scholarly journals Cyberbullying Detection System Using Machine Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 2059-2063
Author(s):  
Ms. Shama Kabeer
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
The Internet ◽  
Bullying Behavior ◽  
Bayes Algorithm ◽  
Cyberbullying Detection ◽  
Online Form

Abstract: Cyberbullying is an online form of harassment. By posting, commenting, sending, or distributing personal, derogatory, false, or nasty stuff about others that can shame or humiliate them, this conduct is done with the goal of harming others. Once such content is published on the internet, it remains accessible indefinitely. This activity is considered unlawful, and it is more widespread among children and teenagers. Cyberbullying is an online epidemic that has the potential to result in devastating outcomes such as violence and suicide, and so must be dealt with swiftly and properly. To detect bullying behavior in textual messages, a real-time cyberbullying detection system based on machine learning—Naïve Bayes Algorithm is presented. The model was created to determine whether a tweet was bullying or non-bullying in nature. Also, to assist victims in dealing with bullying difficulties without their identities being revealed. Keywords: Machine Learning, Cyberbullying, Naïve Bayes, Cybercrimes, Cyberbullying Detection

Application of Machine Learning Approaches in Intrusion Detection System

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Zena Abdulmunim Aziz ◽  
◽  
Adnan Mohsin Abdulazeez ◽  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Naive Bayes ◽  
Detection System ◽  
Rapid Development ◽  
Naïve Bayes ◽  
The Internet ◽  
Learning Approaches ◽  
Data Set ◽  
Supporting Layer

The rapid development of technology reveals several safety concerns for making life more straightforward. The advance of the Internet over the years has increased the number of attacks on the Internet. The IDS is one supporting layer for data protection. Intrusion Detection Systems (IDS) offer a healthy market climate and prevent misgivings in the network. Recently, IDS has been used to recognize and distinguish safety risks using Machine Learning (ML). This paper proposed a comparative analysis of the different ML algorithms used in IDS and aimed to identify intrusions with SVM, J48, and Naive Bayes. Intrusion is also classified. Work with the KDD-CUP data set, and their performance has been checked with the WEKA software. A comparison of techniques such as J48, SVM, and Naïve Bayes showed that the accuracy of j48 is the higher one which was (99.96%).

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

scholarly journals An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm

2020 ◽  
Vol 20 (1) ◽  
pp. 82-94
Author(s):  
Badrus Zaman ◽  
Army Justitia ◽  
Kretawiweka Nuraga Sani ◽  
Endah Purwanti
Keyword(s):  
Performance Evaluation ◽  
System Performance ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Bayes Algorithm ◽  
F Measure ◽  
System Performance Evaluation

AbstractHoax news in Indonesia spread at an alarming rate. To reduce this, hoax news detection system needs to be created and put into practice. Such a system may use readers’ feedback and Naïve Bayes algorithm, which is used to verify news. Overtime, by using readers’ feedback, database corpus will continue to grow and could improve system performance. The current research aims to reach this. System performance evaluation is carried out under two conditions ‒ with and without sources (URL). The system is able to detect hoax news very well under both conditions. The highest precision, recall and f-measure values when including URL are 0.91, 1, and 0.95 respectively. Meanwhile, the highest value of precision, recall and f-measure without URL are 0.88, 1 and 0.94, respectively.

scholarly journals Effectiveness of Classification Methods on the Diabetes System

2021 ◽  
pp. 33-43
Author(s):  
Ahmed T. Shawky ◽  
Ismail M. Hagag
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Early Stage ◽  
Research Paper ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Bayes Algorithm

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

scholarly journals ANALISIS PEMANTAUAN LAN MENGGUNAKAN METODE QoS DAN PENGKLASIFIKASIAN STATUS JARINGAN INTERNET MENGGUNAKAN ALGORITMA NAIVE BAYES

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
Sachin Sabloak ◽  
Jasuandi Wijaya ◽  
Abdul Rahman ◽  
Molavi Arman
Keyword(s):  
Quality Of Service ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Internet ◽  
Data Set ◽  
Computer Laboratory ◽  
The Status ◽  
Bayes Algorithm ◽  
Internet Network

[Id]Pentingnya jaringan komputer pada kehidupan sekarang, perlu adanya kestabilan jaringan komputer yang digunakan. Pemantauan kualitas jaringan internet didalam sebuah jaringan LAN dilakukan network administrator untuk mendapatkan nilai dari data yang didapat, penelitian ini menerapkan algoritma Naive Bayes menggunakan dataset TIPHON dengan parameter yang terdapat dalam metode QoS yaitu delay, packetloss dan jitter untuk memonitor kualitas jaringan internet. Metode QoS akan menghasilkan nilai dari setiap parameter yang dibutuhkan untuk pemantauan jaringan, guna mendapatkan kesimpulan mengenai status jaringan internet digunakan Algoritma Naive Bayes. Metode Quality of Service (QoS) merupakan sebuah metode yang digunakan dalam mendefinisikan kemampuan suatu jaringan yang ?digunakan untuk pengukuran tentang kualitas ?jaringan. Penggunaan algoritma Naive Bayes diperlukan karena algoritma tersebut digunakan dalam pengklasifikasian yang menggunakan probabilitas dan statistik serta mampu mengambil keputusan dengan menggunakan dataset yang telah disediakan. Tujuan penelitian ini dilakukan untuk mengetahui status jaringan internet di lab komputer STMIK Global Informatika MDP serta mengetahui tingkat akurasi dari algoritma Naive Bayes untuk mengklasifikasikan status jaringan internet. Pengujian penelitian dilakukan di lab komputer STMIK Global Informatika MDP. Hasil pengujian dalam penelitian ini menunjukkan bahwa akurasi Naive Bayes yang didapatkan sebesar 87,78% dan status jaringan internet di lab komputer STMIK Global Informatika MDP masuk ke dalam kategori memuaskan dengan nilai dominan yaitu sebesar 47,78%.Kata Kunci: Naive Bayes, network administrator, Quality of Service (QoS), status jaringan internet.[En]Since computer network is very important nowadays, it needs the stability of the network used. Monitoring the quality of the internet network in LAN is conducted by an administrator to get the value of the data obtained. This research applied Naive Bayes algorithm using TIPHON data set with parameters in QoS method; delay, packetloss and jitter, to monitor the quality of the internet network. QoS method will gain value in every parameter needed for network monitoring. To get a conclusion about the status of the internet network, Naive Bayes algorithm was used. Quality of Service (QoS) method is a method used to define the ability of a network to measure its quality. Naive Bayes algorithm is needed since the algorithm is used in classifying using probability and statistic as well as making decision using dataset provided. This research is conducted to see the status of the internet network in STMIK Global Informatika MDP computer laboratory and to know the level of accuracy of Naive Bayes algorithm to classify the status of the network. The research was conducted in STMIK Global Informatika MDP computer laboratory. The result of the research showed that the accuracy of Naive Bayes was 87,78% and the status of the internet network STMIK Global Informatika MDP was in the category of satisfactory with dominant value 47,78%.

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