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Road improvement projects require consistency between technical planning documents and field implementation to ensure that service quality, structural performance, construction time, and user safety are achieved. This article evaluates the conformity between planning and realization in the Pangkalan-Batas Jambi road improvement project in Rawas Ulu District, North Musi Rawas Regency. The study used a descriptive-quantitative evaluative approach based on field monitoring, project technical documents, comparative quantity analysis between contract and Contract Change Order (CCO), and identification of implementation constraints. The results showed that several work items changed during construction because initial planning data did not fully capture actual field conditions. Quantities increased for drainage excavation (+18.87%), roadbed preparation (+8.99%), aggregate class A base course (+12.51%), and AC-Base (+11.98%). Conversely, reinforced pipe culverts decreased (-58.33%), unsealed aggregate base/sirtu decreased (-43.50%), and tack/prime coat volume slightly decreased (-0.47%). The time schedule showed a minor delay of -0.13% in the first week but recovered in the second week, indicating effective field coordination. Main constraints included traffic interference, limited worker visibility, and equipment visibility. The study recommends more detailed site surveys during planning, stronger traffic management, stricter occupational safety implementation, systematic progress control, and routine post-construction maintenance to maintain road service life.

Recent Internet of Things (IoT) intrusion detection studies have reported near-perfect benchmark performance for Distributed Denial of Service (DDoS) detection, yet limited attention has been given to understanding how different traffic representations contribute to the detection process under highly imbalanced traffic conditions. This study presents an ablation-driven analysis to investigate the contribution of statistical and temporal representations for large-scale IoT DDoS detection using the CICIoT2023 dataset. Three experimental scenarios are evaluated, including statistical representation, temporal sequence representation, and hybrid statistical–temporal representation. Temporal representations are learned using a one-dimensional Convolutional Neural Network (1D-CNN) with lag-based traffic sequences, while ensemble tree-based classifiers are employed for final classification and representation analysis. In addition, multiple ablation configurations are designed to evaluate the impact of temporal dependency modeling and feature engineering strategies on detection performance. Experimental results show that statistical traffic representations remain highly effective for DDoS detection on CICIoT2023, achieving 99.36% accuracy and 99.31% weighted F1-score in the statistical representation scenario. Feature importance analysis further indicates that engineered statistical features contribute substantially more to the classification process than CNN-based temporal representations. Although temporal modeling captures sequential traffic behavior, its contribution is relatively limited and mainly acts as a complementary representation. Furthermore, the hybrid configuration produces only marginal improvements over the statistical representation alone. These findings highlight the importance of representation-level analysis for understanding the actual contribution of statistical and temporal modeling in modern IoT intrusion detection systems beyond relying solely on benchmark accuracy.

. A website is a crucial digital promotion medium for companies to convey information and attract potential customers. However, many websites experience low user interaction despite having high visitor traffic. This study aims to analyze the effectiveness of A/B Testing implementation in improving the performance of the PT Pamor Putra Mandiri website. A quantitative experimental approach was used, applying A/B Testing to two page variations: Variation 1 (Rafting & Outdoor Activity theme) and Variation 2 (Camping & Accommodation theme). The evaluation focused on three main performance metrics: Click, Page Visit, and Engagement. The results show that Variation 1 consistently outperformed Variation 2 across all metrics. Statistical testing using a two-proportion z-test confirmed that these differences were statistically significant (p-value < 0.05). Therefore, A/B Testing proves to be an effective method in identifying superior content strategies and page structures, as well as a relevant tool for data-driven decision-making in corporate website development.

Population growth and socio-economic activities increase traffic volume, affecting the performance of the Raden Kusno – A. Djaelani – Sujarwo signalized intersection. This study aims to analyze the intersection’s current performance, estimate its condition over the next five years, and formulate alternative treatment strategies. The research data include geometric characteristics, signal timing, vehicle speed, and traffic volume obtained from CCTV recording over a three-day observation period from moning to evening, as well as population and vehicle data for projection. The intersection performance was analyzed using the PKJI 2023 approach and VISSIM simulation. Under current conditions, the intersection operates at LOS E with delays of 45,12 seconds (PKJI 2023) dan 60,56 seconds (VISSIM). In the five-year projection, delays increase to 48,97 seconds with LOS E (PKJI 2023) and 131,29 seconds with LOS F (VISSIM). Modifying the signal from four to three phases with a 70-second cycle improves the current condition to LOS C, with delays of 24,50 seconds (PKJI 2023) and 29,43 seconds (VISSIM). For the five-year projection, adding a continuous left-turn lane results in LOS D with 27,04 seconds (PKJI 2023) and LOS C with 32,01 seconds (VISSIM).

The performance of asphalt mixtures is strongly influenced by the composition of their constituent materials, particularly aggregate size and mixing temperature during production. In many tropical and subtropical regions, asphalt pavements frequently experience rutting, reduced stability, and changes in viscoelastic properties due to high environmental temperatures and heavy traffic loads. These conditions can significantly affect pavement durability, making it essential to produce asphalt mixtures that meet established technical standards. This study aims to analyze the effect of variations in aggregate size distribution and mixing temperature on the Marshall characteristics of Asphalt Concrete Wearing Course (AC-WC) mixtures. The research employs the Marshall test method to evaluate the load-bearing capacity and stability of hot asphalt mixtures and to assess their compliance with ASTM/SNI standards. The results indicate that mixtures with standard aggregate gradation achieve stability values of 985 kg at 120°C, 1055 kg at 140°C, and 1107 kg at 160°C. As mixing temperature increases, flow values decrease, while the Marshall Quotient (MQ) increases, indicating improved stiffness. Higher temperatures also enhance compaction, reducing VIM and VMA while increasing VFA. Conversely, non-standard aggregate gradations result in several parameters failing to meet ASTM/SNI requirements, confirming that standard gradation produces superior asphalt performance.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

This study examines the effectiveness of public service management in Ponorogo Regency's transport sector, focusing on the performance gap between traffic asset conditions (signs, traffic lights) and formal maintenance policies. This gap indicates a non-proactive maintenance cycle, exacerbated by limited resources and low organisational responsiveness to public complaints. Employing a qualitative case study grounded in a synthesis of Edwards III's Policy Implementation Theory and the New Public Service (NPS) perspective, the core findings confirm that frontline officials' low proactive disposition mediates policy implementation failure. Instead of proactive responsiveness (anticipating minor damage), officials often exhibit passive responsiveness (acting only after major incidents or reports), leading to a critical breakdown in which administrative procedures are completed but the public outcome remains poor. The novelty of this research lies in proposing a Proactive and Participatory Governance Model. This model necessitates the institutionalisation of Public Involvement (Participation) to enhance transparency and shift asset performance evaluation from output-oriented to outcome-oriented. The study concludes that the optimal model for the Ponorogo Transportation Department is the synergy between agile asset management and NPS principles (proactive and participatory) to enhance public service effectiveness.

The increasing integration of internet technology in educational institutions requires structured network governance to ensure that digital resources support academic activities effectively. Unrestricted access to online platforms often leads to non-academic usage such as online gaming and social media engagement during instructional hours, which may reduce learning concentration and degrade network performance. This research develops and evaluates a network access control simulation using a MikroTik RouterBoard RB951Ui-2HnD device. The system applies firewall filtering mechanisms, hotspot-based authentication, and bandwidth allocation strategies through Simple Queue configuration. Network segmentation is implemented to differentiate teacher and student access privileges. The study adopts a Research and Development (R&D) approach to design, configure, test, and evaluate the proposed system. Testing results indicate that the firewall configuration successfully restricts access to selected online games (Mobile Legends, Clash of Clans, Roblox) and social media platforms (YouTube, TikTok, Shopee, Instagram, Telegram). Furthermore, bandwidth management demonstrates effective traffic prioritization, ensuring more stable allocation for teacher accounts in accordance with configured maximum limits. The findings confirm that structured firewall and bandwidth policies can improve network discipline, enhance performance stability, and support a controlled digital learning environment in schools.

The increasing complexity and scale of modern network traffic driven by IoT and cloud-based infrastructures have made accurate intrusion detection a critical challenge. Conventional network intrusion detection systems (NIDS) and many deep learning–based approaches struggle to reliably detect minority and stealthy attacks due to severe class imbalance and limited discrimination of subtle traffic patterns. To address these limitations, this study proposes a hybrid CNN–RBF–Attention framework for network intrusion detection. The proposed model integrates three complementary components: (i) a convolutional neural network for hierarchical feature extraction from network flow data, (ii) a radial basis function (RBF) network for localized nonlinear classification using prototype-based decision regions, and (iii) an attention mechanism that adaptively weights RBF activations to emphasize discriminative traffic patterns. SMOTE is applied exclusively to the training data to mitigate class imbalance. The framework is evaluated on the widely used CICIDS2017 and CICIDS2018 benchmark datasets in both binary and multiclass settings, using recall, precision, F1-score, confusion matrices, and ROC analysis. Experimental results demonstrate that the proposed hybrid model consistently outperforms standalone CNN and RBF baselines, particularly in terms of recall and F1-score. On the CICIDS2018 dataset, the model achieves 99.81% accuracy and 99.81% F1-score in binary classification, and 99.54% accuracy and 99.54% F1-score in multiclass classification. On CICIDS2017, it achieves 98.12% accuracy and 98.12% F1-score in binary classification, and 98.92% accuracy and 98.92% F1-score in multiclass classification. Confusion matrix and ROC analyses further show strong class separability and reliable performance in low–false-positive-rate regions, which is critical for real-world IDS deployment. These results confirm that combining deep hierarchical feature learning, localized prototype-based classification, and attention-guided refinement yields a robust, operationally reliable intrusion detection framework for highly imbalanced network environments.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

Software Defined Network is a popular computer network concept today because of the ease of managing network traffic with the control plane. Massive internet usage makes web server services on SDN networks overloaded. There are many load balancing concepts to overcome this problem, one of which is implementing the K-NN algorithm. This study aims to maximize the performance of the K-NN algorithm on SDN networks by optimizing the K value using Grid Search Cross Validation, and adding server status selection logic based on the smallest disk if the server status calculated by K-NN has the same. All implementations of the load balancing concept in this study were created virtually using Open vSwitch and virtualbox. Testing was carried out using CPU, MEMORY, and DISK parameters sent by the server with the help of the psutils component. JMeter software was used for testing by sending data using the POST method. The data type is text/plain with a data size of 1MB, testing was carried out in stages with threads 100, 200, 300, 400. The test results showed that the performance of the K-NN algorithm was running optimally. There was no significant difference in the distribution of the load to the server, this made the optimization and addition of logic successful.

This study aims to develop an Indonesian traffic sign detection system using a transfer learning approach to improve road safety and traffic efficiency. The dataset was obtained from Kaggle and consists of 2,100 images across 21 traffic sign classes. The research stages include data collection, preprocessing to reduce noise and normalize image brightness, object detection using YOLOv5, and classification based on transfer learning with ResNet, VGG-16, and MobileNet architectures. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the YOLOv5 model is capable of detecting traffic sign objects; however, the classification performance remains relatively low, with a mean Average Precision (mAP) value of 0.17. These findings suggest that further optimization is required in data preprocessing, dataset quality, and model parameter tuning to achieve better performance. This study demonstrates that transfer learning has significant potential for developing computer vision-based traffic sign detection systems, although further improvements are necessary to ensure robustness under real-world Indonesian traffic conditions.

This study examines the seismic performance of slender Air Traffic Control (ATC) towers in high‑hazard regions (PGA > 0.4g), where vertical taper, torsional eccentricity, and top‑heavy cab mass can significantly increase drift, base shear, and collapse risk relative to conventional buildings. Existing studies often rely on linear procedures and outdated provisions, leading to underestimation of nonlinear behaviour and limited guidance for ATC towers designed to SNI 1726:2019. The research aims to quantify these irregularity effects and formulate design recommendations that satisfy Immediate Occupancy, Life Safety, and Collapse Prevention performance targets. The methodology couples response spectrum analysis, using a site‑specific Padang spectrum consistent with SNI 1726:2019 and ASCE 7‑16, with nonlinear pushover analysis interpreted through FEMA/ATC performance‑based criteria. A parametric study is performed on three cab configurations small, medium, and large modelled as 5%, 15%, and 25% mass ratios at the tower head, while keeping a 10 m × 10 m hybrid core–frame shaft constant. Results indicate that larger cab mass produces systematic but moderate increases in global displacement, story drift, and base shear, while plastic hinges localize primarily in the upper stories and cab‑support region, yielding performance levels from Immediate Occupancy to Collapse Prevention. Overall, the tower meets code drift limits and acceptable performance if local strengthening is provided around the shaft–cab interface, offering a calibrated reference for top‑heavy ATC tower design in Indonesian high‑seismic settings and identifying priorities for future time‑history and soil–structure interaction studies.

In Surabaya City, the City Government through the Transportation Agency developed the Suroboyo Bus and Trans Semanggi programs to address traffic congestion, limited public transportation, and the need for safe, comfortable, and environmentally friendly transportation. However, complaints are still found regarding limited facilities, irregular schedules, and suboptimal communication services, so that service performance evaluation is needed from the user's perspective. This study aims to analyze the performance of Suroboyo Bus and Trans Semanggi public transportation services at the Surabaya City Transportation Agency using five public service performance indicators according to Dwiyanto et al. (2021), namely productivity, service quality, responsiveness, responsibility, and accountability. The approach used is quantitative with the Importance Performance Analysis (IPA) method. The results of the study show an average expectation score (importance) of 4.18 and a reality score (performance) of 3.86 with an overall gap of -0.32, which means that the performance of Suroboyo Bus and Trans Semanggi services still does not meet public expectations. Through the IPA mapping, three attributes are in Quadrant I (high priority): the friendly and professional attitude of staff, the adequacy of on-board facilities, and the transparency of official information regarding schedules and service changes. A total of 13 attributes are in Quadrant II (maintained), 13 attributes in Quadrant III (low priority), and one attribute in Quadrant IV (excessive).

The increasing intensity of traffic object movement in urban areas has not been accompanied by adequate road infrastructure, resulting in traffic congestion, air pollution, and a higher risk of traffic accidents. One of the primary causes of accidents is traffic violations, particularly wrong-way driving behavior. This study develops a video-based automated traffic violation detection system using the YOLOv5 algorithm. A computer vision approach is employed to detect, classify traffic objects, and count wrong-way violations in real time. Due to limited access to real-world traffic violation footage, simulated traffic scenarios are used as testing data. The system is evaluated on four traffic object classes: motorcycles, cars, buses, and trucks. Experimental results demonstrate strong performance, achieving a precision of 90%, a recall of 92%, and an F1-score of 91%, while the traffic object counting accuracy reaches 89%. These findings indicate that the proposed system has significant potential to support traffic analysis and assist authorities in making more effective decisions to reduce congestion and traffic accidents.

Pemda Road in Kapur Village, Sungai Raya District, Kubu Raya Regency, West Kalimantan is a district road that functions as an alternative route connecting Tanjung Raya II Road and Kapur Village Main Road. Along with increasing traffic loads and limited maintenance, this road section has experienced various surface pavement distresses that reduce driving comfort and compromise road safety. This paper seeks to detect the types and severity levels of pavement damage and to determine appropriate preservation actions using the Pavement Condition Index (PCI) method. The research method involved a visual field survey by dividing the road into several segments, identifying damage types and severity levels, calculating PCI values, and determining suitable preservation measures based on the Asphalt Institute MS-17 guidelines. The results indicate that the flexible pavement section has a PCI value of 21.643, classified as very poor, with dominant damages consisting of potholes and edge cracking. Meanwhile, the rigid pavement section shows a PCI value of 94.960, which falls into the excellent category. The results of this research are anticipated to facilitate decision-making in determining maintenance priorities and road preservation strategies to improve pavement performance and extend service life.

This study aims to analyze the differences in motorcycle traffic flow (Q) during the morning and afternoon peak hours as an indicator of roadway operational performance, referring to the Indonesian Road Capacity Guidelines (PKJI) 2014, with a case study on Mawar Street–Wijaya Kusuma Street, Jember Regency. The research data were obtained from 12 observation points through traffic surveys that recorded motorcycle traffic flow in vehicles per hour (veh/h). The analytical methods used include descriptive statistical analysis, normality testing, and paired sample t-test. The results show that the average motorcycle traffic flow during the morning peak hour is 115.58 veh/h with a standard deviation of 62.97, while during the afternoon peak hour it is 63.25 veh/h with a standard deviation of 28.57. The paired sample t-test yields a significance value of 0.015 (p < 0.05), indicating a statistically significant difference between morning and afternoon traffic flows. These findings suggest that the level of roadway capacity utilization is higher during the morning peak hour, which is closely associated with dominant routine travel activities such as commuting to work and school. The results of this study are expected to serve as a basis for evaluating roadway operational performance and to support traffic management and traffic engineering planning aimed at improving road network performance and reducing congestion.

This study investigates the integration of high performance communication protocols with adaptive signal processing engines in multi-core systems, aiming to enhance scalability, throughput, and inter-core communication efficiency. The challenges inherent in traditional multi core architectures, such as communication overhead, latency, and scalability limitations, are addressed through the incorporation of Network-on-Chip (NoC) architectures and adaptive signal processing techniques. By using a multi-core digital signal processing (DSP) platform, the study evaluates the performance improvements achieved by this integration under varying workloads and core configurations. The experimental results show a 35% improvement in throughput and a 25% reduction in communication latency, highlighting the effectiveness of adaptive communication protocols in managing data traffic between cores and reducing bottlenecks. The integration of NoC architecture facilitates parallel data transfers, while adaptive signal processing engines ensure that data flows more efficiently across the cores, enhancing system responsiveness, especially under high data rate conditions. Furthermore, the study explores the scalability of the proposed system, demonstrating its ability to maintain high performance as core counts increase. The findings emphasize the potential of combining advanced communication protocols with adaptive signal processing for optimizing multi-core system performance. Practical implications of this research include the design of scalable, flexible, and efficient multi core architectures suitable for complex, data-intensive applications. Future research should focus on further refining communication protocols and exploring additional integration strategies to enhance the adaptability and scalability of multi-core systems in next-generation computing environments.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.