Analysis of Weather Prediction, Resource Management, and Land Optimization on the Application of Big Data Analytics in Agricultural Land Utilization in Agrarian Areas of West Java
DOI:
https://doi.org/10.58812/wsnt.v1i02.489Keywords:
Weather Prediction, Resource Management, Land Optimization, Big Data Analytics, Agricultural, Agrarian Areas, West JavaAbstract
This research investigates the impact of Weather Prediction, Resource Management, and Land Optimization on the adoption of Big Data Analytics in agricultural land utilization within the agrarian region of West Java. Employing a quantitative approach, the study integrates measurement model analysis, structural equation modeling, demographic profiling, and model fit assessment to comprehensively explore the intricate dynamics of technological adoption in agriculture. Results indicate that Land Optimization, Resource Management, and Weather Prediction significantly influence the adoption of Big Data Analytics. Demographic factors such as gender, age, education, and farming experience demonstrate varying correlations with key variables. The model exhibits strong fit, and approximately 60.2% of the variance in Big Data Analytics adoption is explained by the combined influence of the identified factors. This study contributes nuanced insights to inform policy and practice for sustainable and technology-driven agriculture in West Java.
References
R. Khairiyakh, I. Irham, and J. H. Mulyo, “Contribution of Agricultural Sector and Sub Sectors on Indonesian Economy,” Ilmu Pertan. (Agricultural Sci., vol. 18, no. 3, p. 150, 2016, doi: 10.22146/ipas.10616.
K. Pawlak and M. Kołodziejczak, “The role of agriculture in ensuring food security in developing countries: Considerations in the context of the problem of sustainable food production,” Sustain., vol. 12, no. 13, 2020, doi: 10.3390/su12135488.
T. Kurniawan and E. Kurniawan, “Policy on Utilizing Indigenous Knowledge in Critical Land Rehabilitation and Fulfillment of Sustainable Food Security in Indonesia: Regrowing ‘Talun-Kebun’ as Part of the Local Permaculture Model in West Java,” Environ. Sci. Proc., vol. 15, no. 1, p. 2, 2022.
R. Virtriana et al., “Development of Spatial Model for Food Security Prediction Using Remote Sensing Data in West Java, Indonesia,” ISPRS Int. J. Geo-Information, vol. 11, no. 5, 2022, doi: 10.3390/ijgi11050284.
E. K. Wikarta, “TOWARDS GREEN ECONOMY: THE DEVELOPMENT OF SUSTAINABLE AGRICULTURAL AND RURAL DEVELOPMENT PLANNING, THE CASE ON UPPER CITARUM RIVER BASIN WEST JAVA PROVINCE INDONESIA,” Ecodevelopment, vol. 3, no. 1, 2022.
Y. Zhao, “Thinking about the strategy and practice path of modern agricultural industry development in the context of big data,” Appl. Math. Nonlinear Sci., 2023, doi: 10.2478/amns.2023.1.00360.
T. Ramirez-Guerrero, M. I. Hernández-Pérez, M. S. Tabares, and E. Villanueva, “Characterization of variables for modeling agroclimatic and phytosanitary events in agricultural crops using deep learning models,” J. Phys. Conf. Ser., vol. 2515, no. 1, 2023, doi: 10.1088/1742-6596/2515/1/012009.
G. Lv, The Application of Intelligent Agricultural Big Data Platform on the Internet. Atlantis Press International BV, 2023. doi: 10.2991/978-94-6463-200-2_43.
H. T. Pham, J. Awange, M. Kuhn, B. Van Nguyen, and L. K. Bui, “Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices,” Sensors, vol. 22, no. 3, pp. 1–19, 2022, doi: 10.3390/s22030719.
M. Bacci, Y. O. Baoua, and V. Tarchiani, “Agrometeorological forecast for smallholder farmers: A powerful tool for weather-informed crops management in the Sahel,” Sustain., vol. 12, no. 8, p. 3246, 2020, doi: 10.3390/SU12083246.
H. Herdiansyah, E. Antriyandarti, A. Rosyada, N. I. D. Arista, T. E. B. Soesilo, and N. Ernawati, “Evaluation of Conventional and Mechanization Methods towards Precision Agriculture in Indonesia,” Sustain., vol. 15, no. 12, 2023, doi: 10.3390/su15129592.
T. R. Alberico, J. R. Ricardo, and S. Cruz, “Sustainable entrepreneurship: a current review of literature,” Int. J. Bus. Res., vol. 14, no. 5556, pp. 1–25, 2022.
Yongchao Zeng, “Navigating the Coevolution of Land Use Changes and Agricultural Technologies to Achieve Sustainability: An Agent-based Study of Policy Influences,” p. 17446, 2023.
X. J. Ge and X. Liu, “Urban land use efficiency under resource-based economic transformation—a case study of shanxi province,” Land, vol. 10, no. 8, 2021, doi: 10.3390/land10080850.
A. A. Varlamov, S. A. Galchenko, R. V. Zdanova, A. A. Rasskazova, and O. B. Borodina, “Assessment of the resource potential of agricultural land use for land management purposes,” IOP Conf. Ser. Earth Environ. Sci., vol. 579, no. 1, 2020, doi: 10.1088/1755-1315/579/1/012143.
C. Musanase, A. Vodacek, D. Hanyurwimfura, A. Uwitonze, and I. Kabandana, “Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices,” Agriculture, vol. 13, no. 11, p. 2141, 2023, doi: 10.3390/agriculture13112141.
Y. Zhao, Q. Li, W. Yi, and H. Xiong, “Agricultural IoT Data Storage Optimization and Information Security Method Based on Blockchain,” Agric., vol. 13, no. 2, 2023, doi: 10.3390/agriculture13020274.
Z. Hu et al., “Application of Non-Orthogonal Multiple Access in Wireless Sensor Networks for Smart Agriculture,” IEEE Access, vol. 7, pp. 87582–87592, 2019, doi: 10.1109/ACCESS.2019.2924917.
D. Han and M. Rodriguez, “Big Data Analytics, Data Science, ML&AI for Connected, Data-driven Precision Agriculture and Smart Farming Systems: Challenges and Future Directions,” ACM Int. Conf. Proceeding Ser., pp. 378–384, 2023, doi: 10.1145/3576914.3588337.
N. Khan, R. L. Ray, G. R. Sargani, M. Ihtisham, M. Khayyam, and S. Ismail, “Current progress and future prospects of agriculture technology: Gateway to sustainable agriculture,” Sustain., vol. 13, no. 9, pp. 1–31, 2021, doi: 10.3390/su13094883.
R. Tang, N. K. Aridas, and M. S. Abu Talip, “Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol,” Agric., vol. 13, no. 8, 2023, doi: 10.3390/agriculture13081518.
D. A. Ross, “by A thesis submitted in conformity with the requirements Graduate Department of Computer Science,” Science (80-. )., vol. M, pp. 275–287, 2008.
A. Murari, R. Rossi, L. Spolladore, M. Lungaroni, P. Gaudio, and M. Gelfusa, “A practical utility-based but objective approach to model selection for regression in scientific applications,” Artif. Intell. Rev., vol. 56, pp. 2825–2859, 2023, doi: 10.1007/s10462-023-10591-4.
Q. Wu, J. Guinney, M. Maggioni, and S. Mukherjee, “Learning gradients: Predictive models that infer geometry and statistical dependence,” J. Mach. Learn. Res., vol. 11, pp. 2175–2198, 2010.
C. El Hachimi, S. Belaqziz, S. Khabba, B. Sebbar, D. Dhiba, and A. Chehbouni, “Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture,” Agric., vol. 13, no. 1, pp. 1–22, 2023, doi: 10.3390/agriculture13010095.
V. B. Narouie, H. Wessels, and U. Römer, “Inferring displacement fields from sparse measurements using the statistical finite element method,” Mech. Syst. Signal Process., vol. 200, no. July, 2023, doi: 10.1016/j.ymssp.2023.110574.
D. Achjari, “Partial Least Squares: Another Method Of Structural Equation Modeling Analysis,” J. Ekon. dan Bisnis Indones., vol. 19, no. 3, pp. 238–248, 2004.
J. Yu, N. Taskin, C. P. Nguyen, J. Li, and D. J. Pauleen, “Investigating the Determinants of Big Data Analytics Adoption in Decision Making: An Empirical Study in New Zealand, China, and Vietnam,” Pacific Asia J. Assoc. Inf. Syst., vol. 14, no. 4, pp. 62–99, 2022, doi: 10.17705/1pais.14403.
N. Peladarinos, D. Piromalis, V. Cheimaras, E. Tserepas, R. A. Munteanu, and P. Papageorgas, “Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review,” Sensors, vol. 23, no. 16, pp. 1–38, 2023, doi: 10.3390/s23167128.
S. A. Edu and D. Q. Agozie, “Exploring Factors Influencing Big Data and Analytics Adoption in Healthcare Management,” no. June, pp. 413–428, 2020, doi: 10.4018/978-1-7998-2610-1.ch020.
K. Batko and A. Ślęzak, “The use of Big Data Analytics in healthcare,” J. Big Data, vol. 9, no. 1, 2022, doi: 10.1186/s40537-021-00553-4.
C. Zhang and Z. Liu, “Application of big data technology in agricultural Internet of Things,” Int. J. Distrib. Sens. Networks, vol. 15, no. 10, 2019, doi: 10.1177/1550147719881610.
N. Jaliyagoda et al., “Internet of things (IoT) for smart agriculture: Assembling and assessment of a low-cost IoT system for polytunnels,” PLoS One, vol. 18, no. 5 May, pp. 1–21, 2023, doi: 10.1371/journal.pone.0278440.
Z. Zhang, “Performance Modeling and Resource Management for Mapreduce Applications,” 2014.
K. P. Agrawal, “Investigating the determinants of Big Data Analytics (BDA) adoption in asian emerging economies,” 2015 Am. Conf. Inf. Syst. AMCIS 2015, pp. 1–18, 2015, doi: 10.5465/ambpp.2015.11290abstract.
A. K. Alsadi, T. H. Alaskar, and K. Mezghani, “Adoption of big data analytics in supply chain management: Combining organizational factors with supply chain connectivity,” Int. J. Inf. Syst. Supply Chain Manag., vol. 14, no. 2, pp. 88–107, 2021, doi: 10.4018/IJISSCM.2021040105.
M. A. Daniri, S. Wahyudi, and I. D. Pangestuti, “The effects of big data analytics, digital learning orientation on the innovative work behavior,” Int. J. Data Netw. Sci., vol. 7, no. 2, pp. 901–910, 2023, doi: 10.5267/j.ijdns.2022.12.021.
M. Safia, R. Abbas, and M. Aslani, “Classification of Weather Conditions Based on Supervised Learning for Swedish Cities,” Atmosphere (Basel)., vol. 14, no. 7, 2023, doi: 10.3390/atmos14071174.
D. Bose, “Big data analytics in Agriculture,” no. February, pp. 407–414, 2022, doi: 10.1007/978-981-16-6460-1_31.
H. Hassani, X. Huang, and A. E. Silva, “Big data and climate change,” Big Data Cogn. Comput., vol. 3, no. 1, pp. 1–17, 2019, doi: 10.3390/bdcc3010012.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Loso Judijanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Facebook 
Instagram 
Twitter 
Youtube 
+62 821 1557 5700 
info@westscience-press.com 