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The quality of assessment instruments plays an important role in determining the accuracy of measuring student learning outcomes in science learning in elementary schools. A good test instrument must meet certain criteria, such as validity, reliability, difficulty level, and discrimination power. This study aims to analyze the quality of daily science test items in grade VIc elementary schools based on these four criteria. The study used a quantitative. The subjects were 19 sixth-grade students, while the instrument analyzed consisted of 25 multiple-choice questions. Data processing and analysis were carried out using Microsoft Excel to calculate item validity through item correlation with total score, test reliability using internal consistency, difficulty level index, and discrimination index. The analysis results showed that 17 questions (68%) were declared valid, while 8 questions (32%) were invalid and needed to be improved. The results of the reliability test indicated that the test instrument had good reliability and was suitable for use as a measuring tool for student learning outcomes. Judging from the level of difficulty, 20 questions (80%) were moderate and 5 questions (20%) were easy, indicating a relatively balanced level of difficulty. Based on the discrimination power, 16 questions (66%) had very good discrimination power, 4 questions (16%) were good, 4 questions (16%) were sufficient, and 1 question (4%) was poor. Based on these findings, it can be concluded that the quality of the sixth grade science daily test questions is classified as good and the test instrument is suitable for use, but improvements are still needed on invalid questions and those with low discrimination power so that the quality of the assessment is more optimal. This study emphasizes the importance of teachers' abilities in compiling and analyzing test items to ensure that the assessment of science learning is objective, valid, and reliable.

This study is motivated by the importance of financial and asset management strategies in supporting the operational effectiveness of the Regional Disaster Management Agency (BPBD) of Central Sulawesi, which plays a strategic role in disaster mitigation, preparedness, emergency response, and post-disaster recovery. The main problems addressed are how financial and asset management strategies are implemented, to what extent they affect operational effectiveness. The objectives of this research are to analyze the applied strategies, assess their influence on operational effectiveness, and identify challenges as well as relevant solutions.The literature review refers to public financial management theories, regional asset management, and previous studies highlighting the relationship between financial governance, accountability, and public sector performance. This study employs a quantitative approach with a descriptive design. Data were collected through literature study, observation, interviews, and questionnaires distributed to BPBD staff in finance and asset divisions. The analysis included validity and reliability tests, along with multiple linear regression to examine the effect of independent variables on operational effectiveness. The findings show that BPBD Central Sulawesi’s financial management strategy in 2024 achieved a realization rate of 89–100% in most programs, although imbalances were found in certain activities such as the disaster management system arrangement, which only reached 38%. In terms of asset management, fixed assets dominate with a book value of IDR 19.6 billion, with significant growth in equipment and machinery. Regression analysis results indicate an R² value of 0.817, meaning that 81.7% of operational effectiveness is influenced by financial and asset management strategies, while the remaining 18.3% is explained by other factors.The study concludes that financial and asset management strategies significantly affect BPBD’s operational effectiveness. Nevertheless, challenges such as limited human resources, inadequate information systems, and discrepancies in budget realization require solutions through capacity building, technology utilization, and improved planning mechanisms to optimize disaster management effectiveness.

This study aims to analyze the effect of price on purchasing decisions of broiler chicken production inputs (sapronak) among partner farmers of PT Sejahtera Abadi Unggas, Kediri Unit. A quantitative approach with a causal research design was employed. The research sample consisted of 104 active partner farmers, selected using purposive sampling. Data were collected through structured questionnaires using a five-point Likert scale. The collected data were analyzed using validity and reliability tests, classical assumption tests, and simple linear regression analysis with the assistance of SPSS software. The results indicate that price has a positive and significant effect on purchasing decisions. The regression analysis produces the equation Y = 2.103 + 0.940X, with a significance value of 0.000, confirming that an increase in positive price perception significantly enhances purchasing decisions. Furthermore, the coefficient of determination (R²) of 0.699 shows that price explains 69.9% of the variation in purchasing decisions, while the remaining 30.1% is influenced by other factors not examined in this study. These findings highlight that competitive, fair, and value-based pricing strategies play a crucial role in strengthening long-term partnerships and sustaining purchasing behavior among broiler farmers. The study provides empirical evidence that can support managerial decision-making in developing effective pricing strategies within the agribusiness sector.

Online transportation services have become an important part of student life because they provide ease of access, time efficiency, and mobility flexibility. The Gojek application is one of the most frequently used services by students in Medan City to support academic and non-academic activities. This study aims to analyze students' experience in using the Gojek application and its effect on the effectiveness of the application in online transportation services. The research method used was a quantitative approach with survey techniques through a Likert scale questionnaire (1–5) to 50 students who were active users of Gojek. Data were analyzed using descriptive statistics and simple linear regression. The results showed that user experience (UX) was in the category of quite good with an average value of 3.58, while the effectiveness of the application was in the category of quite good with an average of 3.47. The reliability test showed Cronbach's Alpha values of 0.935 (UX) and 0.951 (Effectiveness), which means the instrument is very reliable. The results of the regression analysis showed that user experience had a positive and significant effect on the effectiveness of the application (β = 0.961; R² = 0.881; p < 0.001). Thus, the better the student experience in using the Gojek application, the higher the effectiveness felt in online transportation services. These findings confirm that improving the quality of user experience is a key factor to maintain and increase the effectiveness of online transportation applications among students.

PT Telering Distrindo is a telecommunications distribution and retail company whose operations rely heavily on information systems. As the business expands and transaction volumes increase, higher demands for system security and reliability require a strategic evaluation of information technology (IT) implementation to ensure alignment with business objectives. This study aims to analyze the condition of the IT infrastructure at PT Telering Distrindo and to formulate development strategies using the Ward & Peppard framework. The research methods include literature review, system observation, IT architecture analysis, and interviews with internal stakeholders. The results show that the company has implemented core systems such as aPOSPlus, aBusinessPlus, and the PPOB TR Reload application, which play an important role in supporting business operations. However, several weaknesses remain in system security, integration, and IT risk management. The SWOT analysis identifies strengths in access control and authentication mechanisms, opportunities to leverage security as a competitive advantage, and threats from cyberattacks and high dependency on IT infrastructure. The study concludes that the Ward & Peppard framework provides a comprehensive strategic perspective and serves as a foundation for developing a more structured, secure, and sustainable IT strategy to support business growth.

This study investigates the relationship between design patterns, modular architecture, and the maintainability of distributed real time systems developed using agile practices. Distributed real time systems are critical in various sectors, including telecommunications, healthcare, and automotive, where strict timing constraints and reliability are essential. Agile methodologies, known for their flexibility and iterative development, have been widely applied to software engineering, but their impact on long-term system maintainability, especially in complex real time environments, has been insufficiently explored. This research employs an empirical analysis, combining both quantitative and qualitative data from multiple real time system projects using agile methods. The analysis focuses on the application of design patterns, such as Singleton, Observer, and Factory, and evaluates the effectiveness of modular architectures in enhancing system scalability, flexibility, and long-term sustainability. The study also explores how agile practices contribute to system performance and maintainability, despite challenges related to frequent updates and coordination among distributed teams. Key findings show a positive correlation between the consistent use of design patterns and modularity, which significantly improves the maintainability and adaptability of distributed real time systems. This research also highlights the challenges faced by agile methods in maintaining architectural consistency and managing non-functional requirements, particularly in distributed environments. The results contribute valuable insights into adapting agile practices to meet the specific demands of distributed real time systems, offering recommendations for developers and project managers to incorporate modular architecture and design patterns to enhance long-term system sustainability. Further research is suggested to explore new design patterns and investigate the broader impact of agile methodologies on system quality beyond maintainability.

Cloud-native systems are essential for modern software development, offering enhanced scalability, flexibility, and resilience through cloud computing environments. However, ensuring the reliability and performance of these systems presents a challenge due to their dynamic and distributed nature. Traditional testing methods, such as unit and integration testing, while valuable for detecting individual component defects and interactions, are insufficient for predicting failure rates in complex, cloud-native applications. This study explores the effectiveness of various testing techniques and quality metrics in predicting failure rates within scalable cloud-native systems. A comparative experimental study was conducted using three primary testing techniques: unit testing, integration testing, and chaos testing. The results indicate that chaos testing, when combined with advanced quality metrics such as migration rate and mismigration rate, significantly outperforms traditional methods in predicting failure rates and evaluating system resilience. These findings suggest that chaos testing offers a more comprehensive evaluation, simulating real-world disruptions to test system behavior under stress, which is essential for cloud-native environments where high availability and fault tolerance are critical. The study also highlights the importance of integrating predictive quality metrics, which improve the accuracy of failure predictions and enhance system reliability. The study concludes that for cloud-native systems, a combination of advanced testing techniques and predictive metrics is essential for ensuring high availability, scalability, and reliability in dynamic environments. Future research should focus on refining predictive testing approaches, developing standardized frameworks, and empirically validating new testing methods to address the growing complexity of cloud-native systems.

This study investigates the adoption of adaptive DevOps practices in embedded systems used in safety-critical industrial applications. Traditional DevOps models, which are primarily designed for cloud-based systems, face significant challenges when applied to embedded platforms due to hardware constraints, real-time performance requirements, and stringent safety standards. The research focuses on developing a tailored DevOps framework that integrates continuous integration/continuous delivery (CI or CD) pipelines, automation, real-time monitoring, and safety assurance processes to enhance system reliability, performance, and compliance with regulatory standards. The study uses a case study methodology, involving embedded system teams across multiple industrial sectors, to assess the impact of these adapted DevOps practices on system stability and operational efficiency. Key findings show that the adoption of adaptive DevOps practices led to significant improvements in system reliability, performance, and deployment stability. Continuous feedback mechanisms allowed for early issue detection and faster resolution, leading to enhanced system uptime and responsiveness. Additionally, the integration of safety assurance into the DevOps pipeline ensured that safety-critical systems complied with required safety integrity levels and certification standards. The study further explores the integration of DevOps with embedded safety-critical systems, highlighting the benefits of cross-domain collaboration, enhanced communication, and the ability to address the unique challenges of these platforms. The research also underscores the limitations of conventional DevOps models in embedded systems and presents practical implications for the wider adoption of DevOps in safety-critical industrial applications. Future research is recommended to refine DevOps frameworks for embedded systems, integrating emerging technologies like the Industrial Internet of Things (IIoT) and Digital Twins to further optimize performance, security, and predictive maintenance.

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world environments.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

The development of information technology encourages organizations to adopt a more efficient, flexible, and secure data management system, especially in the field of financial management that requires high accuracy and reliability. One of the technologies that is widely used is cloud computing, which offers easy access to data and an integrated security system. This article aims to analyze the utilization of cloud technology in improving the security and accessibility of financial management data. The method used in this study is a literature study by examining various scientific sources, books, and online news relevant to the topic of cloud computing and financial data management. The results of the study show that cloud technology is able to improve data security through the implementation of encryption, multi-layered access control, user authentication, and a reliable data backup system. In addition, cloud technology also improves the accessibility of financial data because it allows users to access information in real-time, flexibly, and without location or device restrictions. Thus, the application of cloud technology can be a strategic solution for organizations in improving operational efficiency, data security, and the quality of decision-making in financial management.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

Public transportation plays an important role in supporting community mobility and accelerating regional economic growth. To improve public transportation services in East Java, the East Java Provincial Transportation Agency introduced the Trans Jatim Bus system. However, its implementation still faces several challenges, including long bus arrival times, the use of mobile applications that are difficult for elderly users, and inconsistencies in the availability of supporting facilities such as seating and trash bins at bus stops. This study aims to analyze the quality of Trans Jatim Bus services based on the service quality dimensions proposed by Wirtz and Lovelock. The research employs a descriptive qualitative approach. Data were collected through interviews, observations, and documentation, and analyzed using stages of data collection, data reduction, data presentation, and conclusion drawing. The findings indicate that reliability has not met service standards due to prolonged waiting times. Responsiveness and assurance were found to meet service standards, as passengers expressed satisfaction with service responses and complaint handling. Empathy was considered adequate, although accessibility issues for elderly passengers remain. Tangible aspects were also sufficiently met, but inconsistencies were found in the provision of seating and trash bins at several bus stops. Based on these findings, the study recommends increasing the number of buses on corridor 2, evaluating bus schedules, conducting public awareness campaigns, and ensuring the consistent provision of seating and trash bins at all bus stops to improve overall service quality.

This research explores the role of human algorithm interaction mechanisms in enhancing trust, reliability, and user confidence in Decision Support Systems (DSS). Traditional DSS models often focus solely on algorithmic accuracy and performance, neglecting crucial factors such as transparency and user engagement, which are essential for building trust. By incorporating explainable AI (XAI) techniques like SHAP and LIME, real-time feedback mechanisms, and user-friendly interfaces, the study develops structured interaction models that improve the interpretability of AI-driven decisions. The results show that transparent decision-making processes and interactive features significantly enhance user trust, making DSS more reliable and easier to adopt. Users interacting with systems that provide clear, understandable explanations of decisions, along with real-time updates on the system’s confidence, reported higher levels of decision-making confidence, especially in high-stakes scenarios. These improvements lead to greater user engagement and adoption of the system in various domains, including healthcare and finance. The study also highlights the importance of balancing interpretability with efficiency in user interface design to ensure both trust and usability. The findings contribute to the design of more user-centric DSS that prioritize trust, interpretability, and cognitive factors, providing a framework for the successful integration of intelligent decision support systems in complex decision-making environments. Future research should focus on refining interaction models and exploring the broader applicability of these systems in different sectors.

The increasing demand for rapid software delivery has led to the widespread adoption of Continuous Integration (CI) and Continuous Deployment (CD) pipelines. These pipelines automate the processes of code integration, testing, and deployment, significantly improving the speed and reliability of software development. However, traditional CI or CD pipelines often overlook security testing, leading to vulnerabilities in the deployed software. To address this gap, this study proposes an integrated framework that embeds automated security testing within the CI or CD process. The framework incorporates security testing tools such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Vulnerability Assessment and Penetration Testing (VAPT) to ensure continuous security checks throughout the development lifecycle. The experimental results show that the proposed framework enhances early vulnerability detection, with detection rates increasing from 30% to 70%. Additionally, the framework reduces deployment failures from 50% to 20%, demonstrating its effectiveness in improving software dependability. While the integration of automated security testing adds a slight 5% increase in pipeline execution time, this minimal impact does not significantly affect the overall speed of the pipeline. The proposed approach successfully balances security and efficiency, ensuring that software is both secure and delivered at high speed. This research highlights the importance of integrating security into CI or CD pipelines and demonstrates that it is possible to achieve high security without sacrificing the speed of software development. The study also discusses the practical implications for software development teams and suggests areas for future research, including the integration of advanced AI-driven security testing tools and the expansion of the framework's applicability across different software projects.

The rapid growth of digital marketplaces has fundamentally transformed consumer decision-making processes by intensifying price transparency, product comparability, and information exposure. In highly competitive marketplace environments, consumers are frequently confronted with numerous product alternatives and dynamic pricing structures, which shape how they evaluate options and ultimately decide to purchase. This study examines the effects of product variety and price perception on consumers’ purchase decisions in the context of ROSCA tumbler products sold on the Shopee marketplace. Using a quantitative explanatory approach, this research employs a survey-based design to analyze the relationships among variables and to estimate their relative effects through multiple linear regression analysis. Data were collected from 100 respondents who had previously purchased ROSCA tumblers on Shopee, selected using purposive sampling based on predefined criteria. The research instrument consisted of 15 Likert-scale items measuring product variety, price perception, and purchase decision. Data analysis included descriptive statistics, instrument validity and reliability testing, regression assumption diagnostics, and hypothesis testing using both partial (t-test) and simultaneous (F-test) procedures. The findings reveal that product variety has a positive and significant effect on purchase decisions, indicating that consumers are more likely to finalize purchases when they perceive product options as sufficiently diverse, relevant, and comparable. Price perception also exerts a positive and significant influence and demonstrates a stronger relative effect compared to product variety. This result underscores the central role of perceived price fairness, competitiveness, and value-for-money in shaping purchase decisions within transparent and highly competitive marketplace settings. Simultaneously, product variety and price perception explain a substantial proportion of variance in purchase decisions, highlighting their combined importance as key marketing stimuli. These findings contribute to the literature on digital consumer behavior by providing empirical evidence from a specific marketplace–product context and offer practical implications for sellers and brand managers in optimizing assortment design and pricing strategies to enhance conversion rates in online marketplaces

The increasing integration of Cyber physical Systems (CPS) into industrial environments has highlighted the need for secure, scalable, and efficient cryptographic key management systems. Traditional centralized key management protocols are often limited by vulnerabilities such as single points of failure, scalability issues, and significant overhead. Blockchain technology presents a promising solution to these challenges by leveraging decentralization, immutability, and transparency to enhance security and efficiency in CPS. This study investigates the use of blockchain based cryptographic key management systems, focusing on smart contracts for automated key distribution and rotation. Experimental results demonstrate that blockchain based systems significantly improve system integrity, auditability, and resilience, offering enhanced protection against cyber-attacks and reducing the risks associated with centralized systems. Blockchain’s decentralized architecture eliminates the need for a central authority, making it more resistant to tampering and operational failures. Additionally, smart contracts automate the key management process, improving efficiency while maintaining a high level of security. The study also evaluates the impact of blockchain on communication performance, finding that it reduces latency and overhead by automating processes and eliminating the need for centralized control. Despite these advantages, challenges such as scalability, latency, and integration with legacy systems remain. The study concludes by suggesting future research directions, including the development of lightweight blockchain protocols tailored for industrial applications and the integration of blockchain with emerging technologies like Artificial Intelligence (AI) to further enhance key management in CPS. Blockchain based solutions have the potential to transform the security landscape of industrial environments, offering greater robustness, reliability, and trust.

This study aims to: (1) Describe and analyze the influence of memorization of the Qur'an on the emotional intelligence of students at the Integrated Islamic Private Middle School of Al-Furqan Tebas Islamic Boarding School, Sambas Regency, (2) Describe and analyze the influence of memorization of the Qur'an on the spiritual intelligence of students at the Integrated Islamic Private Middle School of Al-Furqan Tebas Islamic Boarding School, Sambas Regency. This study is a descriptive quantitative study with a causal associative research type (cause and effect). In this study, there are 3 variables, namely the first variable is memorization of the Qur'an, the second variable is emotional intelligence and the third variable is students' spiritual intelligence. The population with research subjects are 40 students of class VIII of the Integrated Islamic Private Middle School of Al-Furqan Tebas Islamic Boarding School. Data collection using the questionnaire method. Instrument validity test using the product moment analysis technique, while reliability test using the alpha coefficient. The data analysis technique used multiple regression analysis. Prior to data analysis, a normality test was conducted. The results of the study indicate that: (1) There is a positive and significant effect of Quran memorization on emotional intelligence, with a magnitude of 0.388 (38.8%), with the remainder influenced by other factors. (2) There is a positive and significant effect of Quran memorization on spiritual intelligence, with a magnitude of 0.376 (37.6%), with the remainder influenced by other factors.