Haversine Formula Research Articles

Sistem Informasi Geografis Pengukuran Bidang Tanah Menggunakan Metode Haversine Formula Pada Dinas Pertanahan Kabupaten Aceh Besar Berbasis Web

The Aceh Besar District Land Agency is a government agency that has the task of administering affairs in the agrarian or land and spatial planning sector in government to assist the president in administering state government. The Department of Land Affairs manages state property, namely land. Management is done by means of measurement. Measurements are usually done manually. This requires quite a long time because you have to take measurements by spreading a tape measure according to the plot of land and requiring more than one person to measure it. In this study, the calculation of land area uses the haversine formula method, the haversine formula is a method for determining the distance between two points by taking into account that the earth is not a flat plane but is a plane that has a degree of curvature. The haversine formula method calculates the distance between 2 points based on the length of the straight line between the 2 points on longitude and latitude. The system is designed web-based so that the results of a GIS-based application are obtained to be able to map and carry out land surveying measurements on digital images.

Implementation of the Haversine Formula Method in Geographic Information Systems for Searching the Nearest Sea Freight Expedition Services in East Jakarta

In export and import activities, a partner is needed for the shipping process. There are three types of shipping routes available for export and import: land, sea, and air. A company that offers shipping services via sea routes is known as an EMKL. This study designs a Geographic Information System (GIS) to facilitate the search for locations of Marine Cargo Expedition Services (EMKL) in East Jakarta using the Haversine Formula. The main problem faced is the difficulty in finding nearby and relevant expedition service providers in a large and densely populated area like East Jakarta. The proposed solution involves developing a system that integrates location data with the Haversine Formula to accurately calculate the distance between the user's location and the service providers. By using this method, the system can provide precise location information and help users select the nearest expedition service. The goal of this research is to enhance efficiency and accuracy in locating expedition services. The expected outcome is the creation of a user-friendly and effective system that simplifies the process of selecting expedition services by considering distance and service availability in real-time.

Multimodal urban mobility solutions for a smart campus using artificial neural networks for route determination and an algorithm for arrival time prediction

Artificial intelligence algorithms play a key role in solving multimodal urban mobility problems, this study outlines a scheme covering bus, boat, and pedestrian transport modes. This involves designing an Artificial Neural Network (ANN) model to classify the most suitable routes for passengers, together with an algorithm capable of estimating the arrival times of various transportation modes at designated stopping points or buildings within the smart campus. The information used to train the ANN is obtained from an Internet of Things (IoT) network and a database that includes the available routes within the campus (which is situated in the Brazilian Amazon), where there is a multimodal electric mobility service. In seeking to achieve the objectives, this ANN relied on the user's geographic location in the input layer, and route mapping data stored in the database in the output layer, as well as the backpropagation algorithm for computational processing and adjustments of synaptic weight. The algorithm for modal arrival time prediction used real-time geographic location data, together with the Haversine formula to calculate geographic distance, while the average speed was obtained from the IoT network. A range of different ANN parameters were included in the experiments. In the study case, the ANN results show an improvement of above 92% for determining the best routes and predicting arrival times. The findings show that ANNs can effectively find the best route within a real-world smart campus environment. Moreover, the results show the arrival time can be estimated by using real-time geographic location data.

Walking time to nearest public automated external defibrillator for out-of-hospital cardiac arrest in a major U.S. city

BackgroundHow frequently out-of-hospital cardiac arrest (OHCA) occurs within a reasonable walking distance to the nearest public automated external defibrillator (AED) has not been well studied. MethodsAs Kansas City, Missouri has a comprehensive city-wide public AED registry, we identified adults with an OHCA in Kansas City during 2019–2022 in the Cardiac Arrest Registry to Enhance Survival. Using AED location data from the registry, we computed walking times between OHCAs and the nearest registered AED using the Haversine formula, a mapping algorithm to calculate walking distance in miles from one location to another. Results were stratified by OHCA location (home vs. public) and by whether the patient received bystander cardiopulmonary resuscitation (CPR). ResultsOf 1,522 OHCAs, 1,291 (84.8%) occurred at home and 231 (15.2%) in public. Among at-home OHCAs, 634 (49.1%) received bystander CPR and no patients had an AED applied even as 297 (23.0%) were within a 4-minute walk to the closest public AED. Among OHCAs in public, 108 (46.8%) were within a 4-minute walk to the closest public AED. For public OHCAs within a 4-minute walk, bystanders applied an AED in 13 (12.0%) of these cases and in 24.5% (13/53) of those who received bystander CPR. ConclusionIn one U.S. city with a publicly available AED registry, there were no instances in which a bystander accessed a public AED for an OHCA at home. For OHCAs in public, nearly half occurred within a 4-minute walk to the closest AED but bystander use of an AED was low.

PERANCANGAN APLIKASI CEK RADIUS OUTLET PT. IJS BERBASIS WEB MENGGUNAKAN METODE HAVERSINE FORMULA

PT. IJS is a company engaged in marketing cafe and restaurant culinary franchise licenses. Opening a new franchise has rules in the form of a radius between locations that cannot be close together. This company still uses a conventional way of checking the location radius, so it takes longer and is less efficient in the checking process. Based on this problem, the researchers built a check radius outlet application for PT. Web-based IJS calculates the shortest distance using the waterfall model and the Haversine Formula method. The designed application is tested using a black box, comparing the system results with actual conditions using an odometer and user acceptance tests. Furthermore, the black box test results were declared successful, and an accuracy value of 94% was obtained with actual conditions. The user acceptance test received a usability level percentage of 89.93% which is very strong from the user's point of view. Keywords: Black Box, Franchise, Metode Haversine Formula, UAT, Waterfall

UAV-based automated 3D modeling framework using deep learning for building energy modeling

Many approaches to the Urban Building Energy Model (UBEM) have been developed to analyze urban-scale energy demand patterns. The main goal of UBEM is to minimize manual work and improve modeling accuracy when building 3D modeling procedures that deal with vast amounts of data. To do so, it is important to build an automated process and increase the modeling efficiency throughout the process. This study proposes a new framework for automatically generating 3D models for building energy modeling. This framework collects geographic coordinate system (GCS) data by applying algorithms based on a convolutional neural network (CNN), Haversine formula, unmanned aerial vehicle (UAV), and geographic information system (GIS) information. The collected GCS data were used to generate a 3D model using EnergyPlus, resulting in a 3D model capable of a Level of Detail 3. Subsequently, we compared and verified the size and energy performance of the actual building with those of the generated model. The model size errors generated without drawing information are as follows: buildings, 3.69%, windows1 16.75%, and windows2 19.43%. The error range evidenced through the energy performance evaluation indicator showed that the MBE values for the cooling and heating energies were 5.54% and 5.77%, respectively.

Application of RESTful Method with JWT Security and Haversine Algorithm on Web Service-Based Teacher Attendance System

A teacher attendance system utilizing fingerprints has been implemented at Ponpes Daarul Muttaqien II. However, fingerprint recognition errors frequently occur if the scanner is unclean. Teachers fill out an attendance update form for various reasons, including forgotten attendance, damaged fingerprints, and unnoticed absences. The Human Resources Bureau receives updates from around 28 instructors within one week. Statistics reveal that 30% of instructors update due to finger injuries preventing recognition, and 70% due to forgetting. Amid the COVID-19 pandemic, organizations advise remote work. Similarly, only specific sections, like technical and security units, require attendance at Ponpes Daarul Muttaqien II. Due to the potential risks of linking fingerprints to virus transmission, this study proposes a web-based attendance system. It employs the RESTful API approach, JWT Security, and the Haversine Formula Algorithm. The system mandates employees to be within 100 meters of a designated coordinate point for attendance. The system tracks attendance status, arrival and delay times, and absence status. Future enhancements might involve facial recognition for more robust validation. In conclusion, this innovative approach addresses attendance challenges, offering adaptability, security, and potential fraud prevention.

Abstract 15725: Access to Pacemaker Implantation Amongst Medicare Beneficiaries: Travel Distance and Time to Nearest Clinician

Background: Despite being an essential procedure with rising use in the United States, the availability and accessibility of procedural care for a permanent pacemaker are not known. We investigated access to pacemaker implantation among Medicare beneficiaries. Methods: US Medicare physician claims from 2020, and county population characteristic data from AHRQ were used to evaluate the density of providers and beneficiaries across states and counties. For each US zip code, we determined the closest pacemaker implantation center using the haversine formula, then assessed the travel duration for each route using the Google Maps API. Results: There was a median of 5 providers performing pacemaker placement per 100,000 individuals over 65 years across all states, with an IQR of 4.5 to 6. At the county level, only 20% of counties in the US had a center offering transvenous pacemaker implantation, and 4% had a center offering leadless pacemakers. Individuals in metropolitan counties drove a median 29 (IQR 18 - 47) minutes to reach a center for pacemaker implantation, while those in non-metropolitan counties drove a median of 72 (IQR 52 - 102) minutes. In counties where >25% of the population is ≥65 years old, the median drive time to a pacemaker center is 86 (IQR 57 - 126) minutes. Residents of counties with a lower median household income (Spearman’s r = 0.45, p < 0.001) and a higher percentage of residents using public insurance had longer drive time to a pacemaker center (r = 0.23, p < 0.001). Out of the estimated 55,356,000 individuals ≥65 years old in the United States, 20,000,000 (36%) had to drive more than one hour, and 4,383,955 (8%) had to drive more than two hours to a site where a pacemaker implantation procedure is performed. Conclusion: Over one-third of the population in the United States has significant access issues to basic electrophysiologic care, highlighting the need for increased resources and improved accessibility.

The directional antenna tracker for enhancing range communication between UAVs and Ground Control Station

This paper discusses the development of an antenna tracker project focused on evaluating a 2.4 GHz Yagi Uda directional antenna connected to an NRF24L01 transceiver. It employs a gear reduction system for precise yaw and pitch control using two servo motors. The tracker calculates rotation angles using the Haversine formula for coordinates and inverse tangent trigonometry for elevation. Experiments took place in an open area with a stationary Ground Control Station (GCS) and a moving Unmanned Aerial Vehicle (UAV) to assess performance. The experiments comprised three tests: first, GCS without the Yagi Uda antenna or tracker; second, GCS with the Yagi Uda antenna but without the tracker, positioned along a 30-degree angle away from the transmitter; and third, GCS with both the Yagi Uda antenna and the tracker. The UAV gradually moved away from the tracker in 5-meter intervals until data loss occurred. The test results are based on received coordinates, distance, temperature, humidity, and data loss. Among the three tests, Test 3 demonstrates the best outcome, where the Yagi Uda antenna successfully receives data up to 180 meters before experiencing data loss.

Perancangan Aplikasi Pencarian Fasilitas Kesehatan ‘Find Medical’ dengan Menggunakan Metode Haversine dan Algoritma Dijkstra

Currently, technology is rapidly advancing, enabling communication to be done with anyone, anywhere. The sophistication of this technology can be utilized to address issues in various fields, particularly in the healthcare sector, as healthcare is one of the most influential aspects of human life. One common problem in the healthcare sector is the lack of evenly distributed information about healthcare services, leading to difficulties for individuals in finding suitable healthcare facilities. Therefore, this research aims to develop an application called "Find Medical" as a solution to facilitate the public in searching for healthcare services that meet their needs. In this study, the Haversine method and Dijkstra's algorithm are employed to recommend the nearest healthcare services based on the user's location. The research objectives are to design the "Find Medical" application and analyze the accuracy of the employed methods. The "Find Medical" application is developed using the Laravel framework. Based on the testing results, the majority of respondents agree that the application is effective in addressing the inefficient healthcare information retrieval system. Additionally, the Haversine formula and Dijkstra's algorithm implemented in the application provide accurate recommendations with a precision rate of 90%. Therefore, the "Find Medical" application can be an effective and efficient solution for individuals seeking appropriate healthcare services.

Taxi fare prediction based on multiple machine learning models

The use of taxis as a fundamental mode of transportation in everyday life has led to the increased popularity of various ride-hailing applications such as Uber and Lyft, enabling users to conveniently request and view the predicted fare for their desired destination. Accurately predicting the fare is thus of significant importance. In this study, machine learning models were employed to forecast taxi fares based on factors such as distance and passenger count. As the initial data only contained latitude and longitude values, the Haversine formula was utilized to calculate the distance between two locations. Moreover, the raw data was plagued with inconsistencies such as negative fares and grossly exaggerated distances, which were resolved by implementing four data cleaning criteria. Following the preprocessing stage, three distinct models (i.e., linear regression, decision tree, and random forest) were trained and evaluated using the root mean square error metric. The results indicated that the random forest model produced the smallest error (1.264), followed by the decision tree model with a similar error rate (1.277), and lastly, the linear regression model with the highest error (1.718). Thus, the random forest model demonstrated superior performance and is recommended for accurate fare predictions.

RANCANG BANGUN APLIKASI ANDROID UNTUK MENEMUKAN LOKASI UMKM TERDEKAT DENGAN GOOGLE MAP API DAN HAVERSINE DISTANCE

Micro, Small and Medium Enterprises (MSME), plays a role in improving people's welfare and incresing bruto income. With the support of IT for running MSMEs, it has proven to help the sustainability of MSMEs, especially during a pandemic. For this reason, we conducted a research by building a mobile applications to support MSME. This application has a minimum specification limit using Android 10 (Android Q) version. The application has the following functions (Silvanie et al., 2023) detects the current location based on GPS and Android location, (Wika Undari, 2021) displays the user's map from Google Maps and shows the user's position, (Rambe et al., 2022) retrieves UMKM data either briefly or in detail based on the closest distance to user position,(Alimuddin, 2021) the feature of registering as a member with more authority such as giving a rating. The SDLC Waterfall method is used to build the application. Calculation of the distance between the user and the MSME location is done by the Haversine formula. From the results of black box testing and the feasibility testing of the beta version, it is summarized as follows: 33% consider the application quite feasible, 66.7% consider the application feasible, 62.5% willingly to spend money to subscribe and 37.5% do not want to spend money.

Application of The Haversine Method In The Android-Based Donation Search Application

Used goods that we no longer use will only fill the room which if we leave it will be damaged and have no use value because it consumes time. If given to someone else, the goods may still be useful and can help their lives the problem is how we find people who need our goods and vice versa. For this reason, a system is needed that can help facilitate and bring the two parties together. The effort made to make it easier for the recipient to contribute in taking the donated goods is to use the haversine method so that the recipient can consider the distance between them and the donated item. The haversine formula is an equation that uses latitude and longitude to find the distance between the two different points. By using the haversine formula, the output is the distance from the location of the party receiving the donation (current location) to the donor. Therefore, the author wants to create and design an android-based application so that it can reach many people and can make it easier for donors to donate their goods or for the recipient of donations in looking for items that have been donated.

UAV Trajectory Optimisation in Smart Cities Using Modified A* Algorithm Combined With Haversine and Vincenty Formulas

It is anticipated that the backbone of Smart Cities concerning automation and networking will be formed by Unmanned Aerial Vehicles in the imminent future. Therefore, our research focuses on developing advanced microcontrollers embedded with Artificial Intelligence techniques for self-governing Unmanned Aerial Vehicles. The main objective of this research was to enable full automation for the execution of flight paths with non-trivial sequences that will be performed with centimetre-level accuracy. Also, by utilising dynamic flight plans and trajectories, we aim to secure autonomous aviation based on norms, with control loops and fundamental constraints. More specifically, we evolved a novel algorithmic technique for trajectory optimisation, which deploys a modification to the A* search algorithm, implemented by the Haversine formula and enhances accuracy using Vincenty's formula. Furthermore, realistic values for trajectory optimisation and obstacle avoidance were found through the implementation of a simulative investigation. The outcomes of our methodology indicate that the safety constraints associated with the integration of Unmanned Aerial Vehicles in the urban environment can be significantly mitigated. Consequently, their effectiveness will be increased in realising their diverse operations and capabilities.

Implementation Of Teacher Presence System Using Mobile-Based Geofencing & Haversine Formula Methods

Implementation of a teacher presence system using geofencing and the Haversine Formula mobile-based method provides a modern solution for accurately monitoring teacher attendance. This system utilizes geofencing technology to set virtual boundaries around certain locations and the Haversine formula to calculate the distance between the teacher's current location and the school's location. If the teacher's location is within the boundaries of the area, the system assumes the teacher is present, and if outside, the system considers the teacher absent. Attendance status is regularly updated and stored in the database, providing an efficient method for administrators to monitor teacher attendance and increase accountability. The use of this system reduces the possibility of manual errors, making it a valuable tool in education. Two tests were carried out, namely testing the accuracy of the system and testing black box testing. Accuracy testing was carried out under three conditions, namely inside the presence area, at the border of the presence area, and outside the presence area. The test was carried out 30 times and resulted in 100% accuracy. then blackbox testing also concludes that the application works in accordance with the testing that has been done. The conclusion from this research is that the higher the device and network used, the more accurate it will be.

K-Means Clustering Method for Determining Waste Transportation Routes to Landfill

Waste is worsening in Magelang City, especially in urban areas. As a result of poorly managed waste disposal, a landfill is needed. Magelang City has a landfill called TPA Banyuurip, located in Plumbon Hamlet, Banyuurip Village, Tegalrejo Subdistrict, Magelang City. From this case, the application of the kmeans clustering method to determine the efficiency of the waste transportation route to the landfill is needed. The research began by conducting direct observations at the Banyuurip landfill by interviewing the drivers of waste vehicles to find out information such as waste sources, transportation schedules, etc. In this study, the data used are the name and address of the supplier, sub-district, coordinate point, and distance from the supplier's place to the landfill. After data collection, data preprocessing is done by dividing and selecting data based on sub-districts. Then the data is processed using the kmeans clustering algorithm to divide the route efficiency and the haversine formula algorithm to determine the closest distance between clusters. After the data has been successfully processed, the number of clusters is 4 for north Magelang, where each cluster will become a corridor with four routes. For central Magelang, 2 clusters with two routes, while for south Magelang, the results are 4 clusters with four routes. From these results, the evaluation results using silhouette score for data clustering of 3 sub-districts are 0.632560 for North Magelang, 0.640667 for Central Magelang, and 0.630186 for South Magelang. This method is expected to help in grouping routes and mapping supplier areas effectively and efficiently in the waste transportation process in Magelang City.

K-Means Clustering Method for Determining Waste Transportation Routes to Landfill

Waste is worsening in Magelang City, especially in urban areas. As a result of poorly managed waste disposal, a landfill is needed. Magelang City has a landfill called TPA Banyuurip, located in Plumbon Hamlet, Banyuurip Village, Tegalrejo Subdistrict, Magelang City. From this case, the application of the kmeans clustering method to determine the efficiency of the waste transportation route to the landfill is needed. The research began by conducting direct observations at the Banyuurip landfill by interviewing the drivers of waste vehicles to find out information such as waste sources, transportation schedules, etc. In this study, the data used are the name and address of the supplier, sub-district, coordinate point, and distance from the supplier's place to the landfill. After data collection, data preprocessing is done by dividing and selecting data based on sub-districts. Then the data is processed using the kmeans clustering algorithm to divide the route efficiency and the haversine formula algorithm to determine the closest distance between clusters. After the data has been successfully processed, the number of clusters is 4 for north Magelang, where each cluster will become a corridor with four routes. For central Magelang, 2 clusters with two routes, while for south Magelang, the results are 4 clusters with four routes. From these results, the evaluation results using silhouette score for data clustering of 3 sub-districts are 0.632560 for North Magelang, 0.640667 for Central Magelang, and 0.630186 for South Magelang. This method is expected to help in grouping routes and mapping supplier areas effectively and efficiently in the waste transportation process in Magelang City.

An intelligent road accident reduction system using device-to-device communication

<p>Every year thousands of people are dying due to road accidents and most of the accidents are occurring in urban areas and highways. As the number of vehicles is increasing day by day, the probability of the occurrence of the accident is also increasing. A system has been introduced in this paper that can reduce road accidents by sending an alert to drivers via smartphones. For this purpose, an android-based application has been developed. To reduce the accident, every driver of the car must have the application. Firstly, the location of the vehicle will be collected from the driver’s smartphone, then the data will be sent to the server through the application. An algorithm has been also developed using Haversine Formula which calculates the distance among the vehicles. A total of five cases have been implemented according to the dataset and compared for the performance evaluation. An extensive experimental study and comparison have been performed with the other methods where a complete performance is focused that can be claimed to reduce road accidents via the device-to-device communication process.</p>

Comparison of Haversine and Euclidean Distance Formula for Calculating Distance Between Regencies in West Sumatra

Jarak adalah angka yang menunjukkan seberapa jauh jarak antara dua tempat. Manfaat penggunaan jarak banyak digunakan dalam penelitian, salah satunya dalam penerapan matriks pembobot spasial. Matriks pembobot spasial diperoleh berdasarkan informasi kedekatan antar wilayah. Terdapat dua jenis pembobot spasial yaitu, berdasarkan persinggungan (contiguity) dan jarak (distance). Penentuan kedekatan wilayah di Sumatera Barat lebih baik menggunakan pembobot spasial berdasarkan jarak, sebab di Sumatera Barat terdapat wilayah kepulauan dan pegunungan yang membatasi antar wilayah. Beberapa persamaan estimasi jarak yang dapat digunakan adalah jarak Haversine dan Euclidean. Hubungan antara dua titik dalam Haversine memperhitungkan kelengkungan bumi ketika menghitung jarak, yang merupakan perbedaan antara kedua rumus tersebut. Sebaliknya, metode jarak Euclidean menggunakan garis lurus untuk menghubungkan dua titik. Tujuan dari penelitian ini adalah untuk memastikan apakah rumus jarak Haversine dan Euclidean menghasilkan hasil yang berbeda secara signifikan dalam hal jarak. Perhitungan jarak titik koordinat memanfaatkan garis lintang dan garis bujur yang di dapat dari Google Maps. Jarak yang diukur menggunakan kedua rumus tersebut dinyatakan dalam kilometer (km), kemudian data tersebut diolah dengan menggunakan uji z. Temuan menunjukkan bahwa rumus Haversine dan rumus Euclidean Distance tidak berbeda secara signifikan dalam proses penghitungan jarak.

Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks

The non-terrestrial network (NTN) is a network that uses radio frequency (RF) resources mounted on satellites and includes satellite-based communications networks, high altitude platform systems (HAPS), and air-to-ground networks. The fifth generation (5G) and NTN may be crucial in utilizing communication infrastructure to provide 5G services in the future, anytime and anywhere. Based on the outcome of the Rel-16 study, the 3rd generation partnership project (3GPP) decided to start a work item on an NTN in 5G new radio (NR) Rel-17, and the focus of the study was on mobility management procedures, due to the movements of NTN platforms; especially, low earth orbit (LEO) satellites. Handover enhancements were discussed to tackle the frequent handover due to the fast satellite movement. Therefore, the major problem of handover in LEO satellite systems was the signaling storm created by handing over all user equipment (UE) in a cell to a new cell because when the UE crosses the boundary between the neighboring cell of a satellite, an intra-satellite or cell handover occurs; thus, all users in a cell are expected to experience a change of cell due to handover every few seconds. In addition, UE location is not easy to define due to moving cell/beam situations. In this study, we propose machine learning-based solutions for handover decisions in non-terrestrial networks for cell handovers or intra-satellite handovers to reduce signaling storms during handovers where the handover requests will be executed by clustered users. First, the dataset was generated by the simulator that simulates communication between users and satellites. Second, we preprocessed the data, and also used the feature creation technique to create the distance feature using the Haversine formula, and then applied clustering and classification algorithms. The experimental results show that the distance between a user and its cell center is an important parameter for handover decisions in NTN, and the random forest outperforms all models with a higher accuracy of 99% along with a better F1-score of 0.9961.