An Implementation of Fuzzy Logic to Salinity Control of Chanos chanos Pond Based on Internet of Things
DOI:
https://doi.org/10.61841/05daa749Keywords:
Conductivity, Salinity, Fuzzy Logic, Internet of ThingsAbstract
Chanos chanos has great potential to become a business field in Indonesia. To manage a farm many factors must be considered, one of the factors that must be managed well is Water Salinity. At present in Tanjungpakis - Karawang Village, pond farmers still use traditional methods to find out the condition of pond water. Farmers do it by looking at the color of water, the smell of water and using taste buds. Generally brackish chanos chanos can grow well in water conditions that have saline levels ranging from 5 - 25 ppt. With changing environmental conditions and weather, the salinity of ponds usually increases or decreases, during the dry season the pond water salinity usually increases quite dramatically, whereas in the rainy season the pond's water salinity is usually at normal or even less normal limits. Farmers in the pond do additional fresh water in the dry season and increase sea water in the rainy season so that the pond's water salinity remains stable. this study made a water salinity control system for chanos chanos fishponds. The control system is carried out by measuring the salinity of pond water using conductivity sensors, processing data using fuzzy logic, direct monitoring through computers and mobile phones and using aquaculture, namely freshwater and sea water pumps to maintain the stability of salt levels in the ponds. This system runs well with an accuracy rate of 87.8% compared to refractor meter and condition determination using fuzzy logic with 100% accuracy.
Downloads
References
[1] Arji, G., Ahmadi, H., Nilashi, M., Rashid, T. A., Omed, Q., Ahmed, H., … Zainol, A. (2019). ScienceDirect Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification. Integrative Medicine Research. https://doi.org/10.1016/j.bbe.2019.09.004
[2] Ben, R., Bouadila, S., & Mami, A. (2018). Development of a Fuzzy Logic Controller applied to an agricultural greenhouse experimentally validated. Applied Thermal Engineering, 141(February 2017), 798–810. https://doi.org/10.1016/j.applthermaleng.2018.06.014
[3] Chang, B., Chang, Y., Chao, W., Yeh, S., & Kuo, D. (2019). Effects of sulfamethoxazole and sulfamethoxazoledegrading bacteria on water quality and microbial communities in milk fi sh ponds +. Environmental Pollution, 252, 305–316. https://doi.org/10.1016/j.envpol.2019.05.136
[4] Golshan, M., Dastoorpour, M., & Birgani, Y. T. (2020). Fuzzy environmental monitoring for the quality assessment: Detailed feasibility study for the Karun River basin, Iran. Groundwater for Sustainable Development, 100324. https://doi.org/10.1016/j.gsd.2019.100324
[5] 10. Hussain, H.I., Kamarudin, F., Thaker, H.M.T. & Salem, M.A. (2019) Artificial Neural Network to Model Managerial Timing Decision: Non-Linear Evidence of Deviation from Target Leverage, International Journal of Computational Intelligence Systems, 12 (2), 1282-1294.
[6] Jiang, D. (2019). Jo urn. Computer Communications. https://doi.org/10.1016/j.comcom.2019.10.035
[7] Mitchell, T. (n.d.). AN INTRODUCTION TO FUZZY LOGIC APPLICATIONS IN INTELLIGENT SYSTEMS THE KLUWER INTERNATIONAL SERIES KNOWLEDGE REPRESENTATION, LEARNING AND.
[8] Prakash, G., Darbandi, M., Gafar, N., Jabarullah, N.H., & Jalali, M.R. (2019) A New Design of 2-Bit Universal Shift Register Using Rotated Majority Gate Based on Quantum-Dot Cellular Automata Technology, International Journal of Theoretical Physics, https://doi.org/10.1007/s10773-019-04181-w.
[9] Sinha, A., Shrivastava, G., & Kumar, P. (2019). Architecting User-Centric Internet of Things for Smart Agriculture.
Sustainable Computing: Informatics and Systems. https://doi.org/10.1016/j.suscom.2019.07.001
[10] Swanson, C. (1998). Interactive effects of salinity on metabolic rate, activity, growth and osmoregulation in the
euryhaline milkfish (Chanos chanos). Journal of Experimental Biology, 201(24), 3355–3366.
[11] Tirupathi, C., Shashidhar, T., Pandey, V. P., & Shrestha, S. (2018). Fuzzy-based approach for evaluating
groundwater sustainability of Asian cities. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2018.09.027
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
