Design of Decision Support System with C4.5 Algorithm Methods in Determining Results Vaname Shrimp Cultivation

Tobing, Fenina Adline Twince; Dzulhaq, Muhammad Iqbal; Overbeek, Marlinda Vasty; Setiawan, Eko Anto

doi:10.1109/conmedia46929.2019.8981848

Cited by 4 publications

(4 citation statements)

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“…Value calculations are carried out for each variable in the USE questionnaire: the variable's usefulness, ease of use, ease of learning and satisfaction. According to formula (6).…”

Section: Fig 6 Recommendation Results In Web Applicationsmentioning

confidence: 99%

“…This system aims to assist users in navigating the various choices of obstetricians efficiently, considering the role of obstetricians is crucial in the delivery process. Using a recommendation system can reduce the burden of information users receive and guide them in making better decisions [6]. This system will create a model based on user input regarding the user's references [7].…”

Section: Introductionmentioning

confidence: 99%

“…The scenario testing results will show the effectiveness of implementing the VIKOR method in this system. In contrast, the user survey results will provide insight into user satisfaction with the system being developed.This research has important implications for improving the quality of decision-making [6] in selecting obstetricians. By applying the VIKOR method in an application, this research can make a real contribution to developing a more sophisticated and efficient recommendation system.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Vikor Algorithm in Web-Based Recommendation System for Obstetrician Selection

Andrew,

Adline Twince Tobing,

Kusnadi

2023

International Journal of Science, Technology & Management

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The process of childbirth is a natural event for women; with the increase in birth rates, selecting a suitable obstetrician becomes essential. However, many couples find it challenging to choose the right obstetrician, from consultations to hospital births. This study aims to develop an obstetrician recommendation system to overcome decision-making challenges that involve many criteria. The method used is VIseKriterijumska Optimizacija Kompromisno Resenje (VIKOR), which is one of the best methods in Multiple Criteria Decision Making (MCDM) for ranking and selecting alternatives. This method involves several steps, including determining the weight of the criteria, normalizing the matrix, calculating the Utility Measure and Regret Measure, and calculating the VIKOR index. Testing was carried out through scenario tests and user survey tests using the USE questionnaire. The scenario test results show the successful implementation of VIKOR in this system. The results of the user survey test indicated a high level of satisfaction with a score of 83.34%, which fulfilled the four variables in the USE questionnaire: usability (79.76%), ease of use (84.81%), ease of learning (84.81), and satisfaction (83.31%). The results of this study have important implications for increasing the effectiveness of decision-making in selecting the right obstetrician. By incorporating the VIKOR method into an application, this research provides a valuable contribution to developing a VIKOR-based recommendation system. It emphasizes the importance of applying this method in complex decision-making environments.

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“…Value calculations are carried out for each variable in the USE questionnaire: the variable's usefulness, ease of use, ease of learning and satisfaction. According to formula (6).…”

Section: Fig 6 Recommendation Results In Web Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Implementation of Vikor Algorithm in Web-Based Recommendation System for Obstetrician Selection

Andrew,

Adline Twince Tobing,

Kusnadi

2023

International Journal of Science, Technology & Management

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“…There are many challenges in precision agriculture, which have attracted various decision support tools, of which, water quality decision tools are the most common. These tools may provide decision support for determining optimal ranges of water quality [16] or provide sensing infrastructure [6]. Various physical or chemical models have also been developed, often for scenario analysis [7].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Prawn Farming: Forecasting and Anomaly Detection

Dabrowski,

Rahman,

Hellicar

et al. 2022

Preprint

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We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parameters. It provides prawn farmers with tools to proactively avoid a poor growing environment, thereby optimising growth and reducing the risk of losing stock. This is a major shift for farmers who are forced to manage ponds by reactively correcting poor water quality conditions. To our knowledge, we are the first to apply Transformer as an anomaly detection model, and the first to apply anomaly detection in general to this aquaculture problem. Our technical contributions include adapting ForecastNet for multivariate data and adapting Transformer and the Attention model to incorporate weather forecast data into their decoders. We attain an average mean absolute percentage error of 12% for dissolved oxygen forecasts and we demonstrate two anomaly detection case studies. The system is successfully running in its second year of deployment on a commercial prawn farm.

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