A New Computational Intelligence for Face Emotional Detection in Ubiquitous

Wahyono, Irawan Dwi; Ashar, Muhammad; Fadlika, Irham; Asfani, Khoirudin; Saryono, Djoko; Sunarti,

doi:10.1109/iceeie47180.2019.8981420

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…Word pre-processing is an early stage in text mining. Pre-processing is done to eliminate parts of the text that are not necessary to obtain quality data for execution [15][16][17]. First, in the pre-processing phase, namely, tokenization, which aims to break up sentences into individual words known as terms or tokens.…”

Section: Text Miningmentioning

confidence: 99%

“…First, in the pre-processing phase, namely, tokenization, which aims to break up sentences into individual words known as terms or tokens. In addition, filtering is done by removing punctuation marks, changing uppercase letters to lowercase letters, and removing stop words that aim to eliminate words that are not useful or that do not affect the process [15][16][17]. The last is to derive to get basic words from words that have been given affixes or other information.…”

Section: Text Miningmentioning

confidence: 99%

“…The last is to derive to get basic words from words that have been given affixes or other information. The derivation used is derivation and since this algorithm is more precise than Porter's algorithm [16][17].…”

Section: Text Miningmentioning

confidence: 99%

See 2 more Smart Citations

Emotion Detection based on Column Comments in Material of Online Learning using Artificial Intelligence

Wahyono

Saryono

Putranto

et al. 2022

Int. J. Interact. Mob. Technol.

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many universities use online learning as media learning that each material of media which includes videos, textual content, or audio may be given remarks from college students. The lecture desires to recognize approximately the feelings of college students which include happy, disappointed, or unhappy when they accessed the media and instructors get an assessment of pleasant from their media. This study constructed a utility cellular for the detection of emotion from column remarks in the media online. The mobile application makes use of synthetic intelligence to type textual content from remarks and to decide the emotion of college students. The mobile application on a cellular device. The set of rules with inside the utility is k-Nearest Neighbour for the textual content mining feature in this study. The information of trying out these studies is commenting on YouTube channels and online studying which include SIPEJAR The result of trying it out is that the common accuracy is 0,697, the value of recall is 0.5595, and the common precision is 0, 4421 and the accuracy for the utility of this mobile app is 70% for detection emotion-primarily based totally on a column of remark in the media online.

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Section: Text Miningmentioning

confidence: 99%

Section: Text Miningmentioning

confidence: 99%

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Emotion Detection based on Column Comments in Material of Online Learning using Artificial Intelligence

Wahyono

Saryono

Putranto

et al. 2022

Int. J. Interact. Mob. Technol.

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“…This large group consists of 20 students who are done together on the subject matter of electric machines. The purpose of testing this large group is to test the revision in the previous testing stages so that a valid virtual laboratory is found that matches the material, models, and media for the material of electrical machines and in by the ability of students to carry out the practicum of electrical machines [19][20]. This study uses a questionnaire instrument.…”

Section: Figure 1 Model Of Web Learning Developmentmentioning

confidence: 99%

“…The answers of these experts have a score then total all the score results. The answers for each questionnaire have a Likert scale which has 4 categories of answer choices [19][20][21]. In the research method, data analysis used is the percentage form (%).…”

Section: Figure 1 Model Of Web Learning Developmentmentioning

confidence: 99%

Development of a Personalized Virtual Laboratory Using Artificial Intelligent

Wahyono¹,

Putranto²,

Saryono³

et al. 2020

Proceedings of the International Conference on Learning Innovation 2019 (ICLI 2019)

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The development of learning technology in industry 4.0 in the last few years was growing rapidly one of which was a virtual application. One of the virtual learning applications developed was a virtual laboratory. The use of virtual laboratories in Revolution 4.0 was very useful especially educational institutions that did not have enough space and equipment. Virtual laboratory research had been carried out in recent years. The development of virtual laboratories developed was the media, models, and materials in virtual laboratories. The existing virtual laboratory was one-way learning. Virtual laboratories had no feedback with users especially the desires and abilities of users in virtual laboratories. Laboratory learning based on user desires and abilities as needed. This learning model was known as personalization. This research developed a virtual laboratory that was personalized by utilizing artificial intelligence. The ability and will of the user were processed by artificial intelligence to determine the model, media, and materials suitable for the user. The results of testing in this virtual laboratory obtained accuracy values according to users based on personalized obtained at 90.8%.

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