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This study aims to determine the benefits of the farmer card program in improving the welfare of poor farmers in Talotenreng Village at the BPP Sabbangparu of the Wajo Regency Agriculture and Food Security Office and to find out the factors that hinder the evaluation of the farmer card program in improving the welfare of poor farmers in Talotenreng Village at the BPP Sabbangparu of the Wajo Regency Agriculture and Food Security Office. The research method used is a qualitative method, usually the data is inductive or qualitative, and the type of research used is descriptive that provides a detailed picture or phenomenon. The results of this study show that the Farmer Card Program in Talotenreng Village has not been used by farmers as recipients of farmer cards or is still tentative so that farmers cannot feel the benefits, because there are no instructions, directions or orders from the Central Government/Ministry of Agriculture of the Republic of Indonesia. The Farmer Card Program is the latest Government program implemented in all regions with the aim of redeeming the purchase of subsidized fertilizers in an effort to improve the welfare of farmers in accordance with Presidential Regulation No. 77 of 2005 concerning the Determination of Subsidized Fertilizers as Goods in Supervision as amended by Presidential Regulation No. 15 of 2011 concerning Changes in the Determination of Subsidized Fertilizers as Goods Under Supervision.    

This research aims to describe the planning, implementation and evaluation of instilling religious and moral values ​​in Mardhotilah Kindergarten, Gunung Kembang Village, Sarolangun District Jambi. This research uses a qualitative approach with a descriptive type of research conducted in kindergarten with the research subjects being the school principal, teachers and children. Data collection was carried out using interview, observation and documentation techniques. Data were analyzed using qualitative descriptive analysis techniques. Data validity is carried out by triangulating sources and techniques. The research results show; 1) Planning is made by the teacher as a religious team which is arranged into a Semester (Prosem), Weekly Program Plan (RPPM) specifically for instilling religious and moral values, which is divided into seven aspects, namely, aqidah, akhlaq, worship, memorizing letters, memorizing prayers/hadith, Al-Qur'an signals, and mahfudhot; and RPPH. 2) In its implementation, instilling religious and moral values ​​uses various methods, including the habituation method, the example method, the field trip method, the storytelling method and the play method which are applied in various activities carried out by children from the time the child arrives at school until the child returns home from school; 3) Evaluation is carried out through observation assessments, anecdotal notes and portfolios for semester report cards as well as follow-up programs in the form of enrichment; 4) If there are non-Muslim students, the school gives students the option to choose other universal subjects.

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.

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, making network security a critical concern in modern information systems. Traditional security approaches, such as rule-based and signature-based systems, are often limited in detecting sophisticated and unknown attacks. Therefore, this study proposes an Anomaly-Based Intrusion Detection System (AbIDS) utilizing machine learning and deep learning techniques to enhance detection capabilities. The research adopts a Design Science Research approach, involving stages of problem identification, data collection, preprocessing, model development, system implementation, and evaluation. Several models, including Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are implemented and compared. The results indicate that deep learning models, particularly LSTM and CNN, outperform traditional machine learning methods in terms of accuracy, precision, recall, and F1-score, while maintaining a lower false positive rate. Additionally, the integration of incremental learning enables the system to adapt to new attack patterns without requiring complete retraining, improving scalability and real-time performance. Despite the promising results, challenges such as computational complexity and false positives remain. Overall, the proposed IDS model demonstrates strong potential as an effective and adaptive solution for enhancing network security in dynamic environments.

Virtual Reality (VR) is not new in today's digital era. This technology has developed very quickly and has penetrated into many aspects of life, such as education. In this article, researchers reviewed current and previous research, to understand the extent to which Virtual Reality (VR) technology is used as an alternative learning media in educational institutions using the Systematic Literature Review (SLR) method by defining research questions as the first step. The search process was carried out from Scopus database using Publish or Perish 8 tools, and the selection of articles was based on inclusion and exclusion criteria related to the research questions. There were 200 articles found in the early stages of the search with a total of 22 articles that met the inclusion and exclusion criteria. The results obtained are in the form of a discussion of several aspects related to the use of virtual reality as a learning medium, such as: the type of technology, the location of educational institutions that use VR, learning domains, educational levels, evaluation techniques for learning with VR, and the purpose/benefit of its use.

Hospital Information Systems (HIS), or Sistem Informasi Rumah Sakit (SIRS), play a critical role in enhancing administrative efficiency, decision support, and healthcare service quality. However, their implementation and effectiveness vary significantly across healthcare settings, particularly in low- and middle-income countries (LMICs). This study aims to systematically evaluate the existing literature on HIS effectiveness, implementation barriers, and administrative impact. Using a PRISMA-based Systematic Literature Review (SLR) approach, we examined 14 high-quality studies from multiple scholarly databases including PubMed, Scopus, ScienceDirect, and Garuda. The review applied a hybrid thematic synthesis grounded in HOT-FIT and DeLone & McLean models, combined with a normalized quality scoring system. The findings reveal that HIS implementations positively influence administrative workflow, billing accuracy, and patient throughput, though outcomes are context-dependent. Key challenges include lack of interoperability, resistance to change, and insufficient training. Notably, regulatory mandates and national digital health policies were found to significantly enhance HIS adoption and sustainability. This review contributes a multidimensional synthesis of HIS performance, highlighting the importance of human, organizational, and policy alignment. It offers an evidence-backed framework for HIS evaluation that bridges theory and practice. We conclude that integrated, context-sensitive HIS models are essential for advancing hospital management and public health systems, and recommend further empirical studies on long-term impact and cross-sector integration.

Cloud-based resource allocation and VM/container orchestration play a crucial role in ensuring performance, scalability, and energy efficiency in modern distributed computing environments. This study investigates the effectiveness of centralized and decentralized scheduling models combined with heuristic and optimization-based allocation strategies in container-based cloud infrastructures. A quantitative experimental approach was employed to evaluate system performance under varying workload intensities. Key evaluation metrics included response time, throughput, resource utilization, SLA violation rate, and energy consumption. The experimental results indicate that centralized scheduling mechanisms experience scalability limitations and increased latency under high workload conditions. Although optimization-based allocation improves performance within centralized architectures, coordination bottlenecks remain significant. In contrast, decentralized scheduling models demonstrate superior adaptability, reduced response time, and improved throughput due to distributed decision-making and reduced control overhead. The integration of intelligent optimization techniques further enhances resource utilization and energy efficiency, achieving the lowest SLA violation rates and highest system stability. Overall, the findings confirm that combining decentralized scheduling with optimization-driven resource allocation provides a more scalable and sustainable orchestration strategy for modern cloud environments. This approach is particularly suitable for dynamic, large-scale, and latency-sensitive applications in container-based and edge-integrated cloud systems.

This study explores the development and evaluation of an adaptive Intrusion Detection and Response System (IDRS) driven by Reinforcement Learning (RL) for securing 5G networks. The RL-based IDS is designed to overcome the limitations of traditional security systems by dynamically learning from real time network traffic and adapting to emerging cyber threats. Introduction: The rapid growth of 5G networks, with their increased number of connected devices and complex traffic patterns, necessitates advanced security solutions that can detect and respond to evolving cyberattacks. Literature Review: Traditional Intrusion Detection Systems (IDS), including signature based and anomaly based methods, are not equipped to handle the dynamic nature of 5G networks, leading to high false positives and low detection accuracy. In contrast, RL offers significant improvements in adaptability, detection accuracy, and response time. Materials and Method: The study simulates 5G network traffic and develops an RL-based IDS using Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) techniques. The performance of the RL-based system is compared to traditional IDS systems, focusing on detection accuracy, false positive rates, and response times. Results and Discussion: The RL-driven IDS demonstrated superior performance, achieving higher detection accuracy (95%) and faster response times (30 milliseconds) compared to traditional methods. However, challenges such as computational cost and model interpretability were identified. The study emphasizes the importance of adaptive learning mechanisms and the integration of RL into Zero Trust Architecture (ZTA) to enhance the security of 5G networks.

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.

Empowerment of Micro, Small, and Medium Enterprises (MSMEs) is a crucial strategy for enhancing business capacity and family economic welfare. This community service activity employed a participatory approach and continuous mentoring to actively involve MSME actors in all stages, from planning, training, to evaluation. The results indicate an increase in MSME actors' understanding of proper business management, systematic financial recording, and effective marketing strategies. Additionally, technical skills improved, including the ability to maintain daily cash books, income-expenditure reports, and manage operational costs and business capital. The impact of this activity is reflected in increased income, family economic stability, and enhanced economic independence of MSME actors. These findings highlight that MSME empowerment through a participatory approach and sustained mentoring contributes to family economic improvement, community economic resilience, and inclusive, sustainable local economic development

Real Work Lecture (KKN) collaborated with Field Experience Practices (PPL) in increasing student potential through English-based learning. English is a world language used to communicate between countries. The ability to speak English must be owned by everyone, especially elementary school children. English-based learning in education is often implemented with less attractive media, so students' interest in learning English is quite low. This article examines the implementation of work programs that meet the needs of Anuban Jitjongrak school community in improving English language skills. Language and facility limitations are a challenge for students in implementing appropriate work programs. The service method carried out KKN-PPL debriefing, observation and interviews, implementation of work programs, and evaluation of activities. As for the target in the implementation of this KKN-PPL are students at Anuban Jitjongrak School.

This study aims to analyze the importance of facilities and infrastructure management in supporting the quality of educational services in educational units. In the context of modern education, facilities and infrastructure not only function as technical support for teaching and learning activities, but also as indicators of the quality of educational institutions. This research uses the library research method by examining various scientific sources, both in the form of journals, books, and relevant research reports. The results of the study show that effective management of facilities and infrastructure includes the process of planning, procurement, utilization, maintenance, and continuous evaluation of educational facilities. Each of these stages has an important role in ensuring the availability of a safe, comfortable, and conducive learning environment for all school residents. In addition, transparent and participatory management can increase a sense of responsibility, care, and efficiency in the use of facilities. The management of facilities and infrastructure based on the needs of students and the development of information technology has proven to be able to strengthen the quality of educational services and support the achievement of learning goals optimally. These findings provide practical implications for schools and policymakers to continue to strengthen data-driven educational facilities management systems and cross-stakeholder collaboration.

This study analyzes and optimizes production systems in the Industry 4.0 context, examining the fundamental shift from centralized, push-based production models to decentralized, adaptive, pull-based approaches. The research employs a mixed-method approach combining comprehensive literature review and multiple case studies across manufacturing sectors. Findings reveal that integration of Internet of Things (IoT), cyber-physical systems, artificial intelligence, and big data analytics enables real-time communication between production components, product personalization, and faster decision-making. Despite significant benefits in efficiency, flexibility, and competitiveness, implementation challenges persist, including high initial investment, employee resistance, technical expertise limitations, and integration complexity. Optimization approaches such as mixed-integer linear programming, digitally-integrated Lean Six Sigma, and digital twin simulations effectively enhance performance indicators including flexibility, reliability, and energy efficiency. The study concludes that successful production system transformation requires an integrated strategy encompassing process engineering, digital competency development, change management, and continuous evaluation to ensure sustainable optimization in the digital era

This study assesses the effectiveness of teacher training programs aimed at supporting the implementation of Indonesia's national curriculum reforms in secondary education. Using a mixed-methods approach, the research evaluates teacher perceptions, classroom practices, and student outcomes in schools across different provinces. Results indicate that well-structured training significantly enhances teacher confidence and curriculum delivery, although challenges in program consistency and support mechanisms remain. Policy implications emphasize the need for continuous professional development and localized support to improve curriculum implementation.

This study explores the outcomes of inclusive education policies for students with disabilities in Indonesian public schools. The study analyzed data from schools implementing inclusion programs and conducted interviews with students, teachers, and administrators. Findings indicate improvements in social integration but also highlight ongoing challenges in resource allocation and teacher preparedness. Recommendations for policymakers include strengthening teacher training, increasing resources, and improving monitoring mechanisms to support effective inclusive education practices.

One of the consequences of poor nutritional management in toddlers is stunting. One nutritious alternative food to prevent stunting at a relatively cheaper price is sweet corn. Sweet corn is increasingly popular and widely consumed because it has a sweeter taste than regular corn (Kartika, 2019). Siswono (2004 in Surtinah, 2013) reported that the sugar content of the master sweet variety is 16 – 18%, close to the sugar content of cane, namely 19%. Sweet corn contains 96 cal Energy, 3.5 g Protein, 1.0 g Fat, 22.8 g Carbohydrates, 3.0 mg Calcium, 111 mg Phosphorus, 0.7 mg Iron, Vitamin A 400 SI, Vitamin B 0, 15 mg, Vitamin C 12.0 mg, and water 72.7 g Iskandar, 2006). This service activity was carried out at the Kampung Jawa Village Hall, Blangkejeren District, Gayo Lues Regency. The parties involved in this activity were lecturers from the Agrotechnology and Forestry study program, PSDKU Gayo Lues, Faculty of Agriculture, Syiah Kuala University as resource persons, as well as the Agrotechnology and Animal Husbandry study program, Faculty of Agriculture, Gunung Leuser University, Aceh Kutacane, village midwives as partners, village heads as facilitators and the village community. Javanese Village as a participant. Survey of activity locations, socialization of service activities, implementation of service activities, evaluation of service activities. The conclusions from this activity are: 1. Regarding the causes, characteristics, dangers and prevention of stunting by related parties, this is one of the first steps in efforts to improve the nutrition of pregnant women and toddlers in the Kampung Jawa Village community. 2. Sweet corn is an alternative food source that can be consumed by pregnant women and toddlers to prevent stunting. 3. This activity attracts the attention of mothers participating in socialization and Posyandu in Kampung Jawa Village and can increase their knowledge about preventing and handling stunting.

This research aims to improve reading literacy using Antonin board media in class 2 of SDS NU Kencong in the 2022/2023 academic year. The method used is to carry out community service through 5 stages, namely observation, formulating problems, creating materials, carrying out counseling and evaluation. As a result of the counseling using antonym board media, students became active and enthusiastic in participating in learning, but there were some students who had difficulty reading due to their lack of familiarity with reading. This antonym board media makes the learning process fun and interesting. This is an innovation to increase students' interest in reading literacy. In the future, it is hoped that there will be further innovation regarding learning media with the target of teachers being able to help students enjoy reading.

Adolescence is very important to pay attention to because it is a transition period between children and adults, where physically they will experience specific changes. Adolescents must receive balanced nutritional intake according to their needs in order to achieve optimal growth and development and avoid nutritional problems. Nutritional problems that can occur in adolescents are malnutrition, chronic lack of energy (KEK), obesity and anemia (Sandra, Ahmad, & Arinda, 2017). An imbalance between the intake of food consumed causes problems of undernutrition and overnutrition. Malnutrition in adolescents results in decreased body resistance to disease, increased disease rates, abnormal growth, low intelligence levels, low productivity and stunted growth of reproductive organs in adolescents (Nurfiriani & Ratih, 2023). The aim of this service activity is to increase teenagers' knowledge about the importance of nutrition for teenagers, to support the growth and development of teenagers, to prevent the risk of nutritional problems and non-communicable diseases, so it is necessary to carry out health promotion efforts in the form of providing health education and evaluations to increase knowledge about nutrition so that it can increase adolescent awareness in maintaining health.

Ensuring the preservation of human rights and the coherence of law enforcement's tasks are predicated on the pre-trial provisions of the criminal procedure legislation. As part of its mission to ensure that the criminal justice system is fair, the pretrial facility has put safeguards in place to monitor the effectiveness with which police officers carry out their tasks. All eyes are on the judge's evaluation of the law's applicability in light of pre-trial judgment 4/Pid.Pra/2022/PN.Kla. Normative and empirical theories of law are both used in this study's methodology. Primary data collected from the field and secondary data collected from various relevant legal sources are both used in this study. The study team included a law school professor, a prosecutor from the South Lampung District Prosecutor's office, and a judge from the Kalianda District Court. The court considered the following factors in rendering pretrial decision 4/Pid.Pra/2022/PN.Kla, in accordance with the study's conclusions: Instead of using the seizure reports and records that initially connected the suspect to the South Lampung District Prosecutor's Office ( Respondent) as evidence, the judgment was based on legal, social, and philosophical factors. Beyond that, the applicant was not suspected of being the subject of an official audit by the State Financial Loss Calculation Audit (PKKN). In order to establish a monetary loss to the state, the investigator must explicitly recognize a critical component that forms the foundation or risk of the inquiry. All of the applicant's pretrial requests were therefore granted by the court. The officials tasked with carrying out the pretrial decision's enforcement have finished their task. Kla is based on the multi-stage execution of criminal legislation.

This case study investigates the implementation of Occupational Health and Safety (K3) in the PT Gunbuster Nickel Industry area. Through in-depth analysis of policies, practices, challenges, strategies and impacts of K3 implementation, this research aims to provide comprehensive insight into the company's efforts to ensure a safe and healthy work environment for its workers. The research results show that PT Gunbuster Nickel Industry has adopted various comprehensive K3 policies and practices, including routine K3 training, work environment monitoring, and safety campaigns. However, several challenges such as employee resistance and complex working conditions remain obstacles in implementing K3. To overcome these challenges, the company has implemented proactive strategies, including increasing K3 communication and awareness, regular evaluation of working environment conditions, and active involvement of employees in efforts to prevent work-related accidents and diseases. The impact of implementing effective K3 policies at PT Gunbuster Nickel Industry includes reducing the number of work accidents, increasing productivity, and improving the company's image. This research makes an important contribution to the understanding of best practices in K3 management in the industrial sector, as well as becoming a reference for other companies in their efforts to create a safe and healthy work environment.