DATA CLUSTERING DESIGN AND FISH FARMING POND AUTOMATION CASE STUDY: POND SALMON CENTER ITERA.

Clustering and automation design use the K-means and FCM methods as well as the fuzzy logic method to build an automation system that can be used and implemented in real terms in salmon center ITERA ponds. The Clustering carried out in this study can be used as input from an automation system and...

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Main Author: Vidia
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/71882
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Institution: Institut Teknologi Bandung
Language: Indonesia
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spelling id-itb.:718822023-02-27T14:50:21ZDATA CLUSTERING DESIGN AND FISH FARMING POND AUTOMATION CASE STUDY: POND SALMON CENTER ITERA. Vidia Indonesia Theses FCM, Fuzzy logic, automation, salmon center ITERA. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/71882 Clustering and automation design use the K-means and FCM methods as well as the fuzzy logic method to build an automation system that can be used and implemented in real terms in salmon center ITERA ponds. The Clustering carried out in this study can be used as input from an automation system and as a grouping of data on a pond whose results will be applied for various purposes for analysis of pond conditions to optimize pond handling so that pond conditions become optimal. Happen By using K-means and FCM clustering, maximum results are obtained in grouping data, the data obtained in K-means and FCM complement each other. Using SVM in clustering is expected to reduce the error rate in clustering. The gamma value in the SVM regulation is 0.7, or it can be said to be close to zero, so it allows for even more classification errors to occur so that the hyperparameter can be increased so that the results obtained are even better. While the automation system uses fuzzy logic which is one of the AI methods that can produce a system that can automatically determine the on and off of the system automatically based on incoming input, this system can be implemented with the variables used in this study namely DO, pH, salinity, and temperature. The variables used have fuzzy sets, namely the DO variable has 5 sets, namely bad, standard, fair, good, and very good. Whereas for the pH variable, there are 3 fuzzy sets namely acid, neutral and alkaline, for the salinity variable or what can be called the level of saltiness there are 3 fuzzy sets namely fresh, brackish, and marine. The last variable is the temperature variable which has a fuzzy set of heat, growth, and heat. All groups in all variables have values adjusted to the salmon center ITERA pond standards, but this system can be used depending on the needs of each type of fish by changing the parameters and their values depending on the type of fish to be cultivated text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Clustering and automation design use the K-means and FCM methods as well as the fuzzy logic method to build an automation system that can be used and implemented in real terms in salmon center ITERA ponds. The Clustering carried out in this study can be used as input from an automation system and as a grouping of data on a pond whose results will be applied for various purposes for analysis of pond conditions to optimize pond handling so that pond conditions become optimal. Happen By using K-means and FCM clustering, maximum results are obtained in grouping data, the data obtained in K-means and FCM complement each other. Using SVM in clustering is expected to reduce the error rate in clustering. The gamma value in the SVM regulation is 0.7, or it can be said to be close to zero, so it allows for even more classification errors to occur so that the hyperparameter can be increased so that the results obtained are even better. While the automation system uses fuzzy logic which is one of the AI methods that can produce a system that can automatically determine the on and off of the system automatically based on incoming input, this system can be implemented with the variables used in this study namely DO, pH, salinity, and temperature. The variables used have fuzzy sets, namely the DO variable has 5 sets, namely bad, standard, fair, good, and very good. Whereas for the pH variable, there are 3 fuzzy sets namely acid, neutral and alkaline, for the salinity variable or what can be called the level of saltiness there are 3 fuzzy sets namely fresh, brackish, and marine. The last variable is the temperature variable which has a fuzzy set of heat, growth, and heat. All groups in all variables have values adjusted to the salmon center ITERA pond standards, but this system can be used depending on the needs of each type of fish by changing the parameters and their values depending on the type of fish to be cultivated
format Theses
author Vidia
spellingShingle Vidia
DATA CLUSTERING DESIGN AND FISH FARMING POND AUTOMATION CASE STUDY: POND SALMON CENTER ITERA.
author_facet Vidia
author_sort Vidia
title DATA CLUSTERING DESIGN AND FISH FARMING POND AUTOMATION CASE STUDY: POND SALMON CENTER ITERA.
title_short DATA CLUSTERING DESIGN AND FISH FARMING POND AUTOMATION CASE STUDY: POND SALMON CENTER ITERA.
title_full DATA CLUSTERING DESIGN AND FISH FARMING POND AUTOMATION CASE STUDY: POND SALMON CENTER ITERA.
title_fullStr DATA CLUSTERING DESIGN AND FISH FARMING POND AUTOMATION CASE STUDY: POND SALMON CENTER ITERA.
title_full_unstemmed DATA CLUSTERING DESIGN AND FISH FARMING POND AUTOMATION CASE STUDY: POND SALMON CENTER ITERA.
title_sort data clustering design and fish farming pond automation case study: pond salmon center itera.
url https://digilib.itb.ac.id/gdl/view/71882
_version_ 1822992314165886976