This research investigates the application of accounting financial statements in micro-enterprises, specifically focusing on home-based rempeyek businesses in Desa Laden, Pamekasan. Employing qualitative descriptive methods and interviews, the study explores the obstacles and advantages of implementing accounting systems in UMKM. The findings reveal a lack of awareness among business owners regarding financial statements, resulting in underutilization of financial reports for business administration. There is a call for governmental initiatives to provide training and guidance aimed at enhancing UMKM development in Pamekasan.
 

This study aims to evaluate the effectiveness of the implementation of edupreneurship through Business Center, Mini Bank, and MPLB Payment at SMK Negeri 2 Palangka Raya. Edupreneurship combines the concepts of education and entrepreneurship to instill entrepreneurial attitudes and competencies in students. With a descriptive qualitative method, this study collects data through interviews, observations, and document analysis to understand the impact of these programs on students' competence and work readiness. The results of the study show that these programs have succeeded in fostering students' entrepreneurial spirit by providing practical experiences that are relevant to the business world. The Business Center helps students understand business management, marketing, and finance. Mini Bank introduces students to good financial management, while MPLB Payment introduces the latest financial technology. The teaching factory engages students in work practices that resemble a real industrial environment, with a strategy of local industry cooperation and internship programs.

Security is a crucial aspect in the digital age, especially in the management and protection of information. As the volume of information processed increases, the need to organize knowledge and provide adequate security becomes more pressing. This research emphasizes the importance of cybersecurity in the context of digital libraries, which must comply with certain technological and regulatory standards to protect user data and guarantee privacy when accessing electronic resources. Libraries face various challenges in protecting personal data on their electronic resources. This research addresses topics such as user privacy, data encryption, access management, and compliance with privacy laws. By addressing these issues comprehensively, libraries can ensure the protection of user privacy while optimizing the benefits of digital resources in today's information environment. The October 2023 cyberattack by a hacker group known as Rhysida on the British Library's internet information system emphasizes the importance of cybersecurity and data privacy for digital libraries. This research aims to provide insights and solutions to address these challenges, ensuring digital libraries can operate securely and efficiently.

This research was conducted to determine the effects of religiosity, taxpayer morale, and the tax system of online business actors on tax evasion. This research uses a quantitative method as a research method. The research data used is primary data, with research instruments in the form of questionnaires. The population used in this research was 252,953 MSMEs in the East Jakarta area, based on data from the Central Statistics Agency. The sample used in this research was 100 MSME actors. The data analysis method used was multiple linear analysis with the help of the SPSS version 22 program. Based on the results of the analysis conducted, it can be concluded that religiosity and the tax system have a significant effect on tax evasion, while tax morale has no effect on tax evasion.
 

In this study, we evaluate the performance of various machine learning models, including Random Forest (RF), Bagging (BAG), AdaBoost (ADA), Artificial Neural Network (ANN), and Support Vector Machine (SVM), using metrics such as R², Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results indicate that AdaBoost (ADA) achieves the highest performance with an R² of 0.999, RMSE of 2.32, and MAE of 2.24, making it the most accurate model with the smallest prediction errors. Bagging (BAG) also performs exceptionally well, with an R2 of 0.996, RMSE of 3.09, and MAE of 2.92. The Artificial Neural Network (ANN) exhibits a high R2 of 0.999, though RMSE and MAE values are not provided. Random Forest (RF) and Support Vector Machine (SVM) show good performance with R² values of 0.982 and 0.970, respectively, but are outperformed by the ensemble methods. The findings underscore the superiority of ensemble techniques, particularly AdaBoost, in achieving high predictive accuracy and minimal errors in this context.

In this study, we compare the performance of the XGBoost model with a Support Vector Machine (SVM) model from the literature in predicting a given task. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were utilized to evaluate and compare the models. The XGBoost model achieved an R² of 0.99, an RMSE of 2.54, and an MAE of 1.96, significantly outperforming the SVM model, which recorded an R² of 0.96 and an RMSE of 6.79. The scatter plot for the XGBoost model further illustrated its superior performance, showing a tight clustering of points around the ideal line (y = x), indicating high accuracy and low prediction errors. These findings suggest that the XGBoost model is highly effective for the given prediction task, likely due to its ability to capture complex patterns and interactions within the data.

The aim of this research is to analyze commodities in the food crop subsector and horticultural crop subsector which have the potential to be developed in order to support the economy in East Kalimantan Province. Analyzing food crop subsector commodities and horticultural crop subsectors that have competitiveness to be developed in order to support the economy in East Kalimantan Province. Analyzing the shift and role of commodities in the food crop subsector and horticultural crop subsector in the economy in East Kalimantan Province. Analyzing economic development planning strategies that can be carried out in order to develop potential and competitive commodities in the food crop subsector and horticultural crop subsector to support the economy in East Kalimantan Province.In this research, an analysis was carried out to determine potential economic sectors using the Klassen typology analysis tool, then an analysis was carried out to explain competitive sectors and shifts in economic structure using the Shift Share analysis tool, then a SWOT analysis was carried out.The results of this research are food crop subsector commodities and horticultural crop subsectors that have the potential to be developed in order to support the economy in East Kalimantan Province are lowland rice, shallots and large chilies. Commodities in the food crop subsector and horticultural crop subsector that have competitiveness to be developed in order to support the economy in East Kalimantan Province are lowland rice, corn, bananas, shallots and large chilies. The economic development planning strategy that can be carried out in order to develop potential and competitive sectors in the food crop sub-sector and horticultural crop sub-sector in order to support the economy in East Kalimantan Province is to implement short-term, medium-term and long-term plans.

This study aims to analyze the learning difficulties of fourth grade students on flat building material at SD 1 Gondosari, Gebog, Kudus Regency. The method used is qualitative with a case study approach. Data were obtained through questionnaires and interviews involving students and teachers as research subjects. The results showed that students' learning difficulties were influenced by internal and external factors. Internal factors include negative attitudes towards mathematics, lack of understanding of concepts, difficulties in numeracy skills, and problem-solving ability. External factors include less than optimal use of learning media and lack of attention from parents. The findings show that students' main difficulties are in understanding the concept of the formula, calculating the square area, and solving varied problems. Therefore, it is important for teachers to understand students' development and use innovative learning media and involve parents in their children's learning process.

Mango (Mangifera indica L.) is a fruit that is loved by people all over the world. This research aims to determine the morphological characteristics of mango fruit in Panyileukan District, West Java, Indonesia. This research was conducted in four villages, namely Mekar Mulya, Cipadung Kidul, Cipadung Wetan and Cipadung Kulon. This research aims to determine the morphological characters of mangoes in Panyileukan District. Field data collection was carried out by observing the mango varieties planted by the community in their home gardens. A total of eight varieties of mango were found at the research location: mango gedong, garifta, arumanis, cengkir, kaweni, grogot, simanalagi and apple. Each mango variety has different morphological characteristics. Botanical identification was carried out by comparing the morphological characteristics of mango trees using several publications, including journals, magazines and websites. There were 8 types of mango, namely; gedong, garifta, arumanis, cengkir, keweni, grogot, simanalagi and apple

Quantum computing has emerged as a promising technology capable of solving complex computational problems more efficiently than classical computers. Among the various quantum algorithms developed, the Quantum Support Vector Machine (QSVM) has gained significant attention for its potential to enhance machine learning tasks, particularly classification. This review paper explores the theoretical foundations, methodologies, and potential advantages of QSVM for classification tasks. We discuss the quantum computing principles underpinning QSVM, compare them with classical support vector machines, and review recent advancements and applications. Finally, we highlight the challenges and prospects of QSVM in the context of quantum machine learning.