechanisms through which product quality and brand perception influence customer loyalty remains critical for competitive advantage. The mediating role of customer trust in this relationship has received limited empirical attention within Indonesian market contexts. This research analyzes the direct and indirect effects of product quality and brand perception on customer loyalty, with customer trust as a mediating variable, using Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology on 103 respondents. A quantitative cross-sectional survey design was employed, collecting data via Likert-scale questionnaires (1-5) with 15 measurement items across four latent constructs: Product Quality (5 items), Brand Perception (4 items), Customer Trust (3 items), and Customer Loyalty (3 items). Data analysis utilized PLS-SEM via SmartPLS 3.0, including assessment of measurement model validity (outer model), structural relationships (inner model), and mediation effects through bootstrapping (5000 iterations). The outer model demonstrated adequate validity with 12 of 15 indicators loading above 0.7, and all constructs meeting composite reliability (CR > 0.7) and average variance extracted (AVE > 0.5) criteria. The inner model revealed that product quality significantly influenced customer trust (β = 0.624, p < 0.001), while brand perception showed no significant direct effect (β = 0.045, p = 0.767). Customer trust strongly predicted loyalty (β = 0.650, p < 0.001). Product quality demonstrated a significant indirect effect on loyalty through trust (β = 0.405, p < 0.001), indicating full mediation. The model explained 43.5% of trust variance and 42.2% of loyalty variance. Product quality emerged as the dominant antecedent of customer trust and loyalty, while brand perception did not significantly contribute. Trust served as the critical mechanism translating quality into loyalty. These findings suggest that companies should prioritize quality assurance and consistent delivery over brand marketing campaigns for sustainable loyalty development. The research contributes to mediation theory in consumer behavior and provides actionable strategic guidance for practitioners in emerging markets.

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.

This study aims to analyze the effect of using cassava (Manihot esculenta crantz) as a carbohydrate-based feed ingredient on body weight gain in beef cattle. The review was conducted using a descriptive literature study approach based on sixteen scientific articles discussing the nutritional composition, processing methods, and performance responses of beef cattle fed cassava-based diets. The analysis shows that cassava contains 17.45–88.6% dry matter, 2.4–21.45% crude protein, and 11.35–92.2% nitrogen-free extract, with variations influenced by plant part, processing method, and hydrocyanic acid (HCN) content. Processing techniques such as fermentation and ensiling can reduce HCN levels by more than 70% while increasing crude protein content up to 25%, thereby improving digestibility and feed efficiency. The inclusion of cassava in the form of flour, dried chips, pulp, or fermented peel consistently enhances dry matter intake and average daily gain (ADG) of beef cattle at inclusion levels of 20–50% in the diet. Overall, cassava has strong potential as a locally available, economical, and sustainable feed ingredient to improve beef cattle productivity.

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.

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.

Diabetic nephropathy is a chronic complication resulting from hyperglycemia, which triggers oxidative stress and inflammation, leading to progressive structural and functional kidney damage. Orange peel and Aloe vera contain bioactive compounds with antioxidant and antifibrotic properties that may protect the kidneys from diabetes induced injury. Nanoemulsion delivery systems can enhance the bioavailability of these extracts in the body. This experimental study aimed to analyze the nephroprotective effects of orange peel and Aloe vera nanoemulsion in a diabetic nephropathy rat model, including glomerular morphology and kidney function. A post-test only control group design was used on Wistar rats divided into five groups: positive control, negative control, and three treatment groups receiving varying nanoemulsion doses. Glomerular structure was evaluated by assessing the number of glomeruli exhibiting synechiae and analyzed using the Kruskal–Wallis test due to non-normal data distribution, yielding p=0.2387 (p>0.05), indicating no significant differences among groups. Urea levels were elevated above normal, whereas creatinine levels remained within normal limits. Although not statistically significant, the treatment groups demonstrated nephroprotective tendencies, shown by improvements in glomerular synechiae in the diabetic nephropathy model.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

Stunting is a chronic nutritional problem characterized by a child’s height being inappropriate for their age, particularly among children under five years old. One of the interventions implemented to prevent stunting is immunization. However, immunization coverage, especially complete basic immunization, has not yet reached the target, including in Medan City. Therefore, this literature study aims to explore the determinants of complete basic immunization coverage in Medan City using the Google Scholar database. The literature search identified nine articles that were extracted and discussed in this study. The determinants of complete basic immunization coverage include individual maternal factors such as age, education level, knowledge, attitudes, and mothers’ beliefs or perceptions toward immunization; social support factors including family support, economic conditions, and prevailing norms and cultural practices within families and communities; as well as health service factors. Comprehensive and integrated interventions are urgently needed to achieve optimal complete basic immunization coverage in Medan City.

In recent years, with the rapid development of artificial intelligence, many innovative changes have been made in the field of intelligent mobile robot development. In the field of control and navigation of mobile robots, learning-based methods have many advantages over traditional ones. The study of mobile robot control methods using deep reinforcement learning is a remarkable area in the development of mobile robots that must operate in dynamic environments. In the previous studies, the proposed robot control algorithms using deep reinforcement learning are mostly based on the given target point and obstacle information, the robot path planning is performed, and the corresponding control is based on the obtained path. The DDPG-based method is a typical example. However, in dynamic environments, DRL based robot path planning requires a state of target point and obstacles information, which leads to a large amount of computation, resulting in extremely long convergence time and even non-convergent cases. In this paper, we propose a new method for mobile robot control in dynamic environment that solves the dimensional problem by extracting the features of the configuration of obstacles using autoencoder and learning the DDPG algorithm based on the obtained features. Simulation results show that the proposed algorithm can effectively solve the mobile robot control problem in dynamic environment.

This study conceptually examines a self-supervised multi-scale fusion framework designed to enhance accuracy and computational efficiency in medical image segmentation, a domain where data scarcity and annotation cost remain major challenges. Traditional supervised approaches are constrained by their reliance on extensive labeled datasets, limiting applicability in real-world clinical environments. Self-supervised learning (SSL) mitigates this issue by extracting supervisory signals directly from unlabeled data, enabling the model to learn rich feature representations without human annotation. Simultaneously, multi-scale fusion architectures integrate global contextual information with fine-grained local features, supporting robust segmentation across varying anatomical structures and image resolutions. Through a qualitative methodology involving library research and content analysis, this study synthesizes state-of-the-art SSL-driven segmentation techniques and highlights how adaptive multi-scale fusion mechanisms address limitations of existing convolutional and transformer-based architectures. The analysis indicates that combining SSL and multi-scale strategies leads to more generalizable, scalable, and computationally efficient segmentation pipelines suitable for diverse medical imaging modalities. The proposed framework represents a promising direction for developing next-generation diagnostic tools capable of handling sparse labels, complex textures, and real-time deployment constraints.