Dataset Weighting Features Using Gain Ratio To Improve Method Accuracy Naïve Bayesian Classification

Abstract The Naïve Bayes method is proven to have a high speed when applied to large datasets, but the Naïve Bayes method has weaknesses when selecting attributes because Naïve Bayes is a statistical classification method that is only based on the Bayes theorem so that it can only be used to predict the probability of the class membership of a class independently. Independent without being able to do the selection of attributes that have a high correlation and correlation between one attribute with other attributes so that it can affect the value of accuracy. Naïve Bayesian Weight has been able to provide better accuracy than conventional Naïve Bayesian. Where an increase in the highest accuracy value obtained from the Water Quality dataset is equal to 88.57% in the Weight Naïve Bayesian classification model, while the lowest accuracy value is obtained from the Haberman dataset which is 78.95% in the conventional Naïve Bayesian classification model. The increase in accuracy of the Weight Naïve Bayesian classification model in the Water Quality dataset is 2.9%. While the increase in accuracy value in the Haberman dataset is 1.8%. If done the average accuracy of each dataset using the Weight Naïve Bayesian classification model is 2.35%. Based on the testing that has been done on all test data, it can be said that the Weight Naïve Bayesian classification model can provide better accuracy values than those produced by the conventional Naïve Bayesian classification model.

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Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i1.2587 ◽

2021 ◽

Vol 5 (1) ◽

pp. 123-131

Author(s):

Ni Luh Putu Merawati Putu ◽

Ahmad Zuli Amrullah ◽

Ismarmiaty

Keyword(s):

Social Media ◽

Latent Dirichlet Allocation ◽

Naive Bayes ◽

Naïve Bayes ◽

Classification Model ◽

Bayes Method ◽

A Value ◽

Negative Class ◽

Naive Bayes Method ◽

Dirichlet Allocation

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.

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SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN KREDIT USAHA MIKRO PADA BANK MANDIRI GOMBONG

Respati ◽

10.35842/jtir.v10i30.128 ◽

2017 ◽

Vol 10 (30) ◽

Author(s):

Ika Nur Fajri ◽

Bambang Soedijono W ◽

Syamsul A Syahdan

Keyword(s):

Neural Network ◽

Naive Bayes ◽

Naïve Bayes ◽

Bayesian Classification ◽

Naive Bayesian ◽

Naïve Bayesian

ABSTRAKKetepatan dan kecepatan dalam mengambil keputusan menjadi suatu keharusan pada proses penentuan kredit sehingga akan banyak nasabah yang akan menerima hasil, apakah diterima atau ditolak pengajuan kreditnya, karena semakin banyak nasabah yang mengajukan kredit.Penelitian ini mengimplementasikan algoritma naïve bayes untuk membantu menentukan siapa yang berhak mendapatkan kredit khususnya Kredit Usaha Mikro. Algoritma Naive Bayes merupakan salah satu algoritma yang terdapat pada teknik klasifikasi. Bayesian classification adalah pengklasifikasian statistik yang dapat digunakan untuk memprediksi probabilitas keanggotaan suatu class. Bayesian classification didasarkan pada teorema bayes yang memiliki kemampuan klasifikasi serupa dengan decission tree dan neural network. Bayesian classification terbukti memiliki akurasi dan kecepatan yang tinggi saat diaplikasikan ke dalam database dengan data yang benar. (Kusrini dan Luthfi, 2009).Hasil penelitian ini menunjukkan tingkat akurasi naïve bayes dalam memecahkan masalah pengajuan kredit sebesar 85,33 %.Kata kunci :SPK, Naive Bayesian, Klasifikasi

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

Jurnal Ilmiah SINUS ◽

10.30646/sinus.v17i1.378 ◽

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.

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