scholarly journals String-based Multinomial Naïve Bayes for Emotion Detection among Facebook Diabetes Community

2019 ◽  
Vol 159 ◽  
pp. 30-37
Author(s):  
Vimala Balakrishnan ◽  
Wandeep Kaur
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Emotion Detection

KLASIFIKASI GENDER BERDASARKAN SUARA DENGAN NAIVE BAYES DAN MEL FREQUENCY CEPSTRAL COEFFICIENT

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Hery Wiharja MS ◽  
Sri Rahayu ◽  
Evi Rahmiyati
Keyword(s):  
Speech Recognition ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Emotion Detection ◽  
Bayes Method ◽  
Sample Data ◽  
Attribute Model ◽  
Sound Patterns ◽  
Mel Frequency Cepstral Coefficient ◽  
Naive Bayes Method

Abstract For humans, recognizing sounds is an easy thing, by listening carefully and understandingly to what is spoken and humans have intelligence in recognizing sound patterns. Unlike computers, the speech recognition process is a difficult process, this is because the computer requires a standard and logical mechanism to recognize sound patterns. With Mel Frequency Cepstral Coefficient (MFCC) method has an important role in determining the characteristics of a sound. This method is often used for verification of voice, speech recognition, emotion detection of voice. To perform the classification in this study using Naïve Bayes method. The Naive Bayes method is a classification method. In which the classification process in the naïve Bayes method is based on the probability of the data as evidence in probability. The model used in the Naive Bayes method is the independent attribute model. The accuracy rate in this research was 87%. It is based on the amount of data testing 100 samples, the true classified as 87 samples of data while false classified as 13 sample data. Keywords: Voice Recognition; Naïve Bayes; Mel-Frequency Cepstral Coefficient. __________________________ Abstrak Bagi manusia mengenali suara merupakan hal yang mudah, dengan cara mendengarkan dengan seksama dan manusia mempunyai kecerdasan dalam mengenali pola suara. Berbeda dengan komputer, proses pengenalan suara merupakan proses yang sulit, hal ini dikarenakan komputer memerlukan suatu mekanisme yang standar dan logis dalam mengenali pola suara. Dengan metode Mel Frequency Cepstral Coefficient (MFCC) memiliki peran penting dalam menentukan karakteristik dari sebuah suara. Metode ini sering digunakan untuk verifikasi suara, pengenalan suara, deteksi emosi dari suara. Untuk melakukan klasifikasi pada penelitian ini menggunakan metode Naïve Bayes. Metode Naive Bayes merupakan salah satu metode klasifikasi, yang mana proses klasifikasi pada metode naïve bayes berdasarkan dari probabilitas dari data sebagai bukti dalam probalitas. Model yang digunakan pada metode Naive Bayes adalah model atribut independent. Dalam penelitian ini, data suara yang digunakan pada penelitian ini berupa data suara yang direkam mengunkan perekam suara dengan durasi rekaman suara maksimal 30 detik. Tingkat keberhasilan dalam penelitian ini sebesar 87%. Hal ini berdasarkan dari jumlah data pengujian 100 sampel, yang benar diklasifikasi sebanyak 87 sampel data sedangkan yang salah diklasifikasi sebanyak 13 data sampel suara. Kata Kunci: Pengenalan Suara; Naïve Bayes; Mel-Frequency Cepstral Coefficien. __________________________

KLASIFIKASI GENDER BERDASARKAN SUARA DENGAN NAIVE BAYES DAN MEL FREQUENCY CEPSTRAL COEFFICIENT

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Safriadi Safriadi ◽  
Rahmadani Rahmadani
Keyword(s):  
Speech Recognition ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Emotion Detection ◽  
Bayes Method ◽  
Sample Data ◽  
Attribute Model ◽  
Sound Patterns ◽  
Mel Frequency Cepstral Coefficient ◽  
Naive Bayes Method

Bagi manusia mengenali suara merupakan hal yang mudah, dengan cara mendengarkan dengan seksama dan manusia mempunyai kecerdasan dalam mengenali pola suara. Berbeda dengan komputer, proses pengenalan suara merupakan proses yang sulit, hal ini dikarenakan komputer memerlukan suatu mekanisme yang standar dan logis dalam mengenali pola suara. Dengan metode Mel Frequency Cepstral Coefficient (MFCC) memiliki peran penting dalam menentukan karakteristik dari sebuah suara. Metode ini sering digunakan untuk verifikasi suara, pengenalan suara, deteksi emosi dari suara. Untuk melakukan klasifikasi pada penelitian ini menggunakan metode Naïve Bayes. Metode Naive Bayes merupakan salah satu metode klasifikasi, yang mana proses klasifikasi pada metode naïve bayes berdasarkan dari probabilitas dari data sebagai bukti dalam probalitas. Model yang digunakan pada metode Naive Bayes adalah model atribut independent. Dalam penelitian ini, data suara yang digunakan pada penelitian ini berupa data suara yang direkam mengunkan perekam suara dengan durasi rekaman suara maksimal 30 detik. Tingkat keberhasilan dalam penelitian ini sebesar 87%. Hal ini berdasarkan dari jumlah data pengujian 100 sampel, yang benar diklasifikasi sebanyak 87 sampel data sedangkan yang salah diklasifikasi sebanyak 13 data sampel suara. For humans, recognizing sounds is an easy thing, by listening carefully and understandingly to what is spoken and humans have intelligence in recognizing sound patterns. Unlike computers, the speech recognition process is a difficult process, this is because the computer requires a standard and logical mechanism to recognize sound patterns. With Mel Frequency Cepstral Coefficient (MFCC) method has an important role in determining the characteristics of a sound. This method is often used for verification of voice, speech recognition, emotion detection of voice. To perform the classification in this study using Naïve Bayes method. The Naive Bayes method is a classification method. In which the classification process in the naïve Bayes method is based on the probability of the data as evidence in probability. The model used in the Naive Bayes method is the independent attribute model. The accuracy rate in this research was 87%. It is based on the amount of data testing 100 samples, the true classified as 87 samples of data while false classified as 13 sample data.

Emotion Detection of Twitter Post using Multinomial Naive Bayes

2020 ◽  
Author(s):  
Nazia Anjum Sharupa ◽  
Minhaz Rahman ◽  
Nasif Alvi ◽  
M. Raihan ◽  
Afsana Islam ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Emotion Detection

Study of Sentiment of Governor's Election Opinion in 2018

2018 ◽  
pp. 231-238
Author(s):  
Agung Eddy Suryo Saputro ◽  
Khairil Anwar Notodiputro ◽  
Indahwati A
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Addition Method ◽  
Sentiment Mining ◽  
Positive Sentiment ◽  
Trending Topics ◽  
Election News

In 2018, Indonesia implemented a Governor's Election which included 17 provinces. For several months before the Election, news and opinions regarding the Governor's Election were often trending topics on Twitter. This study aims to describe the results of sentiment mining and determine the best method for predicting sentiment classes. Sentiment mining is based on Lexicon. While the methods used for sentiment analysis are Naive Bayes and C5.0. The results showed that the percentage of positive sentiment in 17 provinces was greater than the negative and neutral sentiments. In addition, method C5.0 produces a better prediction than Naive Bayes.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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