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The obligation to seek knowledge holds a central position in Islamic law, grounded in strong normative and theological principles. Islam classifies the pursuit of knowledge as fardhu ‘ain (an individual duty) for essential religious and worldly knowledge, and fardhu kifayah (a collective duty) for broader societal needs. This article explores the legal foundations of this obligation in Islamic jurisprudence and examines its relevance in the context of contemporary education. Using a normative-juridical method, this study analyzes primary Islamic sources such as the Qur’an, Hadith, and the views of classical and modern scholars. The findings show that Islam does not treat knowledge merely as a tool for personal development, but as a moral and spiritual responsibility. In modern educational systems, these values remain highly relevant, particularly in addressing ethical decline and the loss of purpose in learning. This article recommends the integration of Islamic legal perspectives on knowledge into educational curricula to help restore the spiritual and moral aims

This study explores the challenges and expectations faced by teachers in educating deaf students at SLB Negeri Keleyan. Using a qualitative descriptive approach, data were collected through interviews and observations to understand the real experiences of teachers in the learning process. The findings reveal that one of the main challenges is teaching students with total hearing loss from birth, which demands more time, creativity, and adaptation in instructional strategies. Teachers often adjust the curriculum based on the students’ individual abilities. Despite these difficulties, students show positive development when they receive consistent encouragement, emotional support, and recognition. Teachers express hope for improved training opportunities, especially in visual and adaptive teaching techniques, as well as better support from families and the government. Additionally, they highlight the importance of assistive learning tools and inclusive job opportunities after graduation. This study affirms that with collaborative support, deaf students have the potential to grow, learn, and contribute meaningfully to society.

The development of Artificial Intelligence (AI) technology in the education sector has undergone major changes. These changes bring great opportunities to change the learning process to be more personal, effective, and inclusive. The purpose of this journal is to analyze the potential and challenges that need to be faced in the application of AI in education so that it can run optimally. The method used in this study is the literature review method of various recent studies that discuss the use of AI in education, The results of the study show that AI has great potential to improve the personalization of learning and educational efficiency. However, there are number of significant challenges, such as limited infrastructure, privacy and data security issues, resistance from educators and students, and the digital divide that still occurs in several regions. Therefore, a strategy is needed that includes a academic laziness that reduces learning motivation, constraints on AI contextual knowledge, Inability to process complex information, loss of literacy skills, amd the risk of addiction to AI technology. With a planned and sustainable strategy, AI has great potential to improve the quality of national education and also reduce the dangers that may arise from the modern technological era.

Madrasas and Islamic boarding schools are Islamic educational institutions that have a strategic role in shaping the character, morals, and competencies of students. In addition to carrying out the formal learning process, these two institutions are also centers for moral, spiritual, and social development. In their operations, madrasas and pesantren manage various administrative activities, such as student data collection, attendance recording, grade processing, teacher data management, lesson schedule preparation, and delivery of academic information to related parties. However, many institutions still use manual systems that rely on notebooks or separate applications that are not integrated. This condition poses a number of obstacles, including the high risk of recording errors, duplicate data that is difficult to track, the slow process of recapitulation of grades and attendance, and delays in the delivery of academic information. In addition, manual processes cause administrative management to be less efficient and prone to data loss. As the need for fast, accurate, and accurate information increases, technological solutions are needed that are able to integrate all administrative and academic processes in one centralized system. Web-based information systems are an effective alternative to improve work efficiency, minimize errors, and facilitate access to information for teachers, students, and guardians. With the implementation of a well-managed system, madrasas and Islamic boarding schools are expected to improve the quality of services and support a more modern, structured, and sustainable learning process.

With the times, cash transactions that used to use cash are now turning to credit cards. However, the increasing use of credit cards presents challenges, especially in maintaining customer loyalty. Customer churn is the loss of customers within a certain period for various reasons. Logistic Regression is a machine learning algorithm that studies the relationship between a dependent variable and several independent variables and Extreme Gradient Boosting (XGBoost) is a Gradient tree-boosting algorithm that offers out-of-core learning and sparsity awareness.The purpose of this study is to compare the performance between Logistic Regression and Extreme Gradient Boosting (XGBoost) algorithms in predicting customer churn in credit card services using evaluation metrics such as accuracy, precision, recall, and F1-score. Based on the research results, it can be concluded that XGBoost has better performance in all evaluation metrics, both in terms of precision, recall, F1-score, and accuracy. Based on the research, XGBoost shows superior performance compared to Logistic Regression in all evaluation metrics.

Advances in information and internet technologies have significantly transformed the business environment, including the financial sector. The COVID-19 pandemic has further accelerated this digital adoption, expanding the e-commerce industry and highlighting the necessity for secure online transactions. Credit Card Fraud Detection (CCFD) stands critical as the prevalence of fraudulent activities continues to rise with the increasing volume of online transactions. Traditional methods for detecting fraud, such as rule-based systems and basic machine learning models, tend to fail to keep pace with fraudsters' evolving tactics. This study proposes a novel ensemble deep learning-based approach that combines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multilayer Perceptron (MLP) with the Synthetic Minority Oversampling Technique and Edited Nearest Neighbors (SMOTE-ENN) to address class imbalance and improve detection accuracy. The methodology integrates CNN for feature extraction, GRU for sequential transaction analysis, and Multilayer Perceptron (MLP) as a meta-learner in a stacking framework. By leveraging SMOTE-ENN, the proposed approach enhances data balance and prevents overfitting. With synthetic data, the robustness and accuracy of the model have been improved, particularly in scenarios where fraudulent examples are scarce. The experiments conducted on real-world credit card transaction datasets have established that our approach outperforms existing methods, achieving higher metrics performance.

TK Aisyiyah Bustanul Athfal 1 Bengkulu is an early childhood education institution that still manages administrative and academic processes manually, such as recording student data, admitting new learners, and assessing learning outcomes. This has the potential to cause recording errors, data loss, and delays in information processing. This research aims to design a web-based school information system that can help manage school data and information in a computerized manner. The system includes student management, payment, assessment, and school profile modules. The methods used in designing this system include observation, interview, and literature study. System requirements analysis was conducted to identify the needs of users, both school managers and the community. The system was developed using Codeigniter 3 framework and MySQL database. It is expected that this system can improve the efficiency and accuracy of data management and accelerate the delivery of real-time information.

This article aims to analyze the use of muqabalah as a language style in Surah Al-Mulk and its impact on conveying moral and spiritual messages in the Al-Qur'an. Using a qualitative approach, this research analyzes verses containing muqabalah and discusses various contrasts that arise, such as life and death, courage and fear, and luck and loss. The methodology used includes literature study, tafsir, and rhetorical analysis to explore how muqabalah enriches the meaning of the text and increases reader understanding. The results of this research show that there is Muqabalah Khilafi in verse 3, Muqabalah Naziri in verse 15, and Muqabalah Naqidhi in verses 20, 21, and 22. These findings confirm that the use of muqabalah in Surah Al-Mulk not only clarifies the meaning, but also has a psychological impact profound for readers. The contrasts displayed serve to increase moral awareness and spiritual responsibility, as well as provide relevance to these messages in modern life. The uniqueness of this research lies in its emphasis on how muqabalah can be applied in learning the Koran in the contemporary era, as well as providing suggestions for increasing spiritual awareness through creative and relevant approaches. Thus, it is hoped that this research can contribute to a better understanding and application of the teachings of the Qur'an among Muslims.

This research examines the morphological aspects of Indonesian with a focus on the process of word formation and changes in morphological structure in contemporary Indonesian language use. The aim of this research is to identify and analyze various morphological processes that occur in Indonesian word formation, as well as to reveal productive patterns of word formation in the development of modern Indonesian. This research uses a qualitative descriptive method with a morphological approach. Data was collected using skill-free listening (SBLC), note-taking techniques and documentation techniques from various written sources. Data analysis was carried out using the agih method using the technique for direct elements (BUL), loss technique, replace technique, and expand technique. The results of the research show that: (1) the affixation process is the most productive morphological process in Indonesian word formation, (2) new patterns were found in the reduplication process which reflect the dynamics of language development, (3) the composition process shows a tendency to form compound words which increasingly complex along with modern communication needs. This research provides theoretical contributions in the development of Indonesian language morphology studies and practical implications for Indonesian language learning.

Vaccination attacks for children 6-11 years old is very important in community service activities because they can help speed up the spread of the covid-19 vaccine, thereby minimizing elementary school children contracting Covid-19. The purpose of this community service is to help elementary schools feel calmer in carrying out offline learning, as an effort to avoid learning loss when learning online. This activity was supported by the principal and teachers of SD Krian III and the AAL Health unit Health workers. Vaccination is carried out 2 times, namely the 1st vaccination on January 11, 2022 and the second vaccination on February 16, 2022. The vaccination implementation is guided by the Decree of the Minister of Health of the Republic of Indonesia Number HK.01.07/MENKES/6688/2021 concerning the Implementation of the Corona Virus Disease 2019 Vaccination (Covid- 19) for children aged 6 (six) to 11 (eleven) years, with a vaccination service screening format. The results of the 1st vaccination activity of 610 registrants who vaccinated 609 children, of which 1 was postponed. Vaccination 2 of 452 children, 1 child for vaccine 1 and 451 children for vaccine 2. It is recommended to develop a vaccine attack to elementary school students in areas that have not been vaccinated.

Visual impairment is a general term used to describe partial or complete loss of vision function. This research uses a qualitative approach. Observation results show that by implementing appropriate teaching methods, blind children can learn effectively. Good interaction between teachers and students, as well as the use of braille and audio visual aids, greatly contribute to a positive learning process.

The condition and situation of education after the Covid 19 Pandemic caused "Learning Loss" and "Learning Outcomes Loss". It takes a learning experience by providing students' reasoning insights and intellectual competence through a portfolio. The presence of educators with the ability to transform learning is needed as a learning conditioning for the ability of students' portfolios.  Quantitative research using a Likert scale questionnaire instrument constructed and developed from the teacher assessment system and David M. Johnson's portfolio. The population is 80 teachers and 80 students of grade VI elementary school in the Kebon Jeruk area, West Jakarta. The findings of the study provide information that the ability of students' portfolios can be constructed and optimized through learning transformation with three dimensions in the form of active participation and success of students, assessment and feedback on the progress of learning experiences and management of students and learning materials.

Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.

Motivation to learn is certainly one of the important things in determining the success of learning. However, students often feel a loss of motivation to learn so that students experience a decrease in learning motivation which will affect student learning outcomes. Therefore, it is important for teachers to create strategies to increase students' learning motivation. Strategies that can be implemented by teachers include using interactive learning media and can increase students' active participation during learning. The aim of this research is to determine students' learning motivation after implementing science learning using diorama learning media. This research uses descriptive qualitative methodology, examining the process and influence of implementing diorama media on student learning motivation. The data collected for this research took the form of direct observation, student interviews and analysis of several documents used to collect data, which was then analyzed using descriptive analysis methods, the form of this research is Classroom Action Research (PTK). The results obtained from this research are that the application of the Diorama of the Nature of Light learning media in science and science learning in class V at SDN Karangrejo 2 succeeded in increasing student motivation in learning. Students who were previously passive and did not play much of a role in the learning process after implementing learning using the Nature of Light Diorama showed significant changes in their activity in learning.

Technology has undoubtedly revolutionized the way we learn and acquire knowledge. In recent years, there has been a significant shift towards integrating technology into educational practices, with the aim of enhancing the learning experience for students. This transformation has sparked a debate among educators and researchers about the implications of this shift on the traditional methods of teaching and learning. Some argue that technology has the potential to democratize education and provide equal opportunities for all learners, while others express concerns about the impact of digital distractions and the loss of face-to-face interaction. Through the lens of post-positivism, criticality, and constructivism theory, this critical analysis aims to explore the complexities of the transformation of learning with technology. By examining the underlying assumptions and implications of integrating technology into education, we can gain a deeper understanding of how it may shape the future of learning. This analysis will delve into the various perspectives and theories surrounding the use of technology in education, considering both the benefits and drawbacks. Through a balanced examination of the evidence, we hope to uncover the key factors that will determine the success of technology in shaping the future of learning. Ultimately, the goal is to provide insights that will guide educators and policymakers in making informed decisions about integrating technology into educational practices.

The increasing frequency and severity of tsunamis in coastal areas underscore the urgent need for efficient Tsunami Early Warning Systems (TEWS). This research aims to optimize TEWS by integrating fast computational tsunami wave modeling to enhance prediction speed and accuracy. The study utilizes numerical simulations employing finite volume methods, along with GPU acceleration, to model tsunami wave propagation and its impact on coastal areas. Machine learning techniques, such as regression trees, are incorporated to analyze large datasets of pre-computed tsunami simulations for accurate forecasting. The results reveal that by applying rapid computational methods, detection time can be reduced by up to 7 minutes, particularly for near-field tsunamis. This significant time-saving enables more effective evacuation procedures and better disaster mitigation efforts. In comparison to conventional systems, the fast computation model also provides more accurate predictions, including tsunami heights and arrival times. The implications of these findings suggest that fast computational methods can substantially improve the current TEWS, allowing for quicker and more reliable tsunami warnings. Moreover, the integration of advanced machine learning techniques ensures the system's adaptability and robustness in predicting tsunami behaviors based on varying data inputs. The potential for implementing this model in tsunami-prone regions worldwide is considerable, offering an improved approach to tsunami disaster preparedness and response. By reducing detection time and enhancing prediction accuracy, the optimized TEWS can significantly minimize loss of life and infrastructure damage, making it a valuable tool for global disaster management strategies.  

Palembang Religious Education and Training Center as an education and training institution utilizes information technology, especially wifi internet networks to support various learning and training activities. To support these needs, a quality internet network is needed, so an evaluation and measurement of Quality of Service (QoS) is needed, because QoS is able to measure various important parameters in the network, such as throughput, delay, jitter, and packet loss, all of which play an important role in ensuring the network functions properly.  The standard used is TIPHON. By using the Action Research method, this research will produce real internet quality data in accordance with actual conditions. After testing, the average results on the 4 parameters tested obtained an index value of 3. So that the overall quality of the internet network at the Palembang Religious Education and Training Center has good network quality.

After the COVID-19 pandemic, the phenomenon of loss learning forced curriculum changes to become an unavoidable necessity so that the government launched an independent curriculum. The curriculum was developed following the times. The research method used in this research is the library research method or literature study. The author concludes that the independent curriculum is expected to be able to provide concrete and meaningful and effective learning experiences in developing the skills and expertise of students as lifelong learners with Pancasila character. The independent curriculum in social studies learning that focuses on the integration of values and competencies not only encourages intellectual intelligence, but also emotional and social intelligence, such as empathy, social awareness, and effective communication skills. An active and innovative social studies curriculum in independent learning is expected to not only focus on academic achievement, but also on developing students' characters and competencies as socially responsible individuals, while supporting post-pandemic learning recovery.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

The global clean water crisis is exacerbated by significant losses in water distribution networks (WDNs), resulting in inefficient use of both water and energy resources. Traditional methods of leak detection and pressure management often fail to address these inefficiencies, leading to substantial water wastage and high operational costs. This research aims to design a sustainable, smart water distribution system using advanced technologies such as Machine Learning (ML) for leak detection and automated pressure control. The system employs real-time monitoring through IoT sensors, which continuously gather data on water pressure, flow rates, and other critical parameters. This data is analyzed using various ML algorithms, including supervised and unsupervised learning models, to detect anomalies indicative of leaks. Additionally, the system integrates automated pressure control mechanisms that dynamically adjust pressure to prevent over-pressurization, reducing both water loss and energy consumption. By combining leak detection and pressure control, the proposed system offers a more efficient, sustainable solution to water resource management compared to traditional methods. The expected outcomes include a significant reduction in water loss, enhanced energy efficiency, and improved water service quality. However, the implementation of such a system in rural or small-town infrastructure faces challenges, including sensor maintenance, algorithm reliability, and regulatory issues. A cost-benefit analysis suggests that while the initial investment in smart technologies may be high, the long-term savings in water and energy costs outweigh these costs. This study underscores the potential of ML-based systems in enhancing water conservation, operational efficiency, and sustainability in water management.