Publication Search

The development of cellular communication technology has reached a significant point with the presence of 4G LTE networks, which offer high speed and spectrum efficiency. This study aims to evaluate the performance of 4G LTE networks from two operators, Telkomsel and Axis, at two locations of Sultan Ageng Tirtayasa University (UNTIRTA), namely Campus A and Campus C. The method used is a drive test with the help of the SpeedOf.me site, which measures download speed, upload speed, and latency parameters. The test results show that Telkomsel excels in upload speed with relatively good latency stability, while Axis has the advantage in the highest download speed but shows large fluctuations in performance, especially in the latency parameter. This performance variation indicates that location factors, network load, and device conditions greatly affect service quality. Overall, Telkomsel shows more stable performance, making it more reliable for network-based academic activities, while Axis is suitable for needs with a priority of download speed in optimal conditions. This study is expected to be a reference for the development of network infrastructure in higher education environments.

This research investigates the impact of corporate governance practices on the financial performance of banks. By analyzing data from various banking institutions, the study reveals that effective governance mechanisms, such as board diversity and regulatory compliance, positively influence profitability and risk management. The results provide insights into how banks can enhance their performance through improved governance structures.

The purpose of this article is to determine whether the principal's leadership style influences teacher performance. This qualitative research employed a literature review method, which examined several national articles. The results indicate that the principal's leadership style has both positive and negative effects on teacher performance in schools. The various leadership styles include transformational leadership, charismatic leadership, transactional leadership, autocratic leadership, bureaucratic leadership, and democratic leadership.  

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 aims to analyze the use of Google Scholar, SINTA, and BIMA platforms in improving the quality and quantity of academic publications as indicators of lecturer performance evaluation at the Ganesha College of Economics (STIE). The research was conducted for three months from February to April 2023. The method used a qualitative approach with a case study involving 67 lecturers of S1 Management, S1 Accounting and S2 Management of STIE Ganesha. Data collection techniques through literature studies, interviews, and observations. Data analysis was carried out using descriptive and comparative analysis techniques. The results showed that 45 permanent lecturers of STIE Ganesha had used the academic platform, namely Google Scholar and SINTA. And 67 other people already have a BIMA account. The obstacles of lecturers include a) Lack of awareness of lecturers in research, b) Research intention and interest are still weak, c) Do not understand how to use academic platforms, d) Technical ability to make research articles is not the same. The solution to anticipate it by: a) Providing training, socialization or technical support for lecturers in the use of academic platforms, b) Providing intensive assistance to lecturers every time in Writing Academic Skills.  The role of the STIE Ganesha Research and Community Service Institute (LPPM) as an institution that handles lecturer research is very important in encouraging an increase in the publication of scientific papers by lecturers. The research limits the scope of the use of academic platforms for STIE Ganesha lecturers, it is hoped that wider research development can be carried out related to the publication of scientific papers by lecturers. This research contributes to knowledge insights for the general public in the use of academic platform tools. The research results are expected to provide input for educational institutions to improve the lecturer performance evaluation system and provide better support for improving the quality and quantity of lecturers' academic publications.

This research explores the urgency of developing effective compensation strategies as a key factor in increasing employee motivation and performance at PT Hutahaean. Fair and adequate compensation is considered an incentive for employees to make maximum contributions and remain loyal to the company, especially in an increasingly competitive labor market. This research aims to formulate a compensation program design that provides significant incentives and analyze its impact on employee satisfaction levels. A qualitative literature study method was used to detail important findings, including the dominance of financial compensation variables on performance. The findings emphasize the crucial role of a fair and transparent compensation system, and recommendations include regular system updates, increased attention to non-financial compensation, and implementation of compensation fairness. Thus, this research provides concrete and relevant views for PT Hutahaean in improving its compensation strategy.  

This study explores the impact of peer-mentoring programs on vocational high school (SMK) students, focusing on academic resilience, dropout rates, and overall academic performance. The research compares the outcomes of students in schools with and without peer-mentoring programs. The findings indicate that students participating in peer mentoring exhibited significantly lower dropout rates (12%) compared to those in the control group (22%). Additionally, the continuation rate for students in the experimental group was higher (88%) than in the control group (78%). Academic performance was also positively affected, with an average score of 75 for the experimental group, compared to 65 for the control group. Statistical analysis confirmed the significance of these differences, highlighting the positive effects of peer-mentoring programs. The study suggests that peer mentoring not only enhances academic performance but also contributes to improved student retention by providing emotional and social support. The results underscore the importance of well-structured mentoring programs, including effective mentor training and proper matching between mentors and mentees, to maximize the benefits for vocational students.

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.

This article examines the dynamics, challenges, and opportunities in implementing the merit system policy within the context of Indonesia’s bureaucratic reform, employing a narrative literature review approach using a triangulation of scholarly sources. Thematic analysis identifies five critical themes that hinder the implementation of the merit system: structural tensions between political patronage and meritocracy that create a hybrid spoils–merit system; significant institutional capacity gaps between central and local governments; digital transformation through platforms such as CAT, SmartASN, and SIPINTER as catalysts for transparency and objectivity; organizational cultural resistance rooted in patrimonial and seniority values; and the weakening of independent oversight exacerbated by the dissolution of KASN’s mandate. Although a progressive regulatory framework has been established through Law No. 5 of 2014, the implementation of the merit system remains constrained by persistent clientelism, limited institutional capacity, and cultural resistance. This study contributes to the theoretical discourse on Weberian bureaucracy, New Public Management, and good governance by confirming that the implementation of the merit system in developing countries constitutes a political–cultural transformation that requires fundamental changes in political incentive structures, institutional capacity, and organisational values. Managerial implications include strengthening independent oversight, making substantial investments in institutional capacity, accelerating inclusive digital transformation, and implementing systematic change management programs to expedite the transition toward a performance-based bureaucracy that is professional and integrity-driven in support of Indonesia’s Golden Vision 2045

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.

The rapid growth of social media platforms has generated massive volumes of unstructured textual data containing valuable information about public opinions and sentiments. Extracting meaningful insights from this data has become increasingly important for decision-making in various domains, including business, politics, and social analysis. This study aims to evaluate the effectiveness of deep learning techniques for sentiment analysis of social media data, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model. A quantitative experimental approach is employed, where datasets are preprocessed through text cleaning, tokenization, and feature representation using word embeddings. The models are trained and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that all models perform effectively in sentiment classification tasks, with the hybrid CNN-LSTM model achieving the highest performance due to its ability to capture both local textual features and long-term contextual dependencies. This demonstrates that combining CNN and LSTM architectures enhances classification accuracy compared to individual models. Furthermore, the findings confirm that deep learning approaches are more robust in handling the complexity and noisiness of social media data compared to traditional methods. This study contributes to the development of more adaptive and accurate sentiment analysis models and highlights the potential of hybrid deep learning architectures for real-world applications.

This study explores the role of educational policy implementation in reducing urban-rural learning disparities through scholarship programs. Educational disparities between urban and rural areas have persisted across many nations, affecting student outcomes due to differences in access to resources, infrastructure, and qualified teachers. Scholarship programs have emerged as an effective policy tool to address these gaps, providing financial support and enabling rural students to pursue higher education opportunities. This research examines the impact of scholarship policies on bridging the urban-rural education divide, focusing on the performance outcomes of scholarship recipients compared to non-recipients. The study also discusses the socio-economic barriers and challenges faced by rural students and evaluates how scholarships, along with additional support mechanisms such as mentorship and academic workshops, contribute to improving academic performance. The findings suggest that while scholarships have a significant positive effect on academic outcomes, a more comprehensive approach, including better infrastructure, teacher quality, and socio-emotional support, is necessary to fully address educational inequities. This research provides insights into how well-designed scholarship programs, embedded within broader educational reforms, can effectively reduce disparities in educational access and outcomes between urban and rural students.

This study aims to analyze the influence of human resource (HR) quality on the implementation of internal control systems at PT. Avia Avian. The background of this study is based on the importance of the role of quality HR in supporting the effectiveness of the implementation of internal control systems in companies. An effective internal control system is essential to maintain integrity, efficiency, and compliance with applicable regulations. The purpose of this study is to identify and analyze how HR quality can affect the implementation of internal control systems in companies. The method used in this study is a quantitative approach using a survey of employees in various departments of PT. Avia Avian. The data obtained were analyzed using regression techniques to examine the relationship between HR quality variables and internal control systems. The findings of the study indicate that there is a significant influence between HR quality and the implementation of internal control systems, where the better the quality of HR, the more effective the implementation of internal control systems. The implication of this study is that companies need to pay attention to improving HR quality as a strategic step to strengthen the existing internal control system, which in turn can improve the company's performance and compliance with established standards.Additionally, this research shows that continuous training and employee competency development can strengthen HR quality, which in turn contributes to the effectiveness of the internal control system. Companies that invest in improving HR quality will not only benefit in terms of compliance with regulations but also in enhancing operational efficiency and risk management. Therefore, it is crucial for the management of PT. Avia Avian to continue focusing on HR development to ensure the smooth implementation of internal control systems in accordance with applicable standards. This research contributes to the literature on risk management and internal control by highlighting the crucial role of HR quality in the success of internal control systems.

Penelitian ini bertujuan untuk mengetahui pengaruh efikasi diri, pengembangan karir, dan work-life balance terhadap kinerja pegawai BLU UPTD Trans Semarang. Objek penelitian adalah pegawai BLU UPTD Trans Semarang pada bagian kepegawaian administrasi dengan ukuran sampel 86 pegawai dengan menggunakan teknik stratified proportional sampling dari jumlah penduduk 109 pegawai. Data dalam penelitian ini diperoleh primer melalui penyebaran kuesioner (angket). Metode analisis data yang digunakan dalam penelitian ini adalah teknik analisis Structural Equation Modeling (SEM) dengan software SmartPLS versi 3. Hasil penelitian menunjukkan bahwa variabel efikasi diri, pengembangan karir, dan work-life balance berpengaruh positif dan signifikan terhadap kinerja karyawan. This study aims to determine the effect of self-efficacy, career development, and work-life balance on the performance of BLU UPTD Trans Semarang employees. The object of the research was BLU UPTD Trans Semarang employees in the administrative staff section with a sample size of 86 employees using stratified proportional sampling technique from a population of 109 employees. The data in this study were obtained primary through the distribution of questionnaires (questionnaires). The data analysis method used in this research is Structural Equation Modeling (SEM) analysis technique with SmartPLS software version 3. The results showed that the variables of self-efficacy, career development, and work-life balance have a positive and significant influence on employee performance.

The existence of the Manpower and Transmigration Office in Central Java Province as an implementing element of government duties in the employment and transmigration sector certainly does not always run smoothly. One of the main issues that may be faced by the Manpower and Transmigration Office is the existence of a high administrative burden. The research method used is an empirical juridical approach using a combination of primary data and secondary data. Primary data is obtained through interviews and face-to-face meetings with resource persons relevant to the research context, while secondary data is obtained through document analysis of literature, policies, and related documents. The analytical approach used in this research is qualitative analysis, which allows for understanding and detailing qualitative aspects based on the issues studied. This study found that the administrative burden in the Central Java Provincial Manpower and Transmigration Office can include various things, such as the preparation of technical policies in the field of employment, community empowerment and transmigration, the implementation of government affairs and public services in the field of employment, guidance and implementation of tasks in the field of employment, and reporting the results of the implementation of main tasks and functions. And to deal with this burden most government programs, especially those managed by the Central Java Provincial Manpower and Transmigration Office, use online platforms and electronic media to process various aspects of employment. This effort helps in processing community reports more easily, quickly, and efficiently, so as to increase the effectiveness of the performance of Central Java Provincial Disnakertrans employees.   Keywords : Administrative burden, Manpower and Transmigration Office, Central Java Provincial

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.

Autonomous vehicles (AVs) are revolutionizing transportation by relying on advanced AI techniques like deep learning and reinforcement learning for decision-making and navigation. However, concerns about the opacity of traditional AI models in safety-critical applications such as autonomous driving raise issues related to safety, accountability, and trust. This study explores the integration of Explainable AI (XAI) techniques in AV systems to enhance transparency and interpretability while maintaining high prediction accuracy. XAI methods, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive ExPlanations), provide understandable justifications for AI-driven decisions, addressing biases, fairness, and accountability. These techniques also support regulatory compliance and foster public trust in AVs. A mixed-methods approach, combining experimental simulations and user surveys, was employed to integrate XAI into AV systems and test its performance in urban traffic and highway driving scenarios. Feedback from users, collected through questionnaires and in-depth interviews, revealed that XAI-enhanced systems significantly improved the interpretability of AV decisions, leading to higher user trust and satisfaction. The study highlights the importance of balancing model complexity with interpretability, demonstrating that XAI techniques are crucial for building trust and ensuring accountability in autonomous driving systems.

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.

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.

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.