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Law enforcement of road transportation traffic is one of the important aspects in maintaining road safety and order. However, the effectiveness of monitoring and evaluating the performance of Civil Servant Investigators (PPNS) in law enforcement is often hampered by unstructured processes and lack of transparency in reporting. This research aims to analyze the role of information technology (IT)-based system development in improving transparency, efficiency, and accountability in monitoring and evaluating PPNS performance. This research uses a qualitative method with a case study approach, which involves analyzing the application of information systems in institutions that handle traffic law enforcement. The results showed that IT systems can improve transparency by providing clear and real-time data access, increase efficiency by speeding up data processing and analysis, and strengthen accountability through auditable digital records. The study concludes that the development and implementation of IT systems in the oversight of PPNS performance not only improves the effectiveness of traffic law enforcement, but also strengthens the integrity and public trust in the institutions that carry out these tasks. The implementation of IT systems is expected to be a strategic solution in improving the quality of public services in the field of traffic law.

This research aims to address the issue of uneven bandwidth distribution in large organizational networks by implementing Quality of Service (QoS) using FIFO and the Hierarchical Token Bucket (HTB) algorithm on Mikrotik routers. Uneven bandwidth distribution can disrupt productivity and operational efficiency. This study creates a fair and efficient traffic management system, allowing bandwidth allocation according to user needs. The methodology involves detailed configuration of Mikrotik RouterOS to optimize QoS with adjusted HTB settings. Testing was conducted using IPerf3 to measure bandwidth variations received by clients in different conditions, including scenarios with two and three active clients. The results indicate that the HTB method provides more stable and consistent bandwidth distribution compared to FIFO. In the two-active client scenario, the unused bandwidth by the third client is allocated to higher priority clients, demonstrating HTB's effectiveness in managing traffic priorities. This research is expected to enhance user satisfaction by providing a network that is both stable and responsive to the needs of various operational applications, and contribute significantly to the development of best practices for bandwidth management in complex organizational environments.

Increased activity in the airside area, especially on the apron, causes the emergence of foreign objects such as gravel and oil spills that can disrupt flight safety. Apron Movement Control (AMC) plays a crucial role in regulating aircraft traffic, monitoring the apron area, and maintaining cleanliness from Foreign Object Debris (FOD). This study aims to evaluate the effect of AMC performance in FOD supervision on flight safety at Sentani International Airport. The method used is a quantitative approach with a saturated sampling technique, involving airside officers from airlines, Ground Handling (GH), Ground Support Equipment (GSE), and Marshallers. Data were collected through a closed questionnaire with a 4-point Likert scale and analyzed using validity, reliability, simple linear regression, T-test, and coefficient of determination. The results of the analysis show that AMC performance has a positive and significant effect on flight safety, with a significance value of the T test <0.05. The coefficient of determination (R²) of 0.916 indicates that AMC contributes 91.6% to flight safety, while the rest is influenced by other factors. This finding emphasizes the importance of improving the professionalism and effectiveness of AMC officers' performance.

The detection of computer network attacks is becoming increasingly important as the complexity and frequency of cyber-attacks threatening information systems and network infrastructure continue to rise. These attacks may lead to severe consequences, including data breaches, service disruptions, and financial losses. To address these challenges, artificial intelligence techniques have become a major focus in the development of more effective, adaptive, and reliable intrusion detection systems. Among various classification algorithms, the C4.5 decision tree has demonstrated strong performance due to its simplicity, interpretability, and high classification accuracy. This study aims to apply the C4.5 algorithm for network attack detection using a comprehensive dataset that includes multiple categories of attacks and normal network activities. The proposed methodology consists of several stages, including data preprocessing, feature selection, decision tree model construction, and performance evaluation using standard metrics such as accuracy, precision, recall, and F1-score. Data preprocessing is performed to handle missing values, normalize data, and reduce noise, thereby improving the overall quality of the dataset and enhancing classification results. The experimental results indicate that the C4.5 decision tree algorithm effectively classifies network traffic into attack and normal categories with a satisfactory level of accuracy. The model successfully identifies attack-related patterns and highlights significant features that influence detection performance. Further analysis reveals that appropriate feature selection and parameter tuning significantly contribute to improving model reliability and robustness. This research provides a valuable contribution to the development of efficient, accurate, and practical network intrusion detection systems. The proposed approach is expected to strengthen information security frameworks and support proactive defense strategies against increasingly sophisticated cyber threats, thereby enhancing the protection of critical network infrastructures.

Image classification is widely used in everyday life such as in car steering, closed-circuit television (CCTV), traffic cameras, etc. The implementation of image classification can be done using several methods, including neural network and support vector machine models. The neural network method is able to find the right weights that allow the network to show the desired behaviour while the support vector machine method has many dimensions and can overcome linear and non-linear data. In this research, feature extraction was carried out using VGG16 to increase accuracy. This research aims to find out how to implement the neural network and SVM algorithms to classify images and determine the results of analyzing the performance of the two methods. The data used in this study is secondary data consisting of 10 types of large wild cats with a total of 2339 training image datasets and 50 testing image datasets. The research stages consist of data augmentation, model design, model training, and model evaluation. Classification with the neural network model produced an accuracy of 96% and the support vector machine model produced an accuracy of 96%, which means that in a consistent training environment, the two models have the same performance.

This study aims to analyze the Level of Service (LoS) at the international departure terminal facilities at I Gusti Ngurah Rai Airport, Bali, in accommodating the growth of passenger traffic. Referring to IATA standards, PM 178 of 2015, and PM 41 of 2023, this research evaluates the performance of facilities such as passenger and baggage screening, check-in, immigration, and waiting areas. The research method used is qualitative, with data collected through observation, interviews, and documentation. The results show that the international departure terminal facilities meet the established service standards and are capable of handling passenger congestion during holiday seasons (high season and peak season). Efforts made by airport management include terminal expansion, queue management, and the enhancement of security systems with the latest technology. In conclusion, the Level of Service at I Gusti Ngurah Rai Airport is categorized as good and complies with existing standards, but infrastructure development is still needed to accommodate future increases in passenger numbers.

This research aims to develop a network security system with AdGuard DNS on Debian Linux and control time-based internet access using the Microtik Routerboard. The main challenges are rising network security threats and the need for efficient internet access management in a variety of environments, including families,, education, and offices. The aim of this study is to improve network security and efficiency by blocking harmful content and advertisements, ining user privacy, and regulating internet access at a specific time. Technologies used include DNS AdGuard on Debian Linux to strengthen protection against network security threats and RouterOS Microtik devices to manage internet access based on time. These technologies include content filtering features, network traffic distribution (load balancing), and the ability to block unwanted sites to enhance network performance. Research results show that the combination of AdGuard DNS and   Microtik Routerboard is an effective solution to enhance network security and efficiency. The system is capable of blocking harmful content and advertising, improving user privacy, and adjusting internet access as needed, thus creating a more secure, controlled, and optimal network.

The Raya Blega Street intersection is one of the intersections with a relatively high vehicle volume in Bangkalan Regency. There is no APILL (Traffic Signaling Device) available at this intersection, so it has an impact on traffic flow at the intersection. The existence of the market causes many side obstacles which reduce the optimal performance of the intersection. This condition causes the intersection to experience traffic delays, especially during peak hours such as when leaving work or when leaving. In this research, researchers want to identify the problem of congestion at the Raya Blega street-Rajawali street intersection by calculating and analyzing the performance of the Raya Blega street-Rajawali street intersection using the PKJI 2023 method so that it can be used as a benchmark for assessing the performance of unsignalized intersections and assisting related parties in handle problems at the intersection and it is hoped that they can handle the problems that occur at the intersection so that it can create security and safety for passing users.In this study, the highest value was obtained for existing conditions on Monday, April 29 2024, at morning peak hours (07.00-08.00) at approach point A (east Raya Blega street) with an intersection capacity value of 2297,96 smp/hour and the value obtained The degree of saturation is 0,36 and the delay value is 8.44 sec/smp so it is included in Level of Service B. So the status of the Raya Blega street-Rajawali street Bangkalan Regency intersection is still suitable to be maintained.

The construction of the Parepare City Twin Bridges resulted in increasing vehicle volumes and the occurrence of traffic jams which resulted in obstruction of traffic flow or congestion. The aim of this research is to determine the existing conditions, performance of intersections and road sections and their handling. The type of research used is quantitative, with the data collection method carried out (survey) and the data analysis used is MKJI 1997. The results of this research show that the existing condition of the intersection and road sections in segment 1 is at level of service (LoS) B, segment 2 v/c ratio is at level of service (LoS) A, and segment 3 v/c ratio value is at level of service (LoS) A. The performance of intersections and roads in Segment 1, namely Jl. The peak hour for the Makassar-Parepare axis is 16.00-17.00 (afternoon) Monday, the traffic flow value (Qtot) is 1812.5 pcu/hour, the saturation degree (DS) value is 0.21. Segment 2 is Jl. Gen. M. Yusuf and Jl. Arung Mampi peak hours are 16.00-17.00 (afternoon) on Mondays. The traffic flow value (Qtot) is 262.7 pcu/hour, the degree of saturation (DS) value is 0.05. The handling is installing traffic signs; installation of warning lights or traffic lights.

The high pedestrian activity and traffic flow in the education area on Jalan Raya Abepura-Sentani Jayapura City led to the provision of Pedestrian Bridges (JPO) in two locations, namely in front of the Cenderawasih University Auditorium (JPO-1) and in front of SMP YPPK Santu Paulus (JPO-2). This research was conducted to determine the suitability of the physical geometric standards of the pedestrian bridge and its level of effectiveness, as well as factors that affect pedestrian satisfaction in using the two Pedestrian Bridge (JPO) facilities. Geometric analysis was carried out using the standard of Pedestrian Bridge Planning Procedures for Urban Pedestrians (1995) and Technical Planning of Pedestrian Facilities (2023), calculation of the level of effectiveness using the volume of crossings, and analysis of service satisfaction using the Importance Performance Analysis (IPA) method. The results found that the condition of JPO-2 is better and meets the standards because it uses a slope that can be used by people with disabilities, while the slope on the steps of JPO-1 is too steep (40°) and narrow with a width of 1.4 meters, so the effectiveness level of JPO-2 is higher (67%) than JPO-1 (52%). Factors affecting user satisfaction are aspects of comfort, safety, and security.  The main obstacles in using the pedestrian bridge are dirty conditions, no lighting, the presence of people under the influence of alcohol or extortionists, and damage to the Pedestrian Bridge (JPO) facilities.

The important function of bridges in the transportation system requires bridge performance to always be in prime condition. The reliability of the bridge structure in carrying the traffic load on it is the main parameter to guarantee the performance of a bridge. Bridge performance can always be maintained by regularly checking. This article about the evaluation of existing bridges takes a case study of the Maribaya A Bridge which was built in 1972 and is located on Jalan Bts. Tegal City – Bts. The city of Pemalang, which is part of the Pantura, is known for having a heavy load. The Maribaya A Bridge has a total of three spans, spans 1 and 3 with a length of 15 m, and span 2 with a length of 30 m. The evaluation was carried out on the superstructure, the cast in situ type girder components. The analysis is carried out using the rating factor method by comparing the capacity of the girder with the traffic load above it. The calculation results show that the rating factor value for spans 1 and 3 is 0.40 and for span 2 is 0.19. The rating factor value for the entire span shows <1.00, means that the superstructure capacity of the Maribaya A Bridge is no longer able to withstand the traffic load. Serious attention is needed, bridge reconstruction is absolutely necessary.

This research explores increasing the efficiency of network resource use through load balancing techniques. Load balancing distributes workload evenly among servers to optimize resource usage, maximize throughput, and minimize response time. We compare various load balancing algorithms, such as round-robin, least connections, and least response time, in various network scenarios. Experimental results show that proper load balancing techniques can reduce response time by up to 30% and increase resource usage efficiency by up to 40%. This study highlights the importance of selecting appropriate algorithms based on network traffic and workload characteristics. Implementing effective load balancing strategies can improve the quality of network services and ensure even distribution of workloads, providing practical guidance for network administrators to optimize network performance and efficiency.

Computer networks and the Internet are currently developing very rapidly, especially the role of the internet as a source of information, demanding excellent connection quality. Hotel Puri Indah Mataram, for example, has used two internet service providers (ISP) to handle the surge in demand from customers/guests. When there is an increase in requests, network devices become burdened because they have to process more requests. One solution is to implement load balancing, namely distributing the traffic load to network devices so that it is not centralized on just one ISP. This aims to ensure traffic runs optimally, increase throughput, speed up response time, and prevent overload on one connection line. This technique will optimize network performance by utilizing a two-path connection distribution method known as the NTH method. In addition, a failover technique will be implemented, where if one gateway connection is lost, another gateway will automatically take over all network traffic. This aims to ensure continuous service availability. Finally, there will be bandwidth management for all clients, so that bandwidth distribution becomes more even and efficient.

In the present day, many have used communication tools to share data as text, video, images, or sound and the like. There is a speed of data transmission depending on the delivery method, if there is a system error then the data is likely not to be sent, therefore there are various types of data managers so that there are no obstacles or errors during the data transmission process. The star topology was chosen for its advantages in network management, trouble shooting and providing reliable and easy-to-organize connectivity. The main focus is to evaluate the efficacy and effeciency of the star topology in handling different amounts of data traffic using performance characteristics including packet loss, jitter, delay, and throughput. And the use of switching in the star topology can provide optimal and stable performance under high traffic load can provide reliability and efficiency in data traffic management.

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.

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.

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.

This study explores how public service innovations in motor vehicle testing, based on Regional Public Service Agencies (BLUD), can improve road safety governance. In regions with rapidly growing motor vehicle populations, inadequate roadworthiness controls contribute to high traffic accident risks. While Indonesia has a regulatory framework for motor vehicle testing, service delivery remains largely administrative and lacks a strong focus on road safety. The study aims to assess how public service innovations can enhance road safety governance through local government-managed vehicle testing based on BLUD. Using a qualitative case study approach, data were collected through in-depth interviews, observations, and document analysis involving local government officials, testing technicians, and service users. Thematic and interpretive data analysis identified governance dimensions that influence service effectiveness. The study finds that road safety governance improvement relies on four main capacities: relational, structural, resource, and adaptive. Although BLUD-based management offers flexibility for service innovation, its implementation has not fully aligned with road safety goals. Challenges remain in public trust, cross-agency coordination, resource availability, and organizational learning. The study concludes that integrating public service innovation with institutional capacity building and safety-focused performance management is essential for strengthening road safety governance. These findings highlight the strategic importance of motor vehicle testing in enhancing road safety and public value at the regional level.

Driving in a straight line is one of the fundamental tasks for autonomous vehicles, but it can become complex and challenging, especially when dealing with high-speed highways and dense traffic conditions. This research aims to explore the Deep-Q Networking (DQN) model, which is one of the reinforcement learning (RL) methods, in a highway environment. DQN was chosen due to its proficiency in handling complex data through integrated neural network approximations, making it capable of addressing high-complexity environments. DQN simulations were conducted across four scenarios, allowing the agent to operate at speeds ranging from 60 to nearly 100 km/h. The simulations featured a variable number of vehicles/obstacles, ranging from 20 to 80, and each simulation had a duration of 40 seconds within the Highway-Env simulator. Based on the test results, the DQN method exhibited excellent performance, achieving the highest reward value in the first scenario, 35.6117 out of a maximum of 40, and a success rate of 90.075%.

Measuring the company level of productivity performance is a very important thing to do to see and evaluate achievements and improvements to productivity levels that are not yet optimal. This case study was taken from the leather bag UKM which is currently experiencing a decline in productivity during the 2020 period. With the increase in working hours, the company must increase workers' overtime costs. In this research, the method used to measure the level of company productivity is the objective matrix or OMAX method and this research refers to the OMAX steps, namely determining productivity criteria on the production line, determining weights, determining levels on the objective matrix, calculating the productivity index, and productivity analysis with a traffic light system. The research results show that the average value of overall productivity in 2020 is 4,388 The highest productivity index was achieved in August amounting to 67,01%. From the traffic light system, it is known that the worst indicator of productivity is the defect factor, so improvements need to be made regarding this.