Publication Search

The advancement of technology has brought significant changes in the banking sector, posing new challenges related to the protection of customers' personal data. Data breaches can harm customers and undermine public trust in financial institutions. To address disputes related to data breaches, the Financial Services Sector Alternative Dispute Resolution Institution (LAPS SJK) becomes a relevant choice. This study uses a normative legal approach to analyze the legal framework and dispute resolution procedures applied by LAPS SJK. The results show that LAPS SJK prioritizes the principles of accessibility, independence, fairness, efficiency, and effectiveness in dispute resolution. The methods of dispute resolution offered include mediation, arbitration, and binding opinions. Through LAPS SJK, customers can obtain fair, fast, and affordable dispute resolution without having to go through conventional judicial processes. Therefore, LAPS SJK plays a crucial role in protecting customers' rights and maintaining public trust in the banking sector.    

The purpose of this journal article is to provide research findings regarding the application of automated production control system theory to the construction industry. from expediting work processes. The method used in this research is to observe the production process of a Primalland light steel construction company in real time. Please note, raw materials can be obtained from literature studies, with a focus on their implementation in industrial processes. There are many challenges in the development sector in Indonesia, both in terms of quality and quantity, PT. PrimaLand Light Steel Frame was founded on December 25 2008 in Pandeglang. Based on the research that has been carried out, the data obtained from this research is then analyzed in detail to determine whether there is a significant difference between price changes that affect sales volume and price changes that affect sales volume. Research on sales volume at PT PrimaLand Pandeglang between 2014 - 2016. PrimaLand light steel tries as best as possible to maintain the quality of the roof truss profile produced by using high quality steel coils, or what can be called High Tension, namely with a yield strength range of Mega Pascal 400 Mpa – 500 Mpa and generally standard G550, or reaching 2x the strength from conventional melted steel which is more than 240 Mpa.

Standardization of the presentation of accounting information is necessary because many parties (stakeholders) have an interest in the information presented in the company's financial reports. Therefore, the process of presenting financial reports must be based on applicable accounting standards so that the financial information presented provides information that can be understood, relevant, reliable, can be compared in evaluating the financial position and performance of the bank and is useful for users of financial reports (external parties and internal) in economic decision making. Whether conventional (non-shariah) financial institutions or sharia financial institutions, the financial performance of each institution will be visible from the financial reports. Financial reports are media used to conduct research on the health condition of a company consisting of balance sheets, profit and loss calculations, retained earnings and other reports.

The purpose of this research is to 1) find out whether there is a relationship between the Spirituality of PAK Teachers and the Christian Character Formation of Teb State Middle School Students, 2) find out whether there is a relationship between Bible Study and the Christian Character Formation of Tebo State Middle School Students, 3) to find out whether there is a relationship between the Spirituality of PAK Teachers and Bible Study on the Formation of the Christian Character of Shiva SMP N Tebo. The research hypothesis is: "There is a significant relationship between PAK Teacher Spirituality and Bible Study on the Formation of Christian Character of N Tebo Middle School Students." The population is students of class VIII and IX of SMP N 3 Tebo, SMP N 30 Tebo, SMP N 31 Tebo totaling 290 people who are Christians. The research sample was determined using a purposive sampling technique, namely 36 students from class VIII and IX of SMP N Tebo as an experimental class using Religious Education and Bible Study Teacher Spirituality and 36 students from class XI MIPA 3 as a control class using normal treatment (conventional class). The instrument used in this research was a 50-item questionnaire. Research data for the questionnaire was analyzed using the average difference test formula (N-Gain Score). It was found that the average value for the experimental class was 82.4369 in the effective category. And the average value for the control class is 22.9433, which is in the ineffective category. Then carrying out an independent test using the t test obtained a value of t = 20.376> t table (α = 0.05; df = 70) = 2,000. Thus, it can be concluded that the research hypothesis is accepted, namely that there is a relationship between the Spirituality of Religious Education Teachers and Bible Study on the Formation of Christian Character in N Tebo Middle School Students, and H0 is rejected.    

The company began to develop the sharia financial industry, namely corporate Sukuk, which began to experience quite good growth in Indonesia. This is proven by the existence of companies that have issued sukuk. Compared to government sukuk or conventional bonds, corporate sukuk is still relatively slow, but its growth is increasing every year. In this research, the variables that can influence economic activity are the inflation rate, interest rate, Rupiah exchange rate (USD), and industrial production index. This research aims to analyze the growth of corporate sukuk in Indonesia. The research method used is a quantitative method with a descriptive approach. With data analysis techniques using multiple linear regression models. The research results show that the inflation rate and Rupiah exchange rate (USD) influence the growth of corporate sukuk in Indonesia. The results of the research on Interest Rates and the Industrial Production Index show that the results have no effect on Sukuk Growth in Indonesia. Meanwhile, the research results simultaneously show that the variables of inflation rate, interest rate, rupiah exchange rate (USD), and industrial production index influence the growth of sukuk in Indonesia.    

Online loans are financial assistance from financial institutions that is done online, which is one proof of the advancement of financial technology (fintech). Online lending or peer to peer lending as a form of financial technology (fintech) is a technological advancement that offers loans with easier and more flexible terms and conditions. The theory used in this research is the theory of legal legality. The theory of legal legality is very relevant to the research made here, because there needs to be legal legality of peer to peer lending in online loans in conventional law considering that the payment system in this illegal online lending and borrowing service has troubled many people, such as intimidative billing, dissemination of personal data, fraud, and sexual harassment through electronic media. The research method used is qualitative descriptive research. The data collection technique or instrument used is library research by studying various books as literature, official documents, laws and regulations, results of previous research, and other literature sources related to the problems studied. The legality of fintech peer to peer lending online loans in the aspect of conventional law is found in the Financial Services Authority Regulation Number 77/POJK.01/2016 concerning Information Technology-Based Money Lending and Borrowing Services, Law Number: 8 of 1999 concerning Protection of Consumer Protection and Law Number: 11 of 2008 concerning Information and Electronic Transactions and other applicable regulations.

Financial statements are closely related to the field of accounting. Financial statements prepared by a company can be used as a tool for making internal managerial decisions as well as for external parties of the company. A company's financial statements can be analyzed using several financial ratios. Financial statements (Financial Statment) is the end product of a series of processes for recording and summarizing business transaction data, accountants are expected to be able to organize all accounting data so as to produce financial statements and even must be able to integrate and analyze the financial reports they make. Even the benchmark for whether or not a financial institution or company is healthy is seen from the financial statements and this will affect the decisions of external parties in terms of investment or others, in this case the author presents two financial statement materials as a comparison, namely conventional and sharia financial statements, where both have similarities and differences in many ways.

Pronunciation skills are fundamental in English language learning, especially in the correct articulation of regular past tense verbs. However, students often experience difficulties in accurately pronouncing such verbs due to the differences between English spelling and pronunciation. To address this issue, this study investigated the effect of the English Language Speech Assistant (ELSA) application on the pronunciation ability of tenth-grade students at SMA Negeri 2 Pematang Siantar. This research utilized a quantitative approach with a quasi-experimental design, involving 64 students divided equally into an experimental group (X-3) and a control group (X-9) selected through purposive sampling. The experimental group received instruction using the ELSA Speak application, while the control group was taught using conventional teaching methods. Data were collected through pre-tests and post-tests, then analysed using an independent sample t-test. The mean pre-test scores for the experimental and control groups were 50.34 and 50.21, respectively, while the mean post-test scores were 82.56 and 67.37. The t-test analysis revealed a significance value (2-tailed) of 0.000, which was lower than the significance level of 0.05 (0.000 < 0.05). Additionally, the computed t-value exceeded the t-table value (t-observed > t-table at df=62 and α=0.05), leading to the acceptance of the alternative hypothesis (Ha) and the rejection of the null hypothesis (H₀). This indicates that there is a significant difference between students taught using the ELSA Speak application and those taught using conventional methods. Based on the research findings, it can be concluded that the ELSA Speak application significantly improves students’ pronunciation abilities in pronouncing English regular past tense verbs.

This paper presents a comparative analysis of various battery management systems (BMS) in electric vehicles, with a focus on incorporating machine learning techniques to improve battery safety and extend battery life. The study evaluates conventional BMS against machine learning-enhanced models in predicting thermal runaway, state of charge (SOC), and state of health (SOH) under diverse operating conditions. Results indicate that machine learning algorithms outperform conventional methods, providing more accurate SOC and SOH estimations, thus enhancing vehicle safety and longevity.

This paper explores the use of genetic algorithms (GAs) for optimizing nonlinear systems in resource allocation. By simulating various allocation scenarios, we demonstrate the efficiency of GAs in finding near-optimal solutions in complex environments. The study provides a comparison of GA performance against traditional optimization methods and identifies scenarios where GAs outperform. The results emphasize the utility of GAs in real-world applications, especially when conventional approaches struggle with large solution spaces.

The rapid development of smart city initiatives has significantly increased the adoption of Internet of Things (IoT) technologies to enhance urban services, infrastructure efficiency, and quality of life. However, the large-scale deployment of interconnected IoT devices also introduces critical cybersecurity challenges, including unauthorized access, data breaches, and system vulnerabilities. This study aims to develop an integrated IoT security management model to improve cybersecurity resilience in smart city environments. The research adopts a Design Science Research (DSR) approach, which involves problem identification, literature analysis, model design, implementation, and evaluation. The proposed model incorporates key security components such as Identity and Access Management (IAM), device authentication, secure communication through encryption, firmware and patch management, and continuous monitoring with intrusion detection mechanisms. The model is evaluated through simulation in smart city scenarios, including transportation systems, environmental monitoring, and energy management. The results demonstrate significant improvements in security performance, with increases in threat detection rate, vulnerability reduction, access control effectiveness, and system stability under attack conditions. Quantitative analysis shows improvements of up to 37% compared to conventional approaches, indicating the effectiveness of the proposed model in mitigating IoT-related cybersecurity risks. This study contributes by providing a comprehensive and scalable framework for IoT device security management, which can be applied to enhance the reliability and sustainability of smart city systems. Future research is recommended to validate the model in real-world implementations and integrate advanced technologies such as artificial intelligence for predictive threat detection.

This paper investigates the mechanical properties of eco-friendly concrete incorporating recycled aggregates and industrial by-products. An experimental program was conducted to assess the compressive strength, tensile strength, and durability of various concrete mixes. The results reveal that the use of recycled materials can produce concrete with comparable mechanical properties to conventional concrete. This study highlights the potential of eco-friendly concrete in reducing environmental impacts while maintaining structural integrity.

This study focuses on optimizing electric vehicle (EV) battery recycling through the use of green chemical processes and circular economy principles. The research aims to enhance the recovery of valuable metals lithium, cobalt, and nickel from used lithium-ion batteries (LIBs) in an environmentally sustainable manner. Green solvents were employed as a safer alternative to conventional, toxic chemicals, minimizing hazardous waste emissions and improving the efficiency of the recycling process. Experimental results showed that the green solvent-based process achieved high recovery rates of 90% for cobalt, 87% for nickel, and 85% for lithium, with metal purity levels exceeding 95% for all three metals. The study also examined the scalability of the green solvent method, revealing its potential to offer more sustainable and cost-effective solutions compared to traditional methods, which typically involve high temperatures and toxic chemicals. Despite the promising results, challenges such as solvent recovery and the adaptation of the process for large-scale industrial applications remain. Nonetheless, the study demonstrates that integrating green solvent-based recycling into the global EV supply chain can significantly reduce environmental impacts, conserve resources, and support the transition to a circular economy. The findings highlight the potential of this recycling method to provide a more sustainable and efficient solution for EV battery recycling, ultimately contributing to the development of a more sustainable EV industry.

This study investigates the impact of gamified learning platforms on the development of critical thinking and problem-solving skills among middle school students. With the increasing reliance on digital tools, fostering these essential skills has become a critical educational goal. Traditional teaching methods often fail to engage students effectively, which has led educators to explore innovative approaches such as gamified learning. The experimental design of this study involved two groups: an experimental group using gamified learning platforms and a control group following conventional teaching methods. Data were collected through pre-tests and post-tests measuring students' critical thinking and problem-solving abilities. Results indicated that the experimental group showed a significant improvement in both skills, with a 20-point increase in their scores, while the control group exhibited minimal progress. The findings highlight that gamified learning, which integrates game mechanics such as points, rewards, and leaderboards, enhances engagement and motivation, leading to improved cognitive skills. This study emphasizes the potential of gamification to revolutionize educational practices, suggesting that its integration can be a powerful tool to equip students with the necessary skills for the future.

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

Shopping activities have become a dominant phenomenon in people's lives, one of which is among students. Students are an important segment in society, so they have become a shopping preference that deserves to be understood more deeply. The aim of this research is to analyze student behavior and preferences from shopping experiences. This research uses a descriptive quantitative method with data collection using a questionnaire with closed answers. The questionnaire was filled in by 152 respondents spread across various universities in Indonesia. The research results show that the e-commerce applications that are often used by students are Shopee and Tokopedia. Students prefer to shop through online shops because the prices offered are more affordable than conventional shops. Apart from that, if you shop via an online shop, the activity time is shorter and more efficient. In the online shop, the products offered are quite diverse and varied, and there are lots of promotions on offer so that they attract the attention of buyers. Online shops are used by students to buy fashion and skincare products because the products offered are more diverse compared to offline shops. Based on this data, it can be understood that students choose to shop through online shops using the Shopee and Tokopedia applications and choose to shop for fashion and skincare.

This article explores the reframing of holistic education by integrating spirituality and ethics as core components to achieve transformative impact. The study highlights the limitations of conventional education systems that prioritize cognitive development while neglecting moral and spiritual dimensions. The objective is to propose a more comprehensive educational paradigm that nurtures learners as whole individuals—intellectually, emotionally, morally, and spiritually. Using qualitative literature analysis, this study identifies the role of spiritual and ethical integration in shaping character, enhancing empathy, and fostering social responsibility. The findings suggest that embedding these values within curriculum design and instructional practices contributes to meaningful personal development and community transformation. By addressing the growing need for a more inclusive approach to education, the article emphasizes how integrating spirituality and ethics can bridge gaps in traditional educational models. The research underscores the importance of fostering emotional and moral intelligence alongside intellectual growth, particularly in a world where global challenges require not only intellectual problem-solving but also ethical decision-making and empathy. The implications emphasize the need for inclusive and humanistic approaches in education, encouraging educators to act as facilitators of life values and agents of change. Through the integration of spirituality and ethics, education can help shape a new generation of compassionate leaders and engaged citizens. By reimagining holistic education through this lens, the article offers a framework that aligns academic achievement with ethical consciousness and spiritual growth, ultimately fostering a more just and compassionate society. This transformative approach offers a comprehensive model for educators and policymakers aiming to prepare students not only for professional success but also for meaningful lives rooted in integrity and responsibility.

This research aims to determine the influence of the Genially Platform on PAI learning outcomes at SMK Negeri 2 Semarang. The type of research used is field research with a quantitative approach. Data collection methods use observation, tests and documentation. This research design uses an experimental posttest only control design, with different treatments in two classes, namely the experimental class using the Genial platform and the control class using conventional learning. After being given different treatments, analysis prerequisite tests were carried out, namely data normality tests, data homogeneity tests, and hypothesis tests. In testing the hypothesis, researchers used the independent sample t-test. 

Learning models have a role in facilitating the process of transferring knowledge carried out by teachers to students. This study applies the direct instruction learning model with the aim of improving dribble skills in basketball learning. This research uses a quantitative approach with a pseudo-experimental method. The sampling technique used was cluster random sampling involving 115 students at junior high school level 1. The results showed that the direct instruction learning model was effectively applied to basketball dribble learning. The increase in the experimental group occurred by 48.90% for the skill variable, and 25.30% for the assessment variable. These results indicate that the direct instruction learning model is better than the conventional learning model. On the other hand, the direct instruction learning model has the possibility of ineffectiveness if applied to other learning and other levels of education.

The gaming industry continues to grow rapidly, with various genres having different sales patterns. This research aims to group the best game genres based on sales patterns using the K-Means Medoid algorithm. This method was chosen because of its ability to overcome data diversity and reduce the influence of outliers compared to the conventional K-Means algorithm. The data used in this research includes game sales information from various platforms, which is then analyzed to find distribution patterns and market trends. The results of this research show that certain game genres have superior sales patterns compared to others, and there are special characteristics in the groups that are formed. These findings are expected to help game developers and publishers in making strategic decisions regarding the development and marketing of their products. In addition, this research also contributes to the development of a data-based recommendation system that can be used to understand market preferences more accurately.