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Online maps applications have become an essential tool for modern society in finding the fastest and most efficient routes. However, these applications often fail to detect current road conditions such as flooding, demonstrations, accidents, or public events, causing users to get stuck in problematic routes. This study aims to develop a prototype of a community-based road condition reporting system, visualized through a web-based digital map. The system allows users to directly report road conditions by providing photo evidence, descriptions, and event categories. It is also equipped with features for designing event routes such as carnivals and suggesting alternative paths based on community reports. The development process was carried out using a simulation-based approach with scenario testing that reflects real field conditions, without involving direct user data. The implementation results show that all core features work properly. The technologies used include Leaflet.js, OpenStreetMap, and the Nominatim geolocation API. This research produces an adaptive community-based GIS model that can be further developed as an intelligent navigation solution at the city scale

The increasing complexity of urban transportation systems demands intelligent and measurable navigation methods. Medan City, the capital of North Sumatra Province, has a dense road network with multiple route options that often confuse road users. Dijkstra's Algorithm, developed by Edsger Wybe Dijkstra in 1959, is a greedy-based computational approach proven effective for solving the shortest path problem on non-negative weighted graphs. This study applies Dijkstra's Algorithm to determine the shortest route from Medan Railway Station to Universitas Negeri Medan (UNIMED). The road network was modeled as an undirected weighted graph with 15 nodes and 16 edges, where edge weights represent actual road distances measured via Google Maps. The graph has a density of 0.152, confirming its sparse graph characteristic. Three alternative routes were identified and analyzed. The algorithm was implemented in Python 3 using the heapq module as a priority queue. Results show that the optimal route is A → B → C → E → F → M → N → O via Jl. M.T. Haryono, Jl. Aipda KS Tubun, Jl. Madong Lubis, and Jl. Prof. H.M. Yamin, with a total distance of 6.64 km. This achieves 99.1% accuracy compared to Google Maps, with a deviation of only 0.06 km. The optimal route is 6.25% more efficient than Alternative Route 1 (7.30 km) and 11.9% more efficient than Alternative Route 2 (7.54 km). The algorithm executes in under 1 millisecond with time complexity O((V+E) log V). These findings confirm Dijkstra's Algorithm as highly effective for medium-scale urban road network optimization.

This research investigates the integration of the creative economy as a catalyst for sustainable tourism development within Kutai National Park (KNP), East Kalimantan. Despite its ecological significance as a lowland rainforest and Orangutan habitat, KNP faces stagnation due to a reliance on extractive resources and a lack of diversified tourism products. Employing a qualitative descriptive methodology and SWOT analysis, this study maps the creative potential of the region and evaluates its impact across four sustainability pillars: economic, social, environmental, and institutional. Data were gathered through field observations and in-depth interviews with park authorities, local government officials, and creative entrepreneurs in the buffer zones of Sangatta and Bontang. The findings reveal significant potential in non-timber forest-based culinary arts, modernized ethnic crafts, and digital wildlife storytelling. A critical institutional barrier identified is the "sectoral ego" between conservation authorities and local governments. The study proposes a strategic shift toward a "Green Creative Hub" model, emphasizing that sound governance and institutional synchronization are essential for balancing conservation with economic empowerment. This research provides a roadmap for policy-makers to transition from extractive-based livelihoods to a knowledge-based service economy that preserves the integrity of protected landscapes.

This research is motivated by the increasing urban activities in Mempawah Hilir District as a Regional Activity Center, which has led to high traffic movement at the intersection of Jl. Raden Kusno – Jl. A. Djaelani – Jl. Raden Sujarwo. The diversity of land use and the meeting of arterial and collector roads create potential traffic conflicts and accident risks. The installation of traffic lights is implemented as a traffic control measure; however, its effectiveness in improving safety and comfort needs further analysis. This study aims to assess traffic safety and comfort conditions after the implementation of traffic lights at the intersection. A quantitative approach was used with primary and secondary data. Primary data were obtained through questionnaires on road users’ perceptions and field observations, while secondary data included traffic accident records from Mempawah Police and spatial development data from Google Earth Pro, Google Maps, Copernicus, and Google Colab. The analysis employed descriptive-comparative, spatial descriptive, and scoring methods. The results show that traffic lights implemented since August 2023 have reduced traffic accidents and improved safety conditions, although not yet optimal compared to the lowest accident rate in 2021. Increased land use activity and traffic volume influence this condition. User perceptions indicate improved comfort internally, but external factors such as road conditions and side obstacles still reduce comfort and safety.

Village regional information plays an important role in supporting community services and effective development planning. However, some rural areas still face limitations in information regarding hamlet boundaries and road access, including Lunto Barat Village, Lembah Segar District, Sawahlunto City. This Community Service Program (KKN) aimed to improve the availability and accessibility of village regional information through hamlet and road mapping activities. The methods applied included field observation, direct road data collection with village officials, preparation of hamlet maps, and environmental development activities through community-based planting. The results indicated that the mapping activities provided clearer information on hamlet boundaries and village road networks, thereby facilitating community access to regional information. Furthermore, the resulting village map helped both residents and visitors locate destinations more effectively. This program demonstrates that village mapping activities not only enhance access to regional information but also contribute to more structured and participatory village development.

Village regional information plays an important role in supporting community services and effective development planning. However, some rural areas still face limitations in information regarding hamlet boundaries and road access, including Lunto Barat Village, Lembah Segar District, Sawahlunto City. This Community Service Program (KKN) aimed to improve the availability and accessibility of village regional information through hamlet and road mapping activities. The methods applied included field observation, direct road data collection with village officials, preparation of hamlet maps, and environmental development activities through community-based planting. The results indicated that the mapping activities provided clearer information on hamlet boundaries and village road networks, thereby facilitating community access to regional information. Furthermore, the resulting village map helped both residents and visitors locate destinations more effectively. This program demonstrates that village mapping activities not only enhance access to regional information but also contribute to more structured and participatory village development.

The evaluation of haul road geometry at PT Trubaindo Coal Mining was conducted to ensure that the haul road from the stockroom to the jetty meets applicable technical standards, thereby supporting the smoothness, safety, and efficiency of coal transportation activities. This study aims to analyze the conformity of haul road geometric parameters, including straight road width, curve width, curve radius, cross slope, longitudinal grade, and superelevation, based on the standards of AASHTO 7th Edition and Kepmen 1827 of 2018. The research method employed field measurements using a Terrestrial Laser Scanner (TLS), followed by data processing using Maptek and Surpac software. The obtained data were then compared with the applicable standards to determine the level of conformity of the haul road geometry. Based on field measurements, the average values obtained were 14.99 m for straight road width, 18.45 m for curve width, 0.22 m for cross slope, 6.97% for grade, 223.51 m for curve radius, and 1.40 m for superelevation. Furthermore, based on calculations and analysis referring to AASHTO 7th Edition and Kepmen 1827 of 2018, and considering the largest haulage equipment used (Volvo FMX 480), the ideal haul road geometry values were determined as follows: 13 m for straight road width, 17 m for curve width, 124 m for curve radius, 0.26 m for cross slope, 1 m for superelevation, and 6% for grade based on PT Trubaindo Coal Mining standards.

This study evaluates the implementation of spot-check testing for road preservation works in Manado City, specifically on the Kairagi–Mapanget segment, the Manado City–Wori boundary, and the Liwas Terminal Access Road, to identify existing problems and assess the effectiveness of engineering-based mitigation efforts. The objective is to examine construction quality compliance with technical specifications and identify operational challenges encountered on site. The methodology encompasses the measurement of asphalt layer thickness and density via a core drill, the examination of contract documents, the analysis of laboratory test results, and the execution of field observations. The findings show that most samples meet the required standards, although several locations require corrective action. Challenges arise from weather conditions, heavy traffic, equipment limitations, and the need for adaptation to updated technical regulations. Operational mitigation strategies, staff training, the use of core drill technology, and digital documentation were found to enhance accuracy and efficiency in evaluation. The research demonstrates the value of stakeholder collaboration and capacity building in supporting quality control for road preservation works. A multidimensional approach is effective in resolving technical and operational complexities in urban road projects.

 Road infrastructure development is an important factor in supporting community mobility and equitable regional development. However, road construction data management in Palembang City still faces various problems, such as scattered data, lack of integration, and not yet presented in a map-based visual form. This study aims to implement a Geographic Information System (GIS) as a medium for location mapping and presenting road construction data information in Sematang Borang and Kalidoni Districts, Palembang City. The methods used include field observation, road construction data collection and verification, GPS coordinate point retrieval, spatial and non-spatial data processing, and the development of a GIS system based on interactive digital maps. The results of the study indicate that the system is able to present road construction information in a structured, accurate, and easily accessible manner. This GIS helps improve the efficiency of the monitoring process, data management, and preparation of road construction reports at the Palembang City Public Works and Housing Agency. Thus, the application of GIS can be a supporting solution in decision-making and encourage the digitalization of road infrastructure data management.

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.

The attendance process at SMK Swasta Dwitunggal 2 Tanjung Morawa is still carried out manually, potentially causing inefficiency, delays, and inaccuracies in attendance data. This condition results in the difficulty of managing attendance data and less than optimal support for managerial decision-making in the school environment. This study aims to design an Enterprise Architecture (EA) for a school attendance system using the TOGAF Architecture Development Method (ADM) framework. The research method used is a qualitative approach with a case study design. The analysis is carried out through mapping existing conditions (AS-IS) and designing expected conditions (TO-BE) in several TOGAF ADM phases, including business architecture, information system architecture, technology architecture, as well as the Opportunities and Solutions and Migration Planning phases. The results of the study are an enterprise architecture blueprint and a migration roadmap that can serve as guidelines for the gradual implementation of a technology based attendance system. This design is expected to improve the efficiency of attendance management, data accuracy, and support the decision-making process in schools.

The global energy crisis and climate change are driving the development of biodiesel as a renewable energy source. Graphite as an additive shows significant potential in improving the efficiency and reducing emissions of biodiesel. This study maps graphite-biodiesel research in Southeast Asia using a meta analysis of systematic reviews of 68 publications from Scopus, Web of Science, and ScienceDirect from 2015-2024. The results show that Malaysia leads in publication contributions (32%), followed by Thailand (28%) and Indonesia (18%). The optimal graphite concentration of 50 ppm increases brake thermal efficiency by 8.3% and reduces CO (15.7%), HC (12.4%), and smoke (18.9%) emissions, although there is an increase in NOx (6.8%). Palm oil methyl ester dominated the research (56%). Indonesia has strategic opportunities with abundant feedstock and graphite deposits, but faces challenges in research infrastructure, limited international collaboration, and the absence of an integrated national roadmap. Infrastructure investment, human resource strengthening, and industry academia collaboration are needed to accelerate national biodiesel research.

Aceh Tamiang Regency is an area with a high risk of annual flooding. This emergency condition often triggers a surge in environment-related diseases such as skin diseases, respiratory infections, and diarrhea. The success of managing health crises heavily depends on pharmaceutical logistics management, especially the availability of essential medicines and ease of access for refugees at evacuation points. This study aims to analyze the extent of medicine availability at community health centers and health posts, as well as to evaluate the barriers to medicine accessibility for flood victims in Aceh Tamiang Regency. This study uses a qualitative/quantitative descriptive method (choose one) with a case study approach. Primary data were collected through in-depth interviews with pharmaceutical logistics officers and surveys of flood survivors. Secondary data were obtained from the drug stock reports of the Aceh Tamiang District Health Office. Analysis was conducted on variables such as drug types, stock amounts (Buffer Stock), and distribution channels during the emergency response period. The results of the study indicate that the availability of drugs in the initial disaster phase tends to be (state the prediction, e.g., sufficient/limited). However, accessibility is often hindered by damaged road infrastructure and uneven distribution to remote posts. There is an urgent need to strengthen the logistics early warning system so that the types of medicines available match the disease patterns that emerge after floods. Although medicine stocks are generally available in central pharmacy warehouses, geographical constraints and distribution coordination are the main factors hindering accessibility. It is recommended that local governments map out alternative distribution routes and provide disaster-specific buffer stock of medicines at the sub-district level.

Digital entrepreneurship has become a transformative phenomenon in the modern economy, changing the way businesses are developed and run through digital technology. Although the literature on digital entrepreneurship is growing rapidly, comprehensive mapping of the evolution of knowledge and research patterns is still limited. This study analysis the development of digital entrepreneurship in academic literature through bibliometric analysis of the Scopus database to identify research trends, knowledge structures, and future directions. The analysis was conducted on 1,815 publications from 681 sources (1988-2025) written by 8,023 researchers, using R Studio (bibliometrix) and VOSviewer. The results show exponential growth with an annual growth rate of 17.22%, as well as five main theme clusters: digital business models, digital innovation, social entrepreneurship, e-commerce, and fintech. Thematic evolution shifted from traditional entrepreneurship (1988–2010) to digital transformation (2011-2018) to new technology integration (2019-2025). The level of international collaboration reached 26.39%, with the United States, China, and the United Kingdom as the main contributors. These findings provide a research roadmap for identifying research gaps, collaboration opportunities, and trending topics in digital entrepreneurship.

The rapid development of digitalization and innovation has become a key driver in improving business processes and the competitiveness of organizations worldwide. This study is the first comprehensive bibliometric analysis examining the relationship between digitalization and innovation in business processes, to map the intellectual structure of this field, track the development of its themes, and identify remaining research gaps. This analysis, which utilizes data from Scopus processed using VOSviewer and Biblioshiny software, covers publications from 2010 to 2024 and employs co-occurrence, co-authorship, and thematic evolution techniques. The results show a rapid growth in publications since 2016, peaking at over 110 publications in 2024. Eight key thematic clusters stand out: Industry 4.0, artificial intelligence, robotic process automation, blockchain, drivers, and agile business process management. Despite the field's maturity, it still suffers from high fragmentation, strong geographic concentration, and a reliance on cross-sectoral research designs. As a result, longitudinal insights remain limited, and digital transformation failure rates remain high, reaching up to 70%. This research presents the first quantitative and visual roadmap of global knowledge flows in this domain and underscores the need for longitudinal, geographically inclusive, and people-centric research to move beyond single-point understandings to a sustainable, context-sensitive framework that enhances both the theoretical depth and practical success of digital-based business process innovation

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications

The development of science and technology has encouraged the utilization of graph theory in solving optimization problems, particularly in transportation systems and tourism route planning. Medan City, as a metropolitan area with dense road networks and widely dispersed tourist destinations, faces challenges in selecting efficient travel routes. This research aims to determine the optimal route between tourist destinations in Medan City using the Minimum Spanning Tree (MST) method with Prim’s Algorithm. The research was conducted using a weighted graph modeling approach, where each tourist destination is represented as a vertex and the distance between destinations is represented as an edge weight. Distance data and estimated travel time were obtained through digital mapping using Google Maps and then analyzed through iterations of Prim’s Algorithm to produce a minimum spanning tree without forming cycles. The results show that all 23 tourist destinations are successfully connected in a single MST structure with a minimum total distance of 68.97 km and a travel time of approximately 199 minutes or 3 hours and 19 minutes. This model is expected to serve as a reference for tourism planning and support urban transportation efficiency based on mathematical computation.

Pancur Batu District, as one of the areas in Deli Serdang Regency, is experiencing the dynamics of regional development and pressure on land. The total area of ​​Pancur Batu District in Deli Serdang Regency is 122.53 km2. Information on the current land use conditions is essential to support spatial planning and sustainable development policy making. This study aims to identify, map, and analyze the distribution of Sustainable development. This study aims to identify, map and analyze the distribution of land use in Pancur Batu sub-district by utilizing remote sensing technology. The method used is visual interpretation and on-screen digitization using high-resolution satellite imagery from Google Earth in 2024. Land use classification is divided into several main classes, such as settlements, open land and road networks. The main result of this study is a land use map of Pancur Batu sub-district with a 1: 63,360 map that presents a special distribution of each land use class. From the results of the map analysis, it is known that land use is dominated by open land. Meanwhile, identified residential areas are growing rapidly along the main road network. The resulting map can be accurate and up-to-date basic data for the Deli Serdang district government for monitoring, evaluating land suitability, and controlling space use in Pancur Batu sub-district.

Bureaucratic reform is an essential step in realizing a clean, accountable, professional, and transparent government. The 2020–2024 Bureaucratic Reform Road Map has emphasized the Ministry of Administrative and Bureaucratic Reform’s (Kementerian PANRB) efforts to establish a healthy bureaucracy through the strengthening of the merit system, performance management, and service digitalization. As providers of public services, Civil Servants (ASN) play a crucial role in ensuring professional public service delivery; therefore, an organized performance management system is required to achieve optimal bureaucratic reform. However, in practice, the performance management implemented by the Ministry still faces challenges, particularly concerning non-ASN personnel. This study aims to analyze the implementation of performance management in the process of structuring non-ASN personnel by the Ministry of PANRB as part of the national bureaucratic reform agenda. The research employs a qualitative approach with descriptive analysis based on literature review, legislation, and secondary data from official sources. The findings indicate that the policy of structuring non-ASN personnel, grounded in the principles of the merit system and performance management, has brought significant changes to human resource governance within government institutions. Nevertheless, its implementation still encounters challenges such as limited budget allocation, lack of integrated performance data, and uncertainty regarding the employment status and welfare of non-ASN employees. The digitalization efforts through the e-Kinerja system and national personnel data collection serve as strategic steps to enhance transparency and accountability in performance assessment. Thus, comprehensive performance management is expected to promote professionalism among public servants, improve organizational effectiveness, and realize an adaptive and integrity-based bureaucracy.

This study develops a strategic information system plan to improve customer service at the Food Mood MSME in the food and beverage sector. The Ward and Peppard framework is used to map the business and technology environment through Value Chain, SWOT, PEST, and Porter's Five Forces analyses, which are then broken down into Critical Success Factors and measurable key performance indicators. The research design is a qualitative case study with semi-structured interviews with the owner and employees, observations during peak hours, and a review of operational documents. The mapping results in a prioritized portfolio that places a cloud-based point-of-sale system integrated with QRIS, a lightweight inventory and procurement module, a kitchen display system, and basic accounting as the foundation, followed by a mini customer relationship management and loyalty program, online channel integration, a sales dashboard, and simple demand forecasting. The formulated performance targets include a wait time of no more than eight minutes, an order error rate below one percent, stock-outs of less than one day per month, and 100% transaction recording. The suggested three-month roadmap is operational and provides immediate benefits in terms of increased service speed, data accuracy, and potential customer retention, while also confirming the relevance of Ward and Peppard's approach for the Indonesian MSME context.