scholarly journals Application of Nave Bayes Algorithm for SMS Spam Classification Using Orange

2021 ◽  
Vol 1 (1) ◽  
pp. 16-24
Author(s):  
Nosiel Nosiel ◽  
Sigit Andriyanto ◽  
Muhammad Said Hasibuan
Keyword(s):  
Mobile Phones ◽  
Naive Bayes ◽  
Electronic Devices ◽  
Naïve Bayes ◽  
Operating Costs ◽  
Bayes Method ◽  
Data Set ◽  
Bayes Algorithm ◽  
Continuous Use ◽  
Naive Bayes Method

Mobile phones have become a necessity for everyone. SMS is a communication service that is used to send and receive short messages in the form of text on mobile phones. Among all the advantages of SMS, there is a very annoying activity called spam (unsolicited commercial advertisements). Spam is the continuous use of electronic devices to send messages. called spammers. Spam messages are sent by advertisers with the lowest operating costs. Therefore, there are a lot of spammers and the number of messages requested is huge. Therefore, many aspects are harmed and disturbed. When SMS enters the user's mobile device, this study aims to classify spam and ham SMS. SMS classification adopts naive Bayes method. By looking at the contents of the SMS, the application of the naive Bayes method in data mining can distinguish unwanted SMS from non-spam. Results The classification accuracy rate is 0.999%. Based on the research that I have done, the Naive Bayes method can classify 1000 SMS spam data contained in the SMS spam data set file correctly.

scholarly journals Application Of Naïve Bayes to Predict the Potential of Rain in Ternate City

2021 ◽  
Vol 328 ◽  
pp. 04011
Author(s):  
Alwin Ali ◽  
Amal Khairan ◽  
Firman Tempola ◽  
Achmad Fuad
Keyword(s):  
Test Data ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Method ◽  
Calculation Results ◽  
Bayes Algorithm ◽  
Correct Data ◽  
Rain Data ◽  
Naive Bayes Method

The amount of rainfall that occurs cannot be determined with certainty, but it can be predicted or estimated. In predicting the potential for rain, data mining techniques can be used by classifying data using the naive Bayes method. Naïve Bayes algorithm is a classification method using probability and statistical methods. The purpose of this study is how to implement the naive Bayes method to predict the potential for rain in Ternate City, and be able to calculate the accuracy of the Naive Bayes method from system created. The highest calculation results with new data with a total of 400 training data and 30 test data, obtained 30 correct data with 100% precision, 100% recall and 100% accuracy and the lowest calculation results with new data with a total of 500 training data and 50 test data, obtained 38 correct data and 12 incorrect data with a percentage of precision 61.29%, recall 100% and accuracy 76%.

scholarly journals Optimasi Model Prediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
Hartatik Hartatik
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Prediction Models ◽  
Naive Bayes ◽  
Program Planning ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Student Graduation ◽  
Bayes Algorithm ◽  
Naive Bayes Method

<p>Abstrak :</p><p>Prediksi tentang status kelulusan mahasiswa menjadi persoalan tersendiri di perguruan tinggi. Perguruan tinggi utamanya di era Big Data sangatlah penting untuk melakukan prediksi perilaku akademik mahasiswa aktif sehingga dapat di ketahui kemungkinan mahasiswa bisa studi secara tepat waktu serta dapat diketahui langkah preventive dalam membuat prpgram perencanaan. Salah satu cara yang digunakan adalah teknik data mining yaitu menggunakan Algoritma <em>naive bayes</em>. Algoritma <em>Naive bayes</em> merupakan salah satu metode yang digunakan untuk memprediksi kelulusan mahasiswa.  Peneliti  dalam hal ini menerapkan  metode  <em>Naive bayes</em> menggunakan parameter Indeks prestasi kumulatif( IPK) dan membandingkan dengan menggunakan prediksi <em>naive bayes methods</em> berdasarkan parameter IPK dan sosial parameter yaitu jenis kelamin dan status tinggal. Dalam penelitian ini menggunakan parameter akademis  dan dilakukan optimasi menggunakan parameter sosial yang melekat pada mahasiswa. Berdasarkan hasil evaluasi untuk mendapatkan akurasi, hasil dari penelitian ini mendapatkan nilai akurasi untuk metode <em>Naive bayes</em>  sebesar 75% dan akurasi untuk model prediksi dengan parameter sosial  sebesar 85% dengan selisih akurasi 10%.</p><p>__________________________</p><p>Abstract : </p><p><em>Predictions about a student's graduation status are a problem in college. Major tertiary institutions in the era of Big Data are very important to predict the behavior of active students so that they can find out the possibility of students in a timely manner and can determine preventive steps in making program planning. One method used is data mining techniques using the Naive bayes Algorithm. The Naive bayes algorithm is one of the methods used to predict student graduation. Researchers in this case applied the Naive bayes method using the cumulative achievement index (GPA) parameter and compared using the prediction of the Naive bayes method based on the GPA parameters and social parameters, namely gender and status. This study uses academic parameters and is carried out optimally using social parameters inherent in students. Based on the results of the evaluation to get an accuracy value, the results of this study get an accurate value for the Naive bayes method of 75% and accurate for prediction models with social parameters of 85% with a difference of 10%.</em></p>

Prediction of User Loyalty Using the Naive Bayes Method in the "Goprint" Online Printing Marketplace

2020 ◽  
Vol 8 (3) ◽  
pp. 227
Author(s):  
Gede Widiastawan ◽  
I Gusti Agung Gede Arya Kadyanan
Keyword(s):  
Decision Making ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Test Results ◽  
Bayes Method ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Naive Bayes Method

Goprint is an Online Printing Marketplace that connects printing services with users who want to print documents quickly without the need to queue. In the span of time from April 2019 to September 2019 it was found that the number of Goprint users reached 407 users, 24 partners, and 256 orders. From transactions that have been carried out by users, not a few orders are often canceled due to ineffective Goprint features or poor partner performance. This causes Goprint users to feel dissatisfied with the services provided by the Goprint application. The Naive Bayes algorithm is one of the algorithms used for classification or grouping of data, but can also be used for decision making. With this algorithm and the problems that occur, the authors make a system to predict the loyalty of Goprint users to anticipate users who stop leaving Goprint because they are not satisfied or loyal users. The data used as training data is 20 and testing data is 10. From the test results it is found that the value of precision is 80%, 100% recall, and 90% accuracy.

scholarly journals Analysis Naïve Bayes In Classifying Fruit by Utilizing Hog Feature Extraction

2020 ◽  
Vol 4 (1) ◽  
pp. 151-160
Author(s):  
Muhathir Muhathir ◽  
Muhammad Hamdani Santoso
Keyword(s):  
Feature Extraction ◽  
Natural Resources ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Fresh Fruit ◽  
Tropical Fruits ◽  
Bayes Method ◽  
Bayes Algorithm ◽  
Naive Bayes Method

Indonesia has abundant natural resources, especially the results of its plantations. Lots of local fruit that can be used starting from the root to the skin of the fruit. Local fruit can be consumed as fresh fruit and can also be processed into drinks and food. This is reflected in the diversity of tropical fruits found in Indonesia. Fruits that are rich in benefits and can be used as medicines such as Apples, Avocados, Apricots, and Bananas. These fruits are often found around us. In Indonesia these fruits are produced and also exported abroad. However, the limited methods and technology used to classify this fruit are interesting things to discuss and become the main focus in this research. This study analyzed using the Naïve Bayes algorithm and feature extraction of HOG (Oriented Gradient Histogram) to obtain more effective classification results. The results showed that the collection of fruit using the Naïve Bayes method and HOG feature extraction had not yet obtained maximum classification results, only with an accuracy of 56.52%.Keywords – Apple, Avocado, Apricot, Banana, Naïve Bayes, HOG.

scholarly journals PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)

2020 ◽  
Vol 3 (1) ◽  
pp. 22-34
Author(s):  
Komang Aditya Pratama ◽  
Gede Aditra Pradnyana ◽  
I Ketut Resika Arthana
Keyword(s):  
Naive Bayes ◽  
Intelligent System ◽  
Naïve Bayes ◽  
The State ◽  
State University ◽  
Bayes Method ◽  
New Students ◽  
Computer Based ◽  
Entrance Test ◽  
Naive Bayes Method

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.

scholarly journals OPTIMASI METODE NAIVE BAYES DENGAN FEATURE SELECTION INFORMATION GAIN UNTUK PREDIKSI KETERLAMBATAN PEMBAYARAN SPP SEKOLAH

2019 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Muqorobin Muqorobin ◽  
Kusrini Kusrini ◽  
Emha Taufiq Luthfi
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Education Institution ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Education Development ◽  
Main Requirement ◽  
Naive Bayes Method

The cost of education is one component of input that is very important in implementing education. Because costs are the main requirement in an effort to achieve educational goals. SMK Al-Islam Surakarta is a private education institution that requires students to pay school fees in the form of Education Development Donations. Educational Development Donation is a routine school fee that is conducted every month. Based on last year's TU report, many students were late in paying Education Development Donations, around 60%. This is a big problem. The purpose of this study is that researchers will build a predictive system using the Naïve Bayes method. Because the method can classify the class right or late, in the payment of school fees. Data processing was taken from the dapodik data of schools in 2017/2018 with the test dataset taking 30 records. To find out the level of accuracy, this research was conducted with the Naive Bayes Method and the Information Gain Method for feature selection. Accuracy testing is done by the Confusion Matrix method. The results showed that the highest accuracy was obtained by combining the Naive Bayes Method with the Information Gain Method obtained by 90% accuracy. 

scholarly journals An Anti-Spam System using Naive Bayes Method using Feature Selection Methods

2017 ◽  
Vol 165 (4) ◽  
pp. 1-5 ◽  
Author(s):  
Masoome Esmaeili ◽  
Arezoo Arjomandzadeh ◽  
Reza Shams ◽  
Morteza Zahedi
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Selection Methods ◽  
Bayes Method ◽  
Naive Bayes Method
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