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All Journal Jurnal Varian Jurnal Ipteks Terapan : research of applied science and education
Ade Clinton Sitepu
Universitas Potensi Utama
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Determining Bullying Text Classification Using Naive Bayes Classification on Social Media Ade Clinton Sitepu; Wanayumini Wanayumini; Zakarias Situmorang
Jurnal Varian Vol 4 No 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v4i2.1086

Abstract

Cyber-bullying includes repeated acts with the aim of scaring, angering, or embarrassing those who are targeted Cyber-bullying is happening along with the rapid development of technology and social media in society. The media and users need to filter out bully comments because they can indirectly affect the mental psychology that reads them especially directly aimed at that person. By utilizing information mining, the system is expected to be able to classify information circulating in the community. One of the classification techniques that can be applied to text-based classification is Naïve Bayes. The algorithm is good at performing the classification process. In this research, the precision of the algorithm's has been carried out on 1000 comment datasets. The data is grouped manually first into the labels "bully" and "not bully" then the data is divided into training data and test data. To test the system's ability, the classified data is analyzed using the confusion matrix method. The results showed that the Naïve Bayes Algorithm got the level of precision at 87%. and the level of area under the curve (AUC) at 88%. In terms of speed of completing the system, the Naïve Bayes Algorithm has a very good rate of speed with completion time of 0.033 seconds.
COMPARATIVE OF ID3 AND NAIVE BAYES IN PREDICTID INDICATORS OF HOUSE WORTHINESS Ade Clinton Sitepu; Wanayumini -; Zakarias Situmorang
Jurnal Ipteks Terapan (Research Of Applied Science And Education ) Vol. 14 No. 3 (2020): Re Publish Issue
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah X

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.22 KB) | DOI: 10.22216/jit.v14i3.99

Abstract

Decision making is method of solving problems using certain way / techniques so that can beaccepted. After making some calculations and considerations through several stages, the decisionhave taken that decision maker goes through. This stage will be selected until the best decision hasmade. Decision-making aims to solve problems that solve problems so that decisions with finalgoals can be implemented properly and effectively. This study uses a simulation of decision makingfrom seven attributes to the proportion of the feasibility of a house based on data from CentralStatistics Agency (BPS). There are several techniques for presenting decision making including: ID3(decision tree) algorithm concept and Naïve Bayes algorithm. Both classification are learningsuperviseddata grouping. ID3 algorithm depicts the relationship in the form of a tree diagramwhereas Naïve Bayes makes use of probability calculations and statistics. As a result, in datatraining, decision trees are able to model decision making more accurately. The prediction resultsusing the decision tree model = 90.90%, while Naïve Bayes = 72.73%. Meanwhile, the speed of theNaive Bayes algorithm is better
COMPARATIVE OF ID3 AND NAIVE BAYES IN PREDICTID INDICATORS OF HOUSE WORTHINESS Ade Clinton Sitepu; Wanayumini -; Zakarias Situmorang
Jurnal Ipteks Terapan Vol. 14 No. 3 (2020): Re Publish Issue
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah X

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.22 KB) | DOI: 10.22216/jit.v14i3.99

Abstract

Decision making is method of solving problems using certain way / techniques so that can beaccepted. After making some calculations and considerations through several stages, the decisionhave taken that decision maker goes through. This stage will be selected until the best decision hasmade. Decision-making aims to solve problems that solve problems so that decisions with finalgoals can be implemented properly and effectively. This study uses a simulation of decision makingfrom seven attributes to the proportion of the feasibility of a house based on data from CentralStatistics Agency (BPS). There are several techniques for presenting decision making including: ID3(decision tree) algorithm concept and Naïve Bayes algorithm. Both classification are learningsuperviseddata grouping. ID3 algorithm depicts the relationship in the form of a tree diagramwhereas Naïve Bayes makes use of probability calculations and statistics. As a result, in datatraining, decision trees are able to model decision making more accurately. The prediction resultsusing the decision tree model = 90.90%, while Naïve Bayes = 72.73%. Meanwhile, the speed of theNaive Bayes algorithm is better
Co-Authors Wanayumini Zakarias Situmorang