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The incidence of degenerative diseases, which are part of Non-Communicable Diseases (NCDs), remains a major cause of death worldwide and in many regions of Indonesia. Meanwhile, efforts to prevent degenerative diseases affecting the adolescent age group are still rare, such as the establishment of a Youth Posyandu, including in Sungai Duren Village. There is no health post that provides youth care services in Sungai Duren Village, while there are many teenagers living there (students under 21 years old) with various lifestyles that are at risk of degenerative diseases. This study aims to analyze the increase in adolescent knowledge about CERDIK between before and after the establishment of the Youth Posyandu in order to prevent degenerative diseases early. This study is a quasi-experimental study involving 30 adolescents by measuring the dependent variable of knowledge. The research instrument used is a questionnaire. Computerized data processing and analysis were conducted through descriptive statistical analysis and T-test analysis. The results showed a significant difference in respondents' knowledge of CERDIK before and after the establishment of the Youth Posyandu in Sungai Duren Village (p=0.000). This study indicates that structured health education can improve adolescents' understanding of CERDIK and can be used as an effective promotive and preventive strategy.

Flower disease detection is a common challenge in modern agriculture. Various factors, such as changes in leaf color, shape, petal structure, and environmental conditions, make it difficult to achieve high accuracy with conventional models. Transfer learning is an effective solution to improve model performance in image detection, especially when available data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The process included data processing, increasing the data volume, model training, and result verification. The results showed that the EfficientNet-B0 model provided the highest accuracy of 97.2%, significantly better than the CNN model created from scratch with an accuracy of 85.1%. This study proves that the transfer learning method is very effective in improving the accuracy of flower disease detection. These results confirm that transfer learning is effective for detecting plant diseases with higher accuracy, especially when the dataset is limited.  

The rise of financial technology (fintech) has significantly reshaped global investment over the last decade. Fintech innovations are increasingly applied in areas such as digital investment platforms, robo-advisors, blockchain-based assets, and cryptocurrency trading. The adoption of fintech in investment continues to grow due to the rising demand for accessibility, transparency, and efficiency in financial markets. Fintech has the potential to democratize investment by lowering entry barriers, expanding financial inclusion, and offering diverse investment instruments for retail investors. Therefore, research on fintech and investment has become an essential topic in recent years. This study uses a qualitative approach with data obtained from the Scopus database, which includes a total of 4,794 articles on fintech and investment published in the last decade (2020–2025). In addition, several software tools such as R Studio, VOSViewer, and Publish or Perish were used for data processing and bibliometric visualization. This study aims to analyze the development of research trends in fintech-driven investment, explore how technology is changing investor behavior, and provide insights for policymakers and practitioners in strengthening a sustainable and inclusive investment ecosystem.

Smart industry has become an important trend in the development of Industry 4.0, especially in promoting the creation of efficient systems in the manufacturing sector. Various countries and studies are encouraging the application of technologies such as IoT, digital twins, artificial intelligence, and smart factories to improve industrial efficiency and sustainability. Therefore, studies related to smart industry are important and necessary especially on the context of smart manufacturing in order to see the direction of future research trends. This study uses a qualitative approach with literature data from the Scopus database covering the period 2020 to 2025. Research trend analysis was conducted through data processing using Bibliometric analysis in R Studio and the VOSviewer applications. To identify the latest research trends regarding smart industry, particularly in the context of Industry 4.0 and smart manufacturing, this analysis can provide a comprehensive picture of future research developments and directions within a global context.

This study aims to design and build a population distribution application in Demak Regency in 2025 using a Geographic Information System (GIS) approach. The study focuses on three main variables: population, population density, and population growth rate per sub-district. The author used the research method of collecting data and references that can later strengthen the results of this study and the application design using the waterfall model. Non-spatial data, namely data in the form of population information, was obtained from the Central Statistics Agency of Demak Regency, while spatial data is data related to regional administrative boundaries. Data processing was carried out using QGIS 2.18 through the stages of joining attributes, classification using the Natural Breaks (Jenks) method, and thematic map creation. The results show that population distribution is uneven. Demak Kota, Karangtengah, and Sayung sub-districts have the highest number and density, while coastal sub-districts such as Wedung and Bonang have low densities. The highest population growth rate is in Karangtengah sub-district at 0.8%. The application of GIS has proven effective in visualizing population distribution and supporting spatial-based regional development planning.  

Flower disease detection is a significant challenge in modern agriculture, particularly with factors such as changes in leaf color, petal shape and structure, and environmental conditions affecting the accuracy of conventional models. These factors make it difficult to achieve optimal results using traditional methods. Transfer learning is an effective solution to improve image detection performance, especially when data is limited. This study used several pre-trained models, namely VGG16, ResNet50, and EfficientNet-B0, to detect three types of flower diseases: black spot on roses, white powdery mildew, and leaf rust. The research process included data processing, increasing the data volume using augmentation techniques, model training, and evaluation of the results. Experimental results showed that the EfficientNet-B0 model produced the highest accuracy of 97.2%, significantly better than the CNN model built from scratch with an accuracy of 85.1%. This study demonstrates that transfer learning is highly effective in improving the accuracy of flower disease detection, making it a more reliable alternative to methods that do not utilize pre-trained models, especially for agricultural applications that require high levels of accuracy in disease detection.

This study aims to analyze the impact of government expenditure development and government budgeting on the social assistance recipients affected by floods. The research approach uses a quantitative method with a total of 50 respondents selected through sampling techniques. Data processing was conducted using SPSS software with various statistical tests, including validity test, reliability test, multiple linear regression, t-test, and F-test. The results show that the development of government expenditure has a significant impact on the condition of the social assistance recipients. Additionally, government budgeting also has a significant impact on the effectiveness of social assistance distribution after the disaster. Simultaneously, both variables have a strong influence on the social assistance recipients, as reflected by the significant F-test value and the coefficient of determination (R Square) of 0.518. This means that 51.8% of the variation in the condition of social assistance recipients can be explained by the development of government expenditure and government budgeting. These findings highlight the importance of appropriate expenditure and budgeting policies to enhance the effectiveness of social assistance distribution, especially in disaster situations.

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL for homomorphic encryption and PySNARK for Zero-Knowledge Proof verification. Experiments are conducted on standard consumer-grade hardware (Intel i5, 8 GB RAM, Ubuntu 22.04) using datasets ranging from 100 MB to 1 GB. The evaluation focuses on encryption and decryption time, homomorphic computation latency, memory usage, and proof generation overhead. Experimental results show that integrating ZKPs introduces a moderate and stable runtime overhead of approximately 15–20%, as analyzed in Section 4, while enabling verification without plaintext disclosure. Ciphertext expansion remains a notable limitation, with observed growth of approximately 30–40× relative to plaintext size, consistent with prior FHE implementations. Despite these overheads, the system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware. Overall, the results indicate that the integrated FHE+ZKP approach provides a practical balance between confidentiality, verifiability, and performance, supporting its applicability to privacy-preserving scenarios such as secure cloud computation, encrypted data analytics, and confidential data processing under realistic resource constraints.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

The Taman Candi is included in the category of active green open space (GOS). Based on this, it is necessary to carry out good maintenance aimed at maintaining the quality of facilities, both softscape and hardscape in the park. Maintenance in Taman Candi Ngawi Regency refers to the park maintenance standards by Arifin and Arifin (2005). Data processing was carried out using a quantitative method, namely using a work capacity calculation formula, aimed at measuring the suitability of work and worker effectiveness from the maintenance of the Taman Candi Ngawi Regency. The management of the Taman Candi in Ngawi Regency does not have a written maintenance schedule, so maintenance activities are carried out flexibly or according to the conditions in the field, and there are several activities that have not been carried out, the results show that there is still work that is not ideal from the work capacity and exceeds the ideal value in accordance with the standard of maintenance work capacity of Arifin and Arifin (2005). This is due to the lack of knowledge of workers about good and correct maintenance activities, the absence of a written schedule from the manager, and the lack of availability of tools.

This study examines the implementation of digital health services via the Mobile JKN application at the Payung Sekaki Community Health Center in Pekanbaru City and explores factors affecting service effectiveness, especially complaints about long waiting times. Using a descriptive qualitative approach based on the Mobile Health Acceptance Model by Handayani et al. (2021), the study focuses on five constructs: ease of use, system availability, system responsiveness, health workers’ digital skills, and user trust. The results show that Mobile JKN has significantly simplified administrative processes, accelerated registration, and improved service efficiency at the health center. Effectiveness is supported by factors such as the application’s general ease of use, faster queue data processing, and adequate digital skills among staff. Users also show a high level of trust, though queue time estimation still needs improvement. Despite these benefits, complaints about long waits persist due to patients who register online but still queue manually and misunderstandings between Mobile JKN and e-Puskesmas queue numbers. Late patient arrivals also contribute to delays. Overall, Mobile JKN proves effective in enhancing digital health services, but further optimization is needed through better socialization of service procedures, accurate queue information, and improved system integration to maximize the advantages of digitalization.

Relational databases store information in interconnected tables and are widely used for data management and retrieval. However, in certain environments, the original values stored in a relational database cannot be exposed during data retrieval. This limitation creates a challenge because common encryption methods only transform data for storage and do not support mathematical operations needed for value matching. Partially Homomorphic Encryption is a cryptographic approach that allows specific mathematical operations to be performed directly on transformed data without restoring it to its original form. This study proposes the use of Partially Homomorphic Encryption to enable value-based data retrieval while keeping all stored values in their transformed form throughout the entire process. The method relies on homomorphic properties that allow mathematical comparison to be conducted on encrypted data, making the retrieval process possible without revealing the original values. The results show that this approach can perform data retrieval operations in a relational database while preserving the transformed structure of the stored data. The proposed method offers an alternative for environments that require data retrieval without exposing original values and demonstrates the potential of homomorphic techniques in supporting secure and functional data processing in relational database contexts.

This study aims to identify the influence of profitability, liquidity, and asset structure on the capital structure of pharmaceutical sub-sector companies listed on the Indonesia Stock Exchange during the 2019–2023 period. The study spanned five years, from 2019 to 2023. Of the total 15 companies in the population, 7 companies were selected as samples using a purposive sampling method. The research data were sourced from annual financial reports accessed through the official IDX website. Data processing was carried out using multiple linear regression methods. Capital structure was measured using two indicators: the Debt to Equity Ratio (DER) and the Debt to Asset Ratio (DAR). The analysis results showed that profitability had no effect on these two capital structure indicators. Conversely, liquidity and asset structure were shown to influence both DER and DAR. This study provides insight into the factors influencing debt financing decisions in pharmaceutical companies and their implications for the company's financial stability.

This study aims to see the effect of Debt to Equity Ratio (DER) and Return on Assets (ROA) on Stock Price in PT. Wilmar Cahaya Indonesia, Tbk which is listed on the IDX during the period 2015–2022. The data used in this study is in the form of the company's annual financial statements obtained through secondary sources. This study uses a quantitative approach with multiple linear regression analysis methods, while data processing is carried out using the SPSS application. The results of the study show that partially the Debt to Equity Ratio (DER) variable has a negative effect on the Share Price, while the Return on Assets (ROA) does not have a positive effect on the company's Share Price. However, the results of the simultaneous test show that DER and ROA together have a positive and significant influence on the Stock Price. These findings provide an idea that the combination of capital structure and profitability remains an important indicator in assessing the performance of a company's shares even though their partial relationships show different tendencies. In addition, this research can be a reference for investors in considering the company's fundamental condition before making investment decisions, as well as provide additional insights for management in managing the capital structure more optimally.

This research aims to determine the effect of learning using the Problem Based Learning model assisted by the Powtoon application on the learning outcomes of Class XI Madrasah Aliyah at the Islamic Education Park (TPI) Sawit Seberang. This research is a type of quantitative research with a Quasy Experimental Design. The research population was 36 students in class XI MIA A and 36 students in class XI MIA B, 36 students in class XI MIA C and 36 students in class, and X MIA C totaling 36 people as the control class. The instrument in this research is in the form of a test in the form of 30 multiple choice questions. Data analysis of student learning outcomes using the t-test formula, also with the help of SPSS version 25 data processing. Data analysis of student learning outcomes with the results of calculating the average learning outcomes shows that the experimental class which uses the Problem Based Learning models is higher than the control class. The prerequisite test is proven that the data is normally distributed and homogeneous. The results of the test calculation were obtained under count worth 9.709 and table worth 1.668 so that it shows tcount > table so Ha2 is accepted. The conclusions in this research explain that there are influences and differences before and after the implementation of the Problem Based Learning model on student learning outcomes in class XI Madrasah Aliyah at the Islamic Education Park (TPI) Sawit Seberang.

The implementation of patient safety culture in hospitals remains a significant challenge, often leading to adverse events. Establishing a strong patient safety culture requires effective interprofessional collaboration among healthcare professionals to deliver patient-centered care. However, factors such as organizational distrust and job dissatisfaction continue to hinder collaborative efforts and negatively affect the quality of care and patient safety outcomes.This study aims to examine the influence of organizational trust and job satisfaction on patient safety culture, with interprofessional collaboration serving as an intervening variable at MP hospital.  This quantitative associative research used a causal approach involving 93 inpatient nurses as respondents. Primary data were obtained through questionnaires using an ordinal scale based on a 4–1 Likert scale. Data processing employed the three-box method, and data analysis was conducted using Structural Equation Modeling (SEM).The results revealed that organizational trust, job satisfaction, and interprofessional collaboration simultaneously and partially influence patient safety culture. Moreover, interprofessional collaboration was found to mediate the relationship between organizational trust, job satisfaction, and patient safety culture.The study concludes that enhancing patient safety culture can be achieved by strengthening organizational trust and job satisfaction through effective interprofessional collaboration. Hospitals should develop supportive systems that foster care and concern among staff, enhance conflict management, improve performance appraisal mechanisms, and promote open, effective communication across all professional groups involved in patient care. These strategies can create a safer, more collaborative, and high-quality healthcare environment

This study aims to see whether financial distress, leverage, and profitability affect accounting conservatism in manufacturing companies in the food and beverage subsector listed on the IDX for the 2021–2024 period. The independent variables used are financial distress, leverage, and profitability, while the bound variables are accounting conservatism. Data processing was carried out using the SPSS version 26 program with multiple linear regression methods. Sampling used purposive sampling techniques with certain criteria so that 63 companies were obtained as a sample for four years of observation (2021–2024). Of the total 252 financial statement data, after the deletion of outlier data, the number of data used became 183. The results of the study show that simultaneously financial distress, leverage, and profitability affect accounting conservatism. Partially, these three variables also have a positive effect on accounting conservatism. In addition, these findings indicate that companies with financial pressures and certain levels of financial management tend to apply higher prudential principles in the preparation of their financial statements.

Poverty is one of the most difficult economic problems to solve. This problem occurs in all countries. Among the causes of poverty are poor human resources, a low quality of life, a rising unemployment rate, and a decrease in job availability each year, as well as wages that do not match living costs. This is not only due to low human resources; the government also plays a crucial role in this issue. The poverty rate is unavoidable, so an appropriate solution is needed to address this issue. One step to reducing poverty is to analyze which economic instruments can be optimized, especially in the Sidoarjo region. The poverty rate in Sidoarjo is quite high. The open unemployment rate in Sidoarjo ranks third in East Java province. Therefore, the author was interested in conducting this research. This study aims to determine the effect of the distribution of zakat, infaq, and alms (ZIS) funds, GRDP, and open unemployment on the poverty rate in Sidoarjo in 2013-2023. This study uses a quantitative method with multiple linear regression analysis. The data processing tool used is SPSS. The results of the T test indicate that the distribution of ZIS funds has a significant effect on the poverty rate, while GRDP and open unemployment do not have a significant effect on the poverty rate partially. The F test shows that the distribution of ZIS funds, inflation, and GDP have a significant effect on the poverty rate simultaneously in the period 2013-2023. The limitation of this study is the use of variables that affect the poverty rate, so that future researchers can add or change these variables with other variables related to poverty.

The usage of blockchain has increased around the world over the years. It has become widely used in various sectors that need transparency, such as accounting, business, and auditing. Blockchains are gaining more popularity after being applied as a system for digital asset ownership, such as cryptocurrency and NFTs (Non-Fungible Tokens). This growing trend of blockchain is followed by the increasing trend of research regarding it in the last decade. Blockchain has the potential to revolutionize the auditing sector and enhance economic accountability due to its decentralized system. Therefore, research regarding blockchain applications in auditing is becoming an important topic. This study adopts a qualitative approach by using datasets retrieved from the Scopus website, from the search result of blockchain auditing, with a total of 1228 articles that were published in the last decade (2015-2025). Furthermore, this study also uses several software programs as data processing tools, such as R Studio, VOSViewer, and Publish or Perish. This study aims to understand the research trend regarding blockchain auditing in the last decade and highlight its implications for the auditing and economic sectors.