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Jakarta, the capital of Indonesia, is known for its high congestion levels. Data from the TomTom Traffic Index shows that Jakarta ranked 30th in the world in 2023 as one of the most congested cities, with a congestion level reaching 53% during peak hours. Pisangan Lama in East Jakarta is one of the densely populated areas, adjacent to busy roads. The main campus of STIKOM CKI, also located in East Jakarta, is situated along a route prone to heavy traffic. Given the congestion issues and the lack of information on the nearest routes, this study aims to implement the A* algorithm to find the shortest route from Pisangan Lama, East Jakarta, to the main campus of STIKOM CKI. The A* algorithm is chosen for its optimal routing capabilities. Based on research on three routes (Jl. I Gusti Ngurah Rai, Jl. Basuki Rachmat, and Jl. Raya Kalimalang), the results show that the route via Jl. Basuki Rachmat is the shortest, with a distance of 7.7 km. The implementation of the A* algorithm is expected to provide an efficient solution for the community in finding the nearest route.

Dijkstra's algorithm is one of the algorithms in graph theory that is used to solve the problem of the shortest path of a graph at each vertex that has a non-negative value. This algorithm was discovered by Edsger Wybe Dijkstra, a scientist from the Netherlands. The search for the shortest route for product delivery can be calculated through the application of the Dijkstra algorithm in the problem being faced. The problem of decision making for selecting the shortest route is still manual, so it experiences several obstacles, including the absence of a systematic and computerized system to assist the decision-making process in determining the route for shipping goods, the determination of shipping routes still depends on manual estimates so that the time taken between deliveries becomes inconsistent, the operational costs of shipping are relatively high because there is no optimal route determination system. Facing these problems, a system is needed that can minimize delays and increase effectiveness in shipping goods, namely determining the shortest route using the Dijkstra algorithm. This system works by finding various alternative routes for shipping goods at PT AMSA to address various structured and unstructured problems using data and models. To process this data and models, a method called the Dijkstra algorithm is required. Based on the description above, researchers will create a method for determining the shortest route for shipping goods at PT AMSA using the Dijkstra algorithm to facilitate the company's process of determining the shortest route.

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.

The increasing complexity of urban road networks demands intelligent navigation systems capable of determining optimal routes efficiently. This research implements the Dijkstra Shortest Path algorithm to optimize route search on a location navigation system in Medan City. The system models a road network as a weighted graph comprising 57 strategic locations and over 90 road connections, represented using adjacency list data structures. The Dijkstra algorithm, implemented in Python using the heapq module for priority queue management, achieves an optimal time complexity of O((V+E) log V). The system features five main functions: shortest route search, popular routes, location listing, dynamic location addition, and dynamic road connection addition. System testing using a case study from Kualanamu Airport to the University of North Sumatra (USU) yielded an optimal route of 16.5 km through 4 road segments. Results demonstrate that the system successfully determines the most efficient route, provides accurate distance and travel time information for multiple transport modes (motorcycle, car, walking), and presents step-by-step journey guidance. This research contributes as a practical reference for applying shortest path algorithms in urban areas and serves as a foundation for developing more complex navigation applications in the future.

The limited availability of integrated digital tourism information in Blitar Raya has made it challenging for visitors to plan their travel routes efficiently. This research aims to develop an Android-based Geographic Information System (GIS) that integrates the Google Maps API with the Bellman-Ford algorithm to optimize the shortest travel routes among tourism destinations. The system was developed using the Rapid Application Development (RAD) methodology, emphasizing user involvement through iterative prototyping and feedback. Primary data were collected from field observations, semi-structured interviews with tourists and local administrators, and beta testing involving 52 respondents, while secondary data were obtained from the Blitar Tourism Board and BPS statistical databases. The implementation integrates the Google Distance Matrix API to construct a weighted graph, which is processed by the Bellman-Ford algorithm, demonstrating higher route stability and accuracy than the Dijkstra and Haversine approaches in small-to-medium graph structures. The system achieved an 84.46% user acceptance rate, categorized as “Excellent” according to the ISO/IEC 25010 usability standard. Empirical findings show that the average response time of 1.5–2.5 seconds indicates high computational efficiency for real-time map rendering. This study contributes theoretically by presenting an algorithmic integration framework for mobile GIS applications and practically by offering a functional digital tourism platform to enhance local tourism management. The findings also suggest that combining RAD with algorithmic optimization can effectively bridge the gap between user experience and computational performance in tourism-based GIS systems.

Traffic congestion in Jakarta has become a serious problem, affecting various aspects of life, such as time efficiency, environmental quality, and economic productivity. As one solution to overcome this problem, the Jakarta government is developing a mass public transportation system, including the Light Rail Transit (LRT). The LRT is expected to reduce congestion by providing a more efficient and environmentally friendly transportation alternative. In the process of planning and optimizing the LRT system, discrete mathematics, particularly graph theory, plays a very important role. Graph theory is used to model the LRT station network and track, with vertices representing stations and edges representing connecting lines between stations. With this model, various analyses can be performed to optimize the LRT line, such as finding the shortest path between two stations or evaluating the connectivity between existing stations. The use of graph theory allows the identification of more efficient routes, resulting in faster travel and reduced passenger waiting times. In addition, graph theory also plays a role in optimizing the LRT route by minimizing the number of transfers between stations or avoiding passenger congestion at certain stations. With proper route and network optimization, the LRT can make a significant contribution to reducing traffic congestion in Jakarta. Furthermore, the development of an efficient public transportation system also supports improved air quality and reduces reliance on private vehicles. Overall, the application of graph theory in LRT system planning is a strategic step towards creating more efficient and sustainable transportation in large cities like Jakarta. Beyond route optimization, graph theory is also very useful in analyzing route capacity and passenger distribution along the LRT system.

This study presents a Systematic Literature Review (SLR) focusing on the application of the Branch and Bound (B&B) method in production optimization. In modern industry, achieving operational efficiency and profitability is essential, yet optimization often yields fractional solutions that are unrealistic for indivisible entities. The Branch and Bound method, as part of Integer Programming, has proven effective in addressing these constraints by converting fractional solutions into optimal integer values. This SLR analyzed 12 scientific articles published between 2016 and 2024, sourced from databases such as Google Scholar, Scopus, and SINTA. The analysis reveals that B&B is widely utilized to maximize production profit across various sectors, including spring beds, woven fabrics, bread, and furniture, often formulated as Integer Linear Programming (ILP) problems. Furthermore, this method is also applied to minimize production time and determine the shortest routes. Overall, B&B is a flexible and efficient tool that assists companies in managing resources and achieving maximum profit with realistic integer solutions.

This study examines the application of network models in daily life using a systematic literature review (SLR) method. Network models are mathematical approaches that aid in solving various optimization problems such as shortest routes, minimum spanning trees, and maximum flows. Findings reveal that the Floyd-Warshall and Dijkstra algorithms are applied for route navigation, Prim's algorithm for distribution efficiency, and the Edmonds-Karp algorithm for optimizing resource flows like water. This research highlights the significance of network models in enhancing operational efficiency, sustainability, and accessibility across various domains such as transportation, logistics, and resource management. With the integration of modern technology, network models hold significant potential for developing innovative solutions in everyday life.

Waste transportation activities are an example of a form of Capacitated Vehicle Routing Problem (CVRP) related to finding the minimum route. The Tabu Search algorithm is one of the metaheuristic methods that can guide the heuristic local search procedure to explore the solution area outside the local optimal point. The Tabu Search algorithm can be used to find the optimal VRP solution, namely the route that has the minimum total mileage by considering vehicle capacity. The purpose of this research is to determine the optimal route for garbage transportation in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm. Based on the results of the study, it is concluded that the optimal route for transporting waste in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm obtained the shortest route in iteration 1 with the route (12-11-10-9-8-7-6-5-4-3-2-1-0) and route length 16.55 km.

PT Sayuran Pagi is a company that operates in the agricultural sector and focuses on hydroponic vegetables such as spinach, kale, curly kale and other types of vegetables, and is located in the city of Depok and is a Limited Liability Company (PT). The Morning Vegetable Company has three gardens which are used, one of which is the Cipaku area, Bogor, where all of its production will be sent first to a warehouse in Depok and then distributed to several partners who have collaborated. The increase in consumption of green spinach among the public affects the distribution of this vegetable, which means companies must be able to meet demand with proper distribution. In this research, to determine the shortest route when distributing from the garden to the warehouse using the Dijkstra algorithm. Based on the calculation results, the optimal solution for the shortest route is route A-C-B-F-H-I or from the toll road with a total distance of 10.55km. However, because the focus of this research is on costs, from the results of the research using the Dijkstra algorithm calculation method, it can be concluded that PT. Sayuran Pagi can take the non-toll route because the costs incurred are less than the toll route.

This study aims to analyze the performance of the OSPF routing protocol in a tree topology using the Cisco Packet Tracer simulator with a router configuration, and a PC/computer. This research involves building a tree topology consisting of several router nodes and PC/computers, where OSPF is implemented to manage routing. Tests were conducted using steady ping on PC to PC, PC to router and PC to server to measure network performance in terms of latency and reliability. The test data was analyzed to evaluate OSPF convergence, network stability and rounting efficiency. The results of this study provide insight into the effectiveness of OSPF in a tree topology.

In the package delivery industry, exemplified by J&T Express Manado, optimizing courier distribution routes is essential for customer satisfaction, cost reduction, and on-time deliveries. The Traveling Salesman Problem (TSP) is a valuable tool for finding efficient routes to visit all delivery points once. This study employed the Genetic Algorithm and Nearest Neighbor Algorithm to tackle the TSP, aiming to identify the shortest routes and minimize distribution distances for J&T Express Manado's couriers using geographical coordinates. The Genetic Algorithm resulted in a distribution route of 41.20678 km, while the Nearest Neighbor Algorithm achieved a shorter route of 38.10361 km. For J&T Express Manado, our findings indicate that the Nearest Neighbor Algorithm excels in identifying the shortest courier distribution route and requires significantly less computational time. This study offers insights for J&T Express Manado and similar courier services, enabling them to enhance distribution operations, potentially reducing costs and improving efficiency. It also underscores the practical advantages of the Nearest Neighbor Algorithm in addressing TSP challenges within the industry

The main problem of the Traveling Salesman Problem is that a salesman travels to several places to go with a known distance and then returns to his original place by using the shortest route from his journey, and all the places the salesman goes to are only allowed once. This research focuses on the problem of distributing goods at PT. The Medan Nugraha Ekakurir (JNE) route with the destination delivery address in the Medan area. The Cheapest Insertion Heuristic Algorithm is an algorithm used to form tours (travels) by gradually building the shortest path route with minimal weight, by adding new points one at a time. One. The Nearest Neighbor Algorithm is a simple and fast algorithm to build a feasible initial tour length from TSP where the technique takes the shortest distance from the initial position regardless of other distances. This study resulted in the conclusion that the application of the cheapest insertion heuristic and nearest neighbor algorithms in terms of finding the distance to the problem of shipping goods at PT. The Medan Nugraha Ekakurir (JNE) route starts with finding the distance between addresses with the help of google maps, then continues with the help of the WinQSB software. Based on the research results obtained using the cheapest insertion heuristic and nearest neighbor algorithms, it is obtained that the search for the shortest route distance for shipping goods at PT. The smaller Medan Nugraha Ekakurir (JNE) route is generated by the nearest neighbor algorithm. This shows that the nearest neighbor algorithm is more effective in terms of finding the traveling distance on the Traveling Salesman Problem problem of shipping goods at PT. Medan's Nugraha Ekakurir (JNE) Line.

The search is often used to search for the shortest route, the Hill Climbing Method is a part of the test that uses heuristic functions. The problem that is often encountered is in the form of miscalculations in calculating the distance so that it requires long distances, costs a lot and takes a very long time. To solve this case, it can be solved by making a structure graph by looking at the city points from the two sides of the point to be passed. Using an algorithm can help make it easier to find a location and save time and travel costs that will be passed. This advantage is that all points will be obtained and checked from the right and left sides one by one so as to obtain effective and maximum results. The Hill Climbing method that will be used has the concept of a geographic information system as a guide and is used as a system for decision making. The heuristic search method is one of the methods commonly used in finding a way