Publication Search

The advent of 5G technology has marked a significant shift in the telecommunications industry, offering transformative improvements in service speed, latency, and network reliability. This study explores the impact of 5G on operational efficiency and service innovation in telecom companies. By examining the operational performance of three leading telecom companies that have implemented 5G networks, the research identifies key improvements in speed, cost reduction, and resource optimization. The findings highlight that 5G has enabled companies to achieve up to 100 times faster data transfer speeds compared to previous generations, drastically reducing latency and enhancing network reliability. These improvements contribute to increased customer satisfaction, faster response times, and reduced operational costs. Additionally, the integration of artificial intelligence (AI) for network management has optimized resource allocation and further enhanced the efficiency of telecom operations. The research also demonstrates how 5G has driven innovation in service offerings, such as enabling smart cities, IoT integrations, autonomous vehicles, and real-time patient monitoring in healthcare. While the deployment of 5G offers numerous benefits, the study acknowledges challenges such as high infrastructure costs, digital inequality, and regulatory hurdles. Telecom companies must invest significantly in infrastructure and navigate complex regulatory environments to fully realize the potential of 5G. The study concludes that 5G technology has the potential to reshape the telecom sector, fostering greater competitiveness, service quality, and innovation. Future research should focus on the long-term impact of 5G on customer loyalty, its expanded applications, and its role in advancing future technologies such as 6G.

In the digital age, public road lighting (PJU) monitoring efficiency is becoming essential for effective infrastructure management. The research developed a PJU monitoring system based on LoRa technology and an Arduino microcontroller to monitor the operating conditions of PJU solar panels in real time. The system uses the LoRa 433 MHz module for remote data communication and is equipped with a voltage sensor to monitor batteries and solar panels as well as an ACS712 current sensor to measure current consumption on LED lights. The data is displayed on an I2C 16x2 LCD screen, making monitoring easy. LoRa technology offers the advantages of broad communication range and low power consumption. The development method used is prototyping, including needs analysis, system design, implementation, testing, and maintenance. Test results show that the system works well, with sensors providing adequate accuracy and LoRa communication enabling remote data access. The system improves the efficiency and accuracy of PJU monitoring, as well as reduces time and effort in the monitoring process. Overall, the system is an effective solution for PJU management in the digital age.

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.

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

This study presents the development of a smart grid system designed to efficiently integrate renewable energy sources into the existing electrical grid. The proposed system employs advanced communication technologies and real-time data analytics to optimize energy distribution and consumption. A simulation model was created to evaluate the system's performance under various scenarios, demonstrating significant improvements in energy efficiency and reliability. The findings indicate that the smart grid system can enhance the stability of the electrical network while promoting the use of sustainable energy sources.

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.

This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.

Hospitals, as institutions providing healthcare services, must maximize technological advancements. One such advancement is the use of SIMRS (Hospital Management Information System) in accordance with the Indonesian Minister of Health Regulation No. 82 of 2013. The use of SIMRS is predicted to enhance the hospital's efficiency, effectiveness, professionalism, performance, as well as access and services. Nurses' satisfaction in using SIMRS will impact the improvement of patient care quality. One method to evaluate user satisfaction with SIMRS is EUCS (End User Computing Satisfaction), developed by Doll and Torkzadeh, which consists of five dimensions: content, format, accuracy, ease of use, and timeliness. This study aims to determine the relationship between the application of the EUCS method and nurses' job satisfaction in the use of SIMRS at RSUD dr. Doris Sylvanus Palangka Raya. Methods: This research is correlational, employing a cross-sectional design. Respondents were selected using purposive sampling and the chi-square statistical test was used. The sample consisted of 175 inpatient nurses at RSUD dr. Doris Sylvanus Palangka Raya. Results: The chi-square statistical test showed a p-value = 0.000, or a significance level of p < 0.05, indicating that there is a relationship between the application of the EUCS method and nurses' job satisfaction in the use of SIMRS at RSUD dr. Doris Sylvanus Palangka Raya. Conclusion: Effectiveness and efficiency are the primary goals of SIMRS usage, which creates convenience and transparency, ultimately expected to enhance nurses' job satisfaction.

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

The rapid growth of big data has significantly increased the demand for efficient and scalable data processing methods, particularly within cloud computing environments. This study aims to evaluate the effectiveness of distributed computing frameworks, specifically Apache Hadoop and Apache Spark, in optimizing big data processing. A qualitative approach using a Systematic Literature Review (SLR) method is employed to analyze existing studies related to distributed systems, cloud computing architectures, and performance optimization techniques. The analysis focuses on key performance indicators, including processing speed, resource utilization, and scalability, as well as the suitability of each framework for different data processing scenarios. The findings indicate that Apache Hadoop is highly effective for batch processing and storage-intensive tasks due to its disk-based architecture, while Apache Spark demonstrates superior performance in real-time and iterative processing through its in-memory computing capabilities. Additionally, system configuration factors such as cluster size, memory allocation, and network bandwidth are identified as critical elements influencing overall performance. The study also highlights emerging trends, including the adoption of hybrid cloud environments, the integration of artificial intelligence and machine learning, and the utilization of edge computing to enhance real-time data processing. In conclusion, distributed computing frameworks play a vital role in improving the efficiency and scalability of big data processing in cloud environments. The selection of an appropriate framework, combined with optimized system configuration, can significantly enhance operational performance and support data-driven decision-making.

Applying ergonomic principles in work tool design for manufacturing industries is a crucial factor in improving productivity while maintaining worker health. This research aims to analyze the effectiveness of adaptive work tool design models based on cognitive and physiological ergonomic principles, identify interaction patterns between workstation design and operational performance, and develop a conceptual framework for integrating ergonomic principles into production cycles. The research method adopts a cognitive-physiological approach with qualitative analysis of human-machine interactions, biomechanical simulations using digital human modeling, and muscle load measurements through electromyography. Implementation was conducted using a participatory ergonomics approach and IMU sensor-based real-time monitoring systems. Results show that using materials with controlled deformation capabilities (15-20%) in work tools reduces muscle work by up to 27%, while adaptive automation system integration improves assembly accuracy by 18%. Workstations with ergonomic adjustments increase assembly speed by an average of 12%, and low-cost ergonomic interventions effectively improve productivity by 11-15% in resource-limited environments. Longitudinal analysis reveals that evidence-based ergonomic investments yield a 230% ROI through increased productivity, reduced injury compensation costs, and decreased employee turnover. IMU-based posture monitoring systems integrated with adaptive feedback loops reduced musculoskeletal disorder incidents by up to 41%. In conclusion, ergonomic optimization based on cognitive-physiological principles creates synergy between production efficiency and worker well-being, making it an essential component in achieving sustainable productivity.

This study aims to evaluate the impact of bureaucratic reform in Bima Regency following the issuance of the Ministerial Regulation on Administrative Reform (PermenPAN-RB) Number 3 of 2023, with a primary focus on enhancing the quality of public services and the efficiency of administrative processes through the implementation of information technology. Utilizing a qualitative descriptive research method, this study explores the perceptions and experiences of civil servants (ASN), policymakers, and the civilian community in Bima Regency. Data were collected through in-depth interviews, participatory observation, and document analysis to understand the implementation and effectiveness of the reforms undertaken. The findings indicate that bureaucratic reform in Bima Regency has made significant progress in improving the efficiency and effectiveness of public services, underpinned by the Bureaucratic Reform (RB) Index. The main focus of this reform is to accelerate service delivery, enhance user satisfaction, and reduce unnecessary bureaucracy, with the digitalization of services as a key step that has successfully reduced waiting times and increased transparency. Training programs for civil servants have also been enhanced to ensure high-quality services that meet the needs of the community. The roadmap for bureaucratic reform also includes efforts to strengthen integrity and transparency, with an emphasis on improving the Corruption Perception Index (CPI) score through the strengthening of oversight institutions and the implementation of policies that limit direct interactions, reducing opportunities for corruption. Furthermore, Bima Regency continues to innovate by integrating information technology into government administration to improve the Government Effectiveness Index (GEI) and E-Government Development Index (EGDI), ensuring data security, and expanding public access to government services, especially in remote areas.

This research aims to identify the issues faced by one of the paint distributors in managing their sales information system. The study notes that despite the store's successful sales activities, the utilization of the sales information system remains limited, with the store preferring to manage products without online information handling applications. Based on observations, some weaknesses of the current system include lack of stock data accuracy, hindrances in providing real-time stock information, and limitations in tracking stock changes. The author suggests providing training to users of the web-based sales information system and establishing clear task allocation, as well as system improvements to address existing weaknesses. These suggestions are expected to enhance efficiency and effectiveness in sales management and meet the needs of both administrators and buyers.

Old Age Security (JHT) is an important program in the social security system in Indonesia which is organized by the Employment Social Security Administering Agency (BPJS). Despite being a vital form of social protection, JHT claims management is often in the spotlight due to the various challenges it faces, including effectiveness, efficiency and fairness in disbursement of claims. In this context, this research aims to analyze JHT claims management at the Pratama Rantau Prapat BPJS Employment Branch Office. This study uses a qualitative research method with a case study approach. Data was collected through direct observation, interviews with officers and participants, as well as analysis of documents related to the claims process. The research results show that the JHT claims process faces various challenges, including the long time it takes to complete a claim, a lack of transparency and communication, and gaps in understanding between participants and officers. The conclusion of this research is that significant improvements are needed in JHT claims management. Recommendations for improvement include increasing process efficiency, increasing transparency and communication, and simplifying claims procedures. Implementation of these recommendations is expected to improve the quality of JHT claims services and provide greater benefits for participants in their preparation for a more financially secure retirement.

Management transformation in the shipping industry has become increasingly important with the advancement of information technology. This research aims to analyze the role of information technology in management transformation in the shipping industry. Through a literature review approach, this study explores the use of information technology in enhancing operational efficiency, facilitating faster decision-making, and improving company competitiveness. The main findings indicate that information technology has been a key driver in management transformation, with the adoption of integrated logistics management systems and real-time ship monitoring. However, challenges such as infrastructure complexity, data security issues, and resource constraints are also identified in the implementation of information technology. Effective mitigation strategies are needed to address these challenges. This study also highlights the importance of collaboration and partnerships in the transformation of the shipping industry. Thus, this research provides a comprehensive understanding of the role of information technology in management transformation in the shipping industry, while highlighting the challenges faced and their practical implications.

An Internet of Things (IoT)-based logistics monitoring system is an innovation designed to enhance efficiency, transparency, and speed in supply chain management. The implementation of IoT in logistics provides an ideal solution for real-time monitoring of goods, which was previously difficult with conventional methods. This system integrates IoT sensors such as GPS for location tracking, along with temperature and humidity sensors to monitor goods requiring special attention, such as food and pharmaceuticals. By automating processes through IoT technology, goods distribution becomes faster, reducing reliance on manual controls, speeding up decision-making, and minimising human error. This system enables companies to monitor the condition and location of goods in real-time, offering high visibility into the logistics status. With comprehensive monitoring, companies can proactively address issues and ensure goods remain in optimal condition throughout the shipping process. An IoT-based logistics monitoring system has the potential to enhance a company’s competitiveness through efficiency and accuracy in supply chain management. When effectively implemented, IoT technology can not only improve service quality and customer satisfaction but also strengthen a company’s position in an increasingly competitive market.

This study seeks to create a system for monitoring the environment using the Internet of Things. It aims to help farmers manage their rice fields in a way that is both effective and sustainable. The system will provide farmers with real-time data on soil moisture, air temperature, and rainfall, allowing them to make informed decisions. Sensors will collect this information, which will be analyzed and presented simply through a web or mobile app. Early simulations suggest that this system could boost crop yields by as much as 15% and cut water use and costs by 20%. These findings point to the IoT monitoring system as a practical means to enhance rice farming's efficiency and productivity. Yet, there are hurdles. Issues like poor network infrastructure, high costs of implementation, and the farmers' ability to adapt to this technology need to be overcome for the system to work properly. This study aspires to make a real difference in promoting sustainable farming practices while paving the way for more advanced IoT solutions in the future.

Recognizing outstanding students is an essential strategy to encourage learners to improve their academic performance and overall learning quality. However, the process of selecting the best students at SMK Negeri 1 Raman Utara still encounters significant challenges due to the complexity of the evaluation criteria, which include academic achievement, attendance, participation in extracurricular activities, and student behavior. The current manual selection method is considered less effective because it is time-consuming and susceptible to subjectivity in decision-making. This study aims to address these issues by developing a technology-based Decision Support System utilizing the Simple Additive Weighting (SAW) method. SAW was chosen for its efficiency in solving multi-criteria problems through matrix normalization and preference weighting for each alternative. The development process includes requirement analysis, system design, and the implementation of an automated ranking mechanism for student candidates. Based on the testing results, the implemented system can process assessment data rapidly and produce accurate and objective rankings of high-achieving students. The system offers a practical solution for the school by enhancing transparency and validity in the selection process, minimizing human calculation errors, and supporting more equitable and data-driven decision-making.

The advancement of Artificial Intelligence (AI) technology has significantly influenced guidance and counseling services, particularly through the use of chatbots, e-counseling, and digital-based psychological analysis systems. This study aims to analyze the collaboration between AI technology and counselors’ interpersonal skills within the context of Islamic counseling. The research employed a qualitative approach using a library research method. Data sources were obtained from various relevant journals and scientific articles accessed through Google Scholar. Documentation techniques were applied for data collection, while the data were analyzed using descriptive-analytical and thematic approaches. The findings reveal that AI can enhance counseling services through greater efficiency, effectiveness, and accessibility without limitations of time and place. In addition, AI assists counselors in managing data, identifying clients’ problems, and providing quick initial responses. Nevertheless, AI cannot fully replace the role of counselors because it still lacks the ability to deeply understand empathy, build interpersonal relationships, provide emotional support, and comprehend spiritual values that are essential in Islamic counseling. Therefore, the implementation of hybrid counseling, which combines AI technology with human counselors, is considered more appropriate. In this model, AI functions as a supporting tool or co-counselor, while counselors continue to play the primary role in establishing therapeutic, humanistic, and religious relationships with clients. This study emphasizes that the integration of AI technology and counselors’ interpersonal skills is an important strategy in creating modern, effective, and human-oriented counseling services.

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.