Design adaptive learning system using metacognitive strategy path for learning in classroom and intelligent tutoring systems

Agustianto, Khafidurrohman; Permanasari, Adhistya Erna; Kusumawardani, Sri Suning; Hidayah, Indriana

doi:10.1063/1.4958507

Cited by 18 publications

(6 citation statements)

References 22 publications

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“…This research method begins with a literature study, shown in Figure 2, this stage becomes very important in the context of ensuring the State of the Art (research position) [13], the research position found will be compared with the problems that arise in the field, the next step is identification and resolution, problems that are part of the purpose of the study. The next research stage is making rules, this rule will be used in the Artificial Neural Network [14] process on multi-sensors [4].…”

Section: Figure 2 Research Methodsmentioning

confidence: 99%

RETRACTED: Surface Modeling Vehicle for Shrimp Ponds using Artificial Neural Network

Destarianto

Agustianto

Rosdiana

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The worldwide frozen shrimp trade market in 2018 was recorded at the US $ 17.2 billion or around Rp.232.2 trillion. Indonesia is one of the principal exporters of frozen shrimp in the worldwide market. In light of this information, shrimp cultivating is a promising area, yet shrimp cultivating is particularly controlled by water quality. Water quality in shrimp cultivating consistently changes. Many variables impact changes in water quality (shrimp biomass, PH, and temperature). These components in the water should be inside the standard limit rank. Thusly, to accomplish the creation effectiveness of the shrimp business, it is important to robotize water quality control. This study aims to develop Surface Modeling Vehicles (SMV) for Shrimp Ponds utilizing an Artificial Neural Network. The test outcomes show an exactness pace of 94%, the expectation is that the instruments created by the exploration will want to acknowledge exact Vename Shrimp cultivating, so creation proficiency and expanded shrimp creation can be accomplished.

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Section: Figure 2 Research Methodsmentioning

confidence: 99%

RETRACTED: Surface Modeling Vehicle for Shrimp Ponds using Artificial Neural Network

Destarianto

Agustianto

Rosdiana

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Literature study on dependent on Figure 1 was completed determined to assemble data [16][17] from a few references identified with the issues to be examined [18], to frame the State of The Art. The following stage is create rule.…”

Section: Methodsmentioning

confidence: 99%

RETRACTED: Adaptive Temperature And Humidity Control System on Kumbung Mushroom using Fuzzy Neural Network Algorithm

Hartadi

Agustianto

Destarianto

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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National mushroom production in 2018, 2017, 2016, and 2015 were: 31052, 3702, 40915 and 33485 tons. Mushroom cultivation, especially oyster mushrooms, has the potential to be developed more widely in Indonesia because it has economic value and is environmentally friendly. However, the cultivation of oyster mushrooms has challenges. The challenge that arises in the cultivation of oyster mushrooms is to grow well at a temperature of 16 – 30 °C and a relative humidity of 80 – 95%. Environmental conditioning carried out by farmers on average through spraying water in mushroom kumbung manually. The use of manual methods requires high human resources, besides that it is not effective and efficient. This study aims to develop an Adaptive Temperature and Humidity Control System on Kumbung using Backpropagation Neural Network, where to determine the temperature and humidity the system will evaluate the readings of several sensors at once. The system developed is an adaptive system with an accuracy of 97%, so it is can be used to increase the precision of mushroom cultivation, so that production efficiency and increase in mushroom production can be achieved.

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“…The classification method used in this study was Naive Bayes [16][9], the selection was based on the research conducted by [8]. Thus, it is expected that the system can read and then classify quickly and accurately.…”

Section: Methodsmentioning

confidence: 99%

Design of Student Modeling System to Support Adaptive E-Learning

Destarianto¹,

Agustianto²,

Rukiati³

et al. 2023

Proceedings of the 3rd International Conference on Social Science, Humanity and Public Health, ICoSHIP 2022, 05-06 November 202

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Based on the results of the study, it was found that the practice of using E-Learning at Politeknik Negeri Jember was only used to distribute material and collect assignments, even though there was an exam function which was widely used during the pandemic. However, the implementation of E-Learning like this is far from the learning concept being implemented by the institution, namely student-centered learning (problem and project-based learning). This study used learning motivation variables indicated by the students' interaction with E-Learning for clustering (K-Means) and classification (Naive Bayes). The results of the research showed that the accuracy value was 91.66%. As a result, the researchers are optimistic that the results of this study can be used to optimize the learning process at Politeknik Negeri Jember.

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Design adaptive learning system using metacognitive strategy path for learning in classroom and intelligent tutoring systems

Cited by 18 publications

References 22 publications

RETRACTED: Surface Modeling Vehicle for Shrimp Ponds using Artificial Neural Network

RETRACTED: Surface Modeling Vehicle for Shrimp Ponds using Artificial Neural Network

RETRACTED: Adaptive Temperature And Humidity Control System on Kumbung Mushroom using Fuzzy Neural Network Algorithm

Design of Student Modeling System to Support Adaptive E-Learning

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