Cultivation Methods Using Kajarula Technology to Increase the Productivity of Seaweed (Kappaphycus alvarezii)
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Seaweed (Kappaphycus alvarezii) is a potential marine biological resource in Indonesia, especially in Palasa Village, Sumenep, East Java. The conventional (long-line) method applied in cultivation is still not effective and efficient enough. The urgency in this study is that the decrease in production output is influenced by environmental disturbances, such as pest attacks, strong currents and waves, as well as the problem of using plastic bottle floats in conventional cultivation methods that are not environmentally friendly. Therefore, there is a need to develop cultivation methods to increase seaweed productivity. This study aims to analyze seaweed productivity using Kajarula (Seaweed Net Bag) technology at the cultivation location of Palasa Village, Sumenep, East Java. This study uses the Group Random Design (RAK) method. The research began with the measurement of the physical-chemical quality of the waters, the creation of a design for making Kajarula from the clumps, monitoring the growth of seaweed, monitoring the quality of waters, monitoring pests and diseases, and the productivity of seaweed. Data analysis using seaweed productivity parameters including biomass production, relative growth, growth, absolute growth rate, and total harvest. The data obtained is then tabulated using the Ms. Excel application to produce representative data in the form of graphs and tables, and analyzed descriptively. From the results of the study, seaweed cultivated for 42 days produced an average biomass production of 5.54 ± 0.27 kg/unit, a relative growth of 153.41 ± 10.97%/day; growth 754 ± 27 g/binding point; absolute growth rate of 239.32 ± 21.95 g/day; and a total harvest of 5.26 – 5.82 kg/unit of bag. The water quality condition in these waters is in the optimal range in supporting the growth of seaweed. Productivity of cultivation, seaweed using Kajarula technology shows optimal growth and yield.
Al-Turjman, F., Zahmatkesh, H., & Mostarda, L. (2019). Quantifying uncertainty in internet of medical things and big-data services using intelligence and deep learning. IEEE Access, 7, 115749–115759. https://doi.org/10.1109/ACCESS.2019.2931637
Ang, L. M., Seng, K. P., Ijemaru, G. K., & Zungeru, A. M. (2019). Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges. IEEE Access, 7, 6473–6492. https://doi.org/10.1109/ACCESS.2018.2887076
Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., & Altowaijri, S. M. (2019). Smarter traffic prediction using big data, in-memory computing, deep learning and gpus. In Sensors (Switzerland) (Vol. 19, Issue 9). https://doi.org/10.3390/s19092206
Dwivedi, A. D., Srivastava, G., Dhar, S., & Singh, R. (2019). A decentralized privacy-preserving healthcare blockchain for IoT. Sensors (Switzerland), 19(2), 1–17. https://doi.org/10.3390/s19020326
Huang, M., Liu, W., Wang, T., Song, H., Li, X., & Liu, A. (2019). A queuing delay utilization scheme for on-path service aggregation in services-oriented computing networks. IEEE Access, 7, 23816–23833. https://doi.org/10.1109/ACCESS.2019.2899402
Kumar, S., & Singh, M. (2019). Big data analytics for healthcare industry: Impact, applications, and tools. Big Data Mining and Analytics, 2(1), 48–57. https://doi.org/10.26599/BDMA.2018.9020031
Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., & Tan, U. X. (2019). A survey of data fusion in smart city applications. Information Fusion, 52(January), 357–374. https://doi.org/10.1016/j.inffus.2019.05.004
Leonelli, S., & Tempini, N. (2020). Data Journeys in the Sciences.
Mosavi, A., Shamshirband, S., Salwana, E., Chau, K. wing, & Tah, J. H. M. (2019). Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning. Engineering Applications of Computational Fluid Mechanics, 13(1), 482–492. https://doi.org/10.1080/19942060.2019.1613448
Nallaperuma, D., Nawaratne, R., Bandaragoda, T., Adikari, A., Nguyen, S., Kempitiya, T., De Silva, D., Alahakoon, D., & Pothuhera, D. (2019). Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4679–4690. https://doi.org/10.1109/TITS.2019.2924883
Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., López García, A., Heredia, I., Malík, P., & Hluchý, L. (2019). Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey. Artificial Intelligence Review, 52(1), 77–124. https://doi.org/10.1007/s10462-018-09679-z
Palanisamy, V., & Thirunavukarasu, R. (2019). Implications of big data analytics in developing healthcare frameworks – A review. Journal of King Saud University - Computer and Information Sciences, 31(4), 415–425. https://doi.org/10.1016/j.jksuci.2017.12.007
Sadowski, J. (2019). When data is capital: Datafication, accumulation, and extraction. Big Data and Society, 6(1), 1–12. https://doi.org/10.1177/2053951718820549
Saura, J. R., Herraez, B. R., & Reyes-Menendez, A. (2019). Comparing a traditional approach for financial brand communication analysis with a big data analytics technique. IEEE Access, 7, 37100–37108. https://doi.org/10.1109/ACCESS.2019.2905301
Schulz, S., Becker, M., Groseclose, M. R., Schadt, S., & Hopf, C. (2019). Advanced MALDI mass spectrometry imaging in pharmaceutical research and drug development. Current Opinion in Biotechnology, 55, 51–59. https://doi.org/10.1016/j.copbio.2018.08.003
Shang, C., & You, F. (2019). Data Analytics and Machine Learning for Smart Process Manufacturing: Recent Advances and Perspectives in the Big Data Era. Engineering, 5(6), 1010–1016. https://doi.org/10.1016/j.eng.2019.01.019
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0197-0
Sigala, M., Beer, A., Hodgson, L., & O'Connor, A. (2019). Big Data for Measuring the Impact of Tourism Economic Development Programmes: A Process and Quality Criteria Framework for Using Big Data.
Sivaraman, K., Krishnan, R. M. V., Sundarraj, B., & Sri Gowthem, S. (2019). Network failure detection and diagnosis by analyzing syslog and SNS data: Applying big data analysis to network operations. International Journal of Innovative Technology and Exploring Engineering, 8(9 Special Issue 3), 883–887. https://doi.org/10.35940/ijitee.I3187.0789S319
Song, Q., Ge, H., Caverlee, J., & Hu, X. (2017). Tensor completion algorithms in big data analytics. ArXiv, 13(1).
Stylos, N., & Zwiegelaar, J. (2019). Big Data as a Game Changer: How Does It Shape Business Intelligence Within a Tourism and Hospitality Industry Context?
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep Learning Approach for Intelligent Intrusion Detection System. IEEE Access, 7, 41525–41550. https://doi.org/10.1109/ACCESS.2019.2895334
Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., & Fu, Y. (2019). Large scale incremental learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 374–382. https://doi.org/10.1109/CVPR.2019.00046
Xu, G., Shi, Y., Sun, X., & Shen, W. (2019). Internet of things in marine environment monitoring: A review. Sensors (Switzerland), 19(7), 1–21. https://doi.org/10.3390/s19071711
Yu, Y., Li, M., Liu, L., Li, Y., & Wang, J. (2019). Clinical big data and deep learning: Applications, challenges, and future outlooks. Big Data Mining and Analytics, 2(4), 288–305. https://doi.org/10.26599/BDMA.2019.9020007
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