Community service activities with a focus on preventing the practice of money politics among South Kelayan community leaders. Money politics often undermines the integrity of elections and democracy, especially at the local level. In this context, we carry out a series of educational and outreach activities for community leaders to increase their understanding of the negative impacts of money politics and strategies for preventing it. The approach used involves seminars and discussions involving community leaders, youth and political party cadres. The results of this program show increased knowledge and commitment of community leaders in rejecting money politics and playing an active role in promoting clean and fair elections. During program implementation and provide recommendations for more effective money politics prevention efforts in the future. It is hoped that this experience can become a model for other regions facing similar problems, as well as contribute to strengthening local democracy and the integrity of general elections in Indonesia.

This study explores the development and evaluation of a Variational Quantum Algorithm (VQA) for predicting a drug as a corrosion inhibitor, highlighting its advantages over traditional regression models. The VQA leverages quantum-enhanced feature mapping and optimization techniques to capture complex, non-linear relationships within the data. Comparative analysis with AutoRegressive with exogenous inputs (ARX) and Gradient Boosting (GB) models demonstrate the superior performance of VQA across key metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Deviation (MAD). The VQA achieved the lowest RMSE (4.40), MAE (3.33), and MAD (3.17) values, indicating enhanced predictive accuracy and stability. These results underscore the potential of quantum machine learning techniques in advancing predictive modeling capabilities, offering significant improvements in accuracy and consistency over classical methods. The findings suggest that VQA is a promising approach for applications requiring high precision and reliability, paving the way for broader adoption of quantum-enhanced models in material science and beyond.

This study evaluates the performance of Quantum Support Vector Regression (QSVR) in predicting material properties using limited data. Experimental results show that the QSVR model consistently produces superior prediction accuracy compared to previous conventional regression models. This improvement is especially evident in the prediction accuracy for small and complex datasets, where QSVR can better capture non-linear patterns. The superiority of QSVR in processing data with a quantum approach provides great potential in developing predictive models in materials science and computational chemistry.

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.

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.

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.

Memory ability plays an important role in achieving optimal results in cognitive aspects, especially in the context of education which is measured through learning achievement. One of the methods used to hone memory is through memorizing the Qur'an. A study was conducted to examine the effect of the ability to memorize the Qur'an on improving cognitive aspects in tahfidz class students at Bustanul Ulum High School in Central Lampung. The research method used was quantitative involving 33 participants. Data were collected through observation, questionnaires to assess the ability to memorize the Qur'an, and documentation of students' report cards. The results showed that the ability to memorize the Qur'an had a significant effect on improving students' cognitive aspects, with an R Square value of 29.7%. That is, about 29.7% of the improvement in cognitive aspects can be explained by the ability to memorize the Qur'an. Other factors outside the method of memorizing al-Qur'an also contribute to the increase.

The dynamic regulatory landscape presents significant challenges for International Standard Schools (SBI) in Indonesia, impacting their ability to meet global educational standards while integrating local cultural values. This study aims to explore the regulatory challenges faced by SBIs and their implications for educational quality and cultural integration. The method used is library research, which involves reviewing relevant literature and studies on regulations and the implementation of international standard programs. The findings reveal that although SBIs strive to provide high-quality education and facilities in line with international standards, they are often hindered by fluctuating regulations and inadequate financial support. Additionally, the study highlights the importance of a consistent and supportive regulatory framework, sustainable funding sources, and the adoption of an inclusive curriculum that respects local cultural values. These insights offer valuable implications for policymakers, educational institutions, and stakeholders, emphasizing the need for collaborative efforts to enhance the stability and effectiveness of SBIs in Indonesia. By addressing these challenges, SBIs can better fulfill their role in providing internationally competitive education while preserving Indonesia’s rich cultural heritage.

Micro, Small and Medium Enterprises (MSMEs) are the drivers of the Indonesian economy that have great potential to improve the economy and community welfare. The development of MSMEs, which has so far taken the form of training, technical guidance and entrepreneurship workshops attended by ASIPA members and general MSMEs in the ASIPA Community of South Tangerang City, has had a positive effect on several actors, although not all of them have been able to implement what has been given during the training. This research aims to discuss how the Micro, Small and Medium Enterprises (MSME) Development Policy is implemented in the ASIPA South Tangerang Community, using a qualitative approach with descriptive methods. This research analysis uses the theory put forward by Edward III (1980) with four indicators, namely: (1) Communication, (2) Resources, (3) Disposition, (4) Bureaucratic Structure. The data analyzed is the result of interviews, observations and documentation in the ASIPA South Tangerang Community. The results of research on the Implementation of Micro, Small and Medium Enterprises (MSME) Development Policy in the South Tangerang ASIPA Community from four indicators are that (1) Lack of understanding in promotion to improve the business of MSME actors (2) In providing services, guidance and security for MSME actors in terms of quality, there are still administrators and members who lack competence. Apart from that, in terms of quantity, the number of administrators and members is still minimal to carry out improvement activities in innovation (3) There is still a lack of facilities and training activities as well as proposals for security equipment and others, not all of which can be fulfilled because limited budget resources (4) The administrators and members have carried out their duties in accordance with the SOP that has been regulated, however the performance of the administrators and members has not been optimal because there are still administrators and members who carry out concurrent work in other fields.
 
 

Children are part of the young generation who are the successors of the ideals of the nation's struggle and human resources for national development. Currently, violations of the law or criminal acts are not only committed by adults, but criminal acts committed by children are increasingly common. Every child who violates legal rules or commits a criminal act will be subject to sanctions in the form of criminal sanctions. Every child who is 14 (fourteen years old) but not yet 18 (eighteen years old) who is undergoing guidance at a Special Children's Development Institution is called a Foster Child. Every child who has undergone guidance in the LPKA has the right to obtain their rights, especially to obtain conditional release. According to Minister of Law and Human Rights Regulation Number 16 of 2023, conditional release can be granted to children who are serving a prison sentence at LPKA but who have met the requirements. The granting of Parole stages is carried out by the Correctional Center through Community Counselors. Community Counselors play an important role in implementing recommendations for the Parole program, including starting from the initial stages of proposing Parole, making Community Research (Litmas), carrying out guidance to ending guidance at the Correctional Center.