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

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.

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Accuracy Enhancement of Correlated Naive Bayes Method by Using Correlation Feature Selection (CFS) for Health Data Classification

2020 3rd International Conference on Information and Communications Technology (ICOIACT) ◽

10.1109/icoiact50329.2020.9332021 ◽

2020 ◽

Author(s):

Hairani Hairani ◽

Muhammad Innuddin ◽

Majid Rahardi

Keyword(s):

Feature Selection ◽

Naive Bayes ◽

Data Classification ◽

Health Data ◽

Naïve Bayes ◽

Bayes Method ◽

Accuracy Enhancement ◽

Naive Bayes Method

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Kombinasi Metode Correlated Naive Bayes dan Metode Seleksi Fitur Wrapper untuk Klasifikasi Data Kesehatan

Jurnal Teknik Elektro ◽

10.15294/jte.v11i2.23693 ◽

2020 ◽

Vol 11 (2) ◽

pp. 50-55

Author(s):

Hairani Hairani ◽

Muhammad Innuddin

Keyword(s):

Feature Selection ◽

Performance Test ◽

Naive Bayes ◽

Health Data ◽

Naïve Bayes ◽

Bayes Method ◽

Heart Attacks ◽

Bayes Methods ◽

Naive Bayes Method

Most features of health data that have many irrelevant features can reduce the performance of classification method. One health data that has many attributes is the Pima Indian Diabetes dataset and Thyroid. Diabetes is a deadly disease caused by the increasing of blood sugar because of the body's inability to produce enough insulin and its complications can lead to heart attacks and strokes. The purpose of this research is to do a combination of Correlated Naïve Bayes method and Wrapper-based feature selection to classification of health data. The stages of this research consist of several stages, namely; (1) the collection of Pima Indian Diabetes and Thyroid dataset from UCI Machine Learning Repository, (2) pre-processing data such as transformation, Scaling, and Wrapper-based feature selection, (3) classification using the Correlated Naive Bayes and Naive Bayes methods, and (4) performance test based on its accuracy using the 10-fold cross validation method. Based on the results, the combination of Correlated Naive Bayes method and Wrapper-based feature selection get the best accuracy for both datasets used. For Pima Indian Diabetes dataset, the accuracy is 71,4% and the Thyroid dataset accuracy is 79,38%. Thus, the combination of Correlated Naïve Bayes method and Wrapper-based feature selection result in better accuracy without feature selection with an increase of 4,1% for Pima Indian Diabetes dataset and 0,48% for the Thyroid dataset.

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Feature Selection using Particle Swarm Optimization Algorithm in Student Graduation Classification with Naive Bayes Method

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i3.1833 ◽

2020 ◽

Vol 4 (3) ◽

pp. 469-475

Author(s):

Evi Purnamasari ◽

Dian Palupi Rini ◽

Sukemi

Keyword(s):

Feature Selection ◽

Particle Swarm Optimization ◽

Particle Swarm Optimization Algorithm ◽

Naive Bayes ◽

Particle Swarm ◽

Naïve Bayes ◽

Bayes Method ◽

Swarm Optimization ◽

Student Graduation ◽

Naive Bayes Method

The study of the classification of student graduation at a university aims to help the university understand the academic development of students and to be able to find solutions in improving the development of student graduation in a timely manner. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. The classification accuracy can be improved by selecting the appropriate features. Particle Swarm Optimization is an evolutionary optimization method that can be used in feature selection to produce a better level of accuracy. The testing results of the alumni data using the Naive Bayes method that optimized with the Particle Swarm Optimization algorithm in selecting appropriate features, producing an accuracy value of 86%, 6% higher than the classification without feature selection using the Naive Bayes method.

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Forensic Malware Identification Using Naive Bayes Method

2020 International Conference on Information Technology Systems and Innovation (ICITSI) ◽

10.1109/icitsi50517.2020.9264959 ◽

2020 ◽

Author(s):

Beno Ramadhan ◽

Yudha Purwanto ◽

Muhammad Faris Ruriawan

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Bayes Method ◽

Naive Bayes Method

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

SINTECH (Science and Information Technology) Journal ◽

10.31598/sintechjournal.v3i1.523 ◽

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%.

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