Application of the haversine formula method to determine the closest distance to a minimarket

Authors

  • Anik Muttaqin STMIK YMI Tegal, Indonesia
  • Aang Alim Murtopo STMIK YMI Tegal, Indonesia
  • Syefudin Syefudin STMIK YMI Tegal, Indonesia
  • Gunawan Gunawan STMIK YMI Tegal, Indonesia

DOI:

https://doi.org/10.35335/mandiri.v13i1.293

Keywords:

Closest Distance, Geographical Coordinates, Haversine, Minimarket, Navigation

Abstract

In a digital era that demands speed and efficiency, determining the closest distance to minimarkets is crucial for consumers and the logistics industry. This study proposes the use of the haversine method to improve the accuracy of distance calculations. Through quantitative and quasiexperimental approaches, this study describes the steps of data collection, pre-processing, and application of haversine formulas. The results demonstrate the reliability of the haversine method in estimating distances accurately, allowing users to make more informed decisions in planning trips or logistics strategies. These findings contribute to the academic literature and field practice by providing a more robust and applicable methodology for determining the closest distance. Keywords: haversine, closest distance, minimarket.

References

Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Demleitner, M., & Andrae, R. (2021). Estimating distances from parallaxes. V. Geometric and photogeometric distances to 1.47 billion stars in Gaia Early Data Release 3. The Astronomical Journal, 161(3), 147.

Baptist, L. J., Raja, L., & Shanmugasundaram, S. (2024). Real-time healthcare monitoring system through haversine distance calculation-based global positioning system. International Journal of Medical Engineering and Informatics, 16(2), 115–125.

Baskar, A., & Anthony Xavior, M. (2021). A four-point direction search heuristic algorithm applied to facility location on plane, sphere, and ellipsoid surfaces. Journal of the Operational Research Society, 73(11), 2385–2394.

Belda, S., Pipia, L., Morcillo-Pallarés, P., Rivera-Caicedo, J. P., Amin, E., De Grave, C., & Verrelst, J. (2020). DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection. Environmental Modelling & Software, 127, 104666.

Cook, T. D., Zhu, N., Klein, A., Starkey, P., & Thomas, J. (2020). How much bias results if a quasi-experimental design combines local comparison groups, a pretest outcome measure and other covariates?: A within study comparison of preschool effects. Psychological Methods, 25(6), 726.

El-Sayed, O. H., Emam, O., & Abdel-Salam, M. (2022). Deep learning fAramework for locating physical internet hubs using latitude and longitude classification. International Journal of Advanced Computer Science and Applications, 13(7).

Faisal, M., & Zamzami, E. M. (2020). Comparative analysis of inter-centroid K-Means performance using euclidean distance, canberra distance and manhattan distance. Journal of Physics: Conference Series, 1566(1), 12112.

Formánek, T., & Sokol, O. (2022). Location effects: Geo-spatial and socio-demographic determinants of sales dynamics in brick-and-mortar retail stores. Journal of Retailing and Consumer Services, 66, 102902.

Greenaway-McGrevy, R., & Phillips, P. C. B. (2023). The impact of upzoning on housing construction in Auckland. Journal of Urban Economics, 136, 103555.

Hao, F., Yang, Y., & Wang, S. (2021). Patterns of location and other determinants of retail stores in urban commercial districts in Changchun, China. Complexity, 2021(1), 8873374.

Hosseinzadeh, M., Azhir, E., Ahmed, O. H., Ghafour, M. Y., Ahmed, S. H., Rahmani, A. M., & Vo, B. (2023). Data cleansing mechanisms and approaches for big data analytics: a systematic study. Journal of Ambient Intelligence and Humanized Computing, 1–13.

Hu, X., Zhang, G., Shi, Y., & Yu, P. (2024). How Information and Communications Technology Affects the Micro-Location Choices of Stores on On-Demand Food Delivery Platforms: Evidence from Xinjiekou’s Central Business District in Nanjing. ISPRS International Journal of Geo-Information, 13(2), 44.

Ishak, Z., Ahmad, W. S. H. M. W., Radzi, N. A. M., Sulaiman, S., & Ramli, N. E. (2021). Placement accuracy algorithm for smart street lights. Turkish J. Electr. Eng. Comput. Sci., 29(2), 845–859.

Lam, O. H. Y., Kattge, J., Tautenhahn, S., Boenisch, G., Kovach, K. R., & Townsend, P. A. (2024). ‘rtry’: An R package to support plant trait data preprocessing. Ecology and Evolution, 14(5), e11292.

Lerner, J., Calloway, C., & Ason, D. (2024). Carbon reduction and understanding through simulation of transportation (CRUST) for e-commerce. Journal of Cleaner Production, 454, 142198.

Li, J., Wang, F., & He, Y. (2020). Electric vehicle routing problem with battery swapping considering energy consumption and carbon emissions. Sustainability, 12(24), 10537.

Pot, F. J., van Wee, B., & Tillema, T. (2021). Perceived accessibility: What it is and why it differs from calculated accessibility measures based on spatial data. Journal of Transport Geography, 94, 103090.

Shami, M. B., Kiss, G., Haakonsen, T. A., & Lindseth, F. (2024). Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks. ArXiv Preprint ArXiv:2401.07582.

Singh, S., Kumar, R., Panchal, R., & Tiwari, M. K. (2021). Impact of COVID-19 on logistics systems and disruptions in food supply chain. International Journal of Production Research, 59(7), 1993–2008.

Soe, N. C., & Thein, T. L. L. (2020). Haversine formula and RPA algorithm for navigation system. International Journal of Data Science and Analysis, 6(1), 32.

Stone, M. J., Migacz, S., & Wolf, E. (2022). Learning through culinary tourism and developing a culinary tourism education strategy. Journal of Tourism and Cultural Change, 20(1–2), 177–195.

Sudiatmika, I. P. G. A., Dewi, K. H. S., & Jayaningsih, A. A. R. (2021). Garage Geographic Information System Using Haversine Method Based On Android. 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS), 1–7.

Sun, S., Liu, Z., Yin, H., & Ang, M. H. (2022). Fiss: A trajectory planning framework using fast iterative search and sampling strategy for autonomous driving. IEEE Robotics and Automation Letters, 7(4), 9985–9992.

Tian, G., Lu, W., Zhang, X., Zhan, M., Dulebenets, M. A., Aleksandrov, A., Fathollahi-Fard, A. M., & Ivanov, M. (2023). A survey of multi-criteria decision-making techniques for green logistics and low-carbon transportation systems. Environmental Science and Pollution Research, 30(20), 57279–57301.

Tran, V. H., & Sirieix, L. (2020). Shopping and cross-shopping practices in Hanoi Vietnam: An emerging urban market context. Journal of Retailing and Consumer Services, 56, 102178.

Veena, M. B. (2022). OpenCV Implementation of Grid-based Vertical Safe Landing for UAV using YOLOv5. International Journal of Advanced Computer Science and Applications, 13(9).

Viglia, G., & Dolnicar, S. (2020). A review of experiments in tourism and hospitality. Annals of Tourism Research, 80, 102858.

Yáñez-Sandivari, L., Cortés, C. E., & Rey, P. A. (2021). Humanitarian logistics and emergencies management: New perspectives to a sociotechnical problem and its optimization approach management. International Journal of Disaster Risk Reduction, 52, 101952.

Yoon, J., Kim, Y., Lee, S., & Shin, M. (2024). UAV-based automated 3D modeling framework using deep learning for building energy modeling. Sustainable Cities and Society, 101, 105169.

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Published

2024-06-14

How to Cite

Muttaqin, A., Murtopo, A. A., Syefudin, S., & Gunawan, G. (2024). Application of the haversine formula method to determine the closest distance to a minimarket. Jurnal Mandiri IT, 13(1), 72–80. https://doi.org/10.35335/mandiri.v13i1.293

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