scholarly journals ANALISIS PERFORMA ALGORITMA NAIVE BAYES PADA DETEKSI OTOMATIS CITRA MRI

2019 ◽  
Vol 5 (1) ◽  
pp. 37-42
Author(s):  
Fajar Akbar ◽  
Amin Nur Rais ◽  
Irwan Agus Sobari ◽  
Robi Aziz Zuama ◽  
Biktra Rudiarto
Keyword(s):  
Feature Extraction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Brain Images ◽  
Calculation Results ◽  
The Central Nervous System ◽  
The Brain ◽  
Abnormal Brain

The brain in humans becomes part of the central nervous system of the human body. The use of imaging with MRI is one that can be used as a first step to detect parts of the human brain. The imaging step is the first step in diagnosing brain tumor. By performing feature extraction, which aims to process the classification of brain tumors, between normal and abnormal brain images using the naive Bayes method. Obtained 41 images which then became 39 datasets. Feature extraction results with 2 classes, normal as many as 20 data and abnormal data 19. The calculation results obtained the value of the normal class of 0.513 and the abnormal class of 0.487 the value of the calculation accuracy of 84.17%.

Classification of Trends in Lecturer Research Fields Using Naive Bayes Method

2021 ◽  
Author(s):  
Sulthan Rafif ◽  
Pramana Yoga Saputra ◽  
Moch Zawaruddin Abdullah
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Research Fields ◽  
Naive Bayes Method

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 Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain

2020 ◽  
Vol 7 (3) ◽  
pp. 599
Author(s):  
Arif Bijaksana Putra Negara ◽  
Hafiz Muhardi ◽  
Indira Melinda Putri
Keyword(s):  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Review Section ◽  
User Reviews ◽  
Average Value

<p class="Abstrak">Zaman sekarang tren masyarakat untuk memesan tiket pesawat sudah melalui situs-situs <em>booking</em> <em>online</em>. Pegipegi.com merupakan salah satu <em>website</em> yang menyediakan pemesanan tiket dan menyediakan fitur ulasan bagi pengunjung untuk menyampaikan opini. Pengunjung lain yang membaca ulasan-ulasan tersebut dapat memperoleh gambaran secara lebih objektif mengenai maskapai penerbangan. Ulasan pengguna yang terdapat pada website pegipegi.com saat ini sudah sangat banyak sehingga hal ini menyulitkan dan memakan waktu untuk membaca secara keseluruhan. Oleh karena itu dirancang analisis sentimen guna membantu mengklasifikasi ulasan kedalam kategori positif atau negatif sehingga dapat memberikan rekomendasi maskapai penerbangan berdasarkan jumlah kategori ulasan. Metode yang diterapkan untuk klasifikasi sentimen adalah Naïve Bayes dengan seleksi fitur <em>Information Gain</em>. Adapun tujuan dari penelitian ini adalah mengetahui pengaruh dari pemilihan fitur <em>Information Gain</em> terhadap akurasi klasifikasi dan membuktikan bahwa metode Naïve Bayes dengan <em>Information Gain</em> dapat digunakan untuk klasifikasi analisis sentimen. Hasil pengujian yang telah dilakukan menunjukkan bahwa nilai rata-rata akurasi, <em>precision</em>, <em>recall</em> setelah penambahan <em>Information Gain</em> menunjukkan hasil yang lebih baik sebesar 0,865 jika dibandingkan sebelum penambahan information gain yakni sebesar 0,81.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><em>Nowadays people tend to order airplane tickets through online booking sites. Pegipegi.com is a website that provides ticket reservations and a review section for visitors to express their opinions. Other visitors who read the reviews can get a more objective picture of airlines. The user reviews contained on the pegipegi.com website are currently very large so this makes it difficult and time consuming to read in its entirety. Therefore sentiment analysis is designed to help classify reviews into positive or negative categories so that they can provide airline recommendations based on the number of review categories. The method applied for sentiment classification is Naïve Bayes with the Information Gain feature selection. The purpose of this study was to determine the effect of selecting the Information Gain feature on classification accuracy and prove that the Naïve Bayes method with Information Gain can be used for the classification of sentiment analysis. The results of the tests that have been done show that the average value of accuracy, precision, recall after adding Information Gain shows better results of 0.865 compared to the addition of information gain which is equal to 0.81</em>.</em></p>

scholarly journals Naive Bayes Method for Classification of Student Interest Based on Website Accessed

2020 ◽  
Vol 1655 ◽  
pp. 012104
Author(s):  
Alwis Nazir ◽  
Amany Akhyar ◽  
Muhammad Ramadhani ◽  
Herlina
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Student Interest ◽  
Bayes Method ◽  
Naive Bayes Method

scholarly journals Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes

2020 ◽  
Vol 5 (3) ◽  
pp. 295
Author(s):  
Rahmawan Bagus Trianto ◽  
Andri Triyono ◽  
Dhika Malita Puspita Arum
Keyword(s):  
Feature Extraction ◽  
Naive Bayes ◽  
Automatic Classification ◽  
Naïve Bayes ◽  
Lack Of Information ◽  
N Gram ◽  
Bayes Algorithm ◽  
Online Product Ratings ◽  
Product Description

Online product ratings usually provide descriptive reviews and also reviews in the form of ratings. Likewise, what was done at the Lazada online store. Descriptive review can provide a clear view compared to a rating review to other potential buyers. However, in reality there is a mismatch between the description review and the rating given. This creates a lack of information for sellers as well as potential buyers. Automatic classification of buyer descriptive reviews is proposed in this study so that there is a match between descriptive reviews and rating reviews. This automatic classification descriptive review uses the Naive Bayes algorithm with n-gram feature extraction and TF-IDF word weighting. The results of this study obtained the best accuracy of 94.06%, a recall of 91.73% and precision of 90.71% in Bigram feature extraction. With this accuracy value it can be used as a reference or model for classifying product description reviews, so that the feedback process between sellers and buyers can run well.

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