scholarly journals CAMPUS SENTIMENT ANALYSIS E-COMPLAINT USING PROBABILISTIC NEURAL NETWORK ALGORITHM

Kursor ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 135
Author(s):  
Mohammad Zoqi Sarwani
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Electronic Systems ◽  
Testing Dataset ◽  
Testing Data ◽  
Neural Network Algorithm ◽  
Last Stage ◽  
Text Preprocessing

E-complaint is one of the technologies which is used to collect feedback from customers in the form of criticism and suggestions using electronic systems. For some companies or agencies, ecomplaint is used to provide better services to its customers. This study is aimed to perform sentiment analysis of an e-complaint service, with the case of Brawijaya University. There are three main stages for the proposed system, i.e. Text Preprocessing, Text Weighting, and PNN forthe classification. Tokenization, filtering, and stemming are done in the text preprocessing. Resulted text from the preprocessing stage is weighting using Term Inverse Document Frequent (TFIDF). To classify the negative or positive complaints, PNN are used in the last stage. For the experiments, 70 data are used as the training data, and 20 data are used as the testing data. The experimental results based on the combination of the number of training and testing dataset, showed that the accuracy achieved up to 90%.

scholarly journals Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 61-64
Author(s):  
Mohammad Zoqi Sarwani ◽  
Dian Ahkam Sani
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Probabilistic Neural Network ◽  
Media Analysis ◽  
Training Data ◽  
The Internet ◽  
Accuracy Score ◽  
Network Algorithm ◽  
The Social ◽  
Neural Network Algorithm

The Internet creates a new space where people can interact and communicate efficiently. Social media is one type of media used to interact on the internet. Facebook and Twitter are one of the social media. Many people are not aware of bringing their personal life into the public. So that unconsciously provides information about his personality. Big Five personality is one type of personality assessment method and is used as a reference in this study. The data used is the social media status from both Facebook and Twitter. Status has been taken from 50 social media users. Each user is taken as a text status. The results of tests performed using the Probabilistic Neural Network algorithm obtained an average accuracy score of 86.99% during the training process and 83.66% at the time of testing with a total of 30 training data and 20 test data.

scholarly journals Pengolahan Citra untuk Membedakan Ikan Segar dan Tidak Segar Menggunakan Convolutional Neural Network

2021 ◽  
Vol 5 (1) ◽  
pp. 11
Author(s):  
Arif Agustyawan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Independent Learning ◽  
Fresh Fish ◽  
Testing Data ◽  
Neural Network Algorithm ◽  
Sorting Process ◽  
Manual Methods

<p><em>Abstrak: </em></p><p>Proses penyortiran ikan yang dilakukan oleh nelayan atau penjual, untuk menyeleksi ikan berdasar kualitasnya masih menggunakan metode manual dan terkadang meleset karena faktor keterbatasan indra penglihatan ketika lelah. Selama ini pemeriksaan hanya dillihat secara fisik. Akibatnya, saat akan dikonsumsi ikan tersebut kerap kali sudah rusak. Penelitian ini mencoba menerapkan algoritma <em>Convolutional Neural Network</em> (CNN) untuk membedakan ikan segar dan tidak segar. <em>Convolutional Neural Network</em> merupakan salah satu metode <em>deep learning</em> yang mampu melakukan proses pembelajaran mandiri untuk pengenalan objek, ekstraksi objek, dan klasifikasi objek. Pada penelitian ini, diterapkan algoritma <em>Convolutional Neural Network</em> untuk membedakan ikan segar dan tidak segar. Proses <em>learning</em> jaringan menghasilkan akurasi 100% terhadap data <em>training</em> dan data <em>validation</em>. Pengujian terhadap data <em>testing</em> juga menghasilkan akurasi 100%. Hasil penelitian ini menunjukan bahwa penggunaan metode <em>Convolutional Neural Network</em> mampu mengidentifikasi dan mengklasifikasikan ikan segar dan tidak segar dengan sangat baik.</p><p><em>___________________________</em></p><p><em>Abstract:</em></p><p><em>The fish sorting process carried out by fishermen or sellers, to select fish based on quality is still using manual methods and sometimes misses due to the limited sense of sight when tired. So far the examination has only been seen physically. As a result, the fish will often be damaged when consumed. This study tries to apply the Convolutional Neural Network (CNN) algorithm to distinguish between fresh and non-fresh fish. Convolutional Neural Network is a method of deep learning that is capable of conducting independent learning processes for object recognition, object extraction, and object classification. In this study, the Convolutional Neural Network algorithm is applied to distinguish between fresh and non-fresh fish. Network learning process produces 100% accuracy of training data and data validation. Testing of testing data also results in 100% accuracy. The results of this study indicate that the use of the Convolutional Neural Network method can identify and classify fresh and non-fresh fish very well.</em></p>

Personality Classification through Social Media Using Probabilistic Neural Network Algorithms

2019 ◽  
Vol 1 (1) ◽  
pp. 9
Author(s):  
Mohammad Zoqi Sarwani ◽  
Dian Ahkam Sani ◽  
Fitria Chabsah Fakhrini
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Digital Media ◽  
Text Processing ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Modern Culture ◽  
Network Algorithm ◽  
The Status ◽  
Neural Network Algorithm

Today the internet creates a new generation with modern culture that uses digital media. Social media is one of the popular digital media. Facebook is one of the social media that is quite liked by young people. They are accustomed to conveying their thoughts and expression through social media. Text mining analysis can be used to classify one's personality through social media with the probabilistic neural network algorithm. The text can be taken from the status that is on Facebook. In this study, there are three stages, namely text processing, weighting, and probabilistic neural networks for determining classification. Text processing consists of several processes, namely: tokenization, stopword, and steaming. The results of the text processing in the form of text are given a weight value to each word by using the Term Inverse Document Frequent (TF / IDF) method. In the final stage, the Probabilistic Neural Network Algorithm is used to classify personalities. This study uses 25 respondents, with 10 data as training data, and 15 data as testing data. The results of this study reached an accuracy of 60%.

Improving Pattern Recognition Rate by Gaussian Hopfield Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 2042-2045 ◽  
Author(s):  
Shang Jen Chuang ◽  
Chiung Hsing Chen ◽  
Chien Chih Kao ◽  
Fang Tsung Liu
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Gaussian Distribution ◽  
Test Pattern ◽  
Recognition Rate ◽  
Hopfield Neural Network ◽  
Training Data ◽  
New Method ◽  
Gaussian Filter ◽  
Testing Data

English letters cannot be recognized by the Hopfield Neural Network if it contains noise over 50%. This paper proposes a new method to improve recognition rate of the Hopfield Neural Network. To advance it, we add the Gaussian distribution feature to the Hopfield Neural Network. The Gaussian filter was added to eliminate noise and improve Hopfield Neural Network’s recognition rate. We use English letters from ‘A’ to ‘Z’ as training data. The noises from 0% to 100% were generated randomly for testing data. Initially, we use the Gaussian filter to eliminate noise and then to recognize test pattern by Hopfield Neural Network. The results are we found that if letters contain noise between 50% and 53% will become reverse phenomenon or unable recognition [6]. In this paper, we propose to uses multiple filters to improve recognition rate when letters contain noise between 50% and 53%.

An Investigation of the Characteristics of a Bayesian Military Impulse Noise Classifier

2008 ◽  
Author(s):  
Brian Bucci ◽  
Jeffrey Vipperman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bayesian Methods ◽  
False Positive ◽  
Impulse Noise ◽  
False Negative ◽  
Training Data ◽  
Maximum Accuracy ◽  
First Case ◽  
Testing Data

In extension of previous methods to identify military impulse noise in the civilian environmental noise monitoring setting by means of a set of computed scalar metrics input to artificial neural network structures, Bayesian methods are investigated to classify the same dataset. Four interesting cases are identified and analyzed: A) Maximum accuracy achieve on training data, B) Maximum overall accuracy on blind testing data, C) Maximum accuracy on testing data with zero false positive detections, D) Maximum accuracy on testing data with zero false negative rejections. The first case is used to illustrative example and the later three represent actual monitoring modes. All of the cases are compared and contrasted to illuminate respective strengths and weaknesses. Overall accuracies of up to 99.8% are observed with no false negative rejections and accuracies of up to 98.4% are also achieved with no false positive detections.

scholarly journals Multi Class Data Classification to Improve Accuracy in Sentiment Analysis using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1457-1461
Author(s):  
Daram Vishnu
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Confusion Matrix ◽  
Training Data ◽  
Natural Languages ◽  
Parts Of Speech ◽  
Testing Data ◽  
Improve Accuracy ◽  
Textual Form ◽  
Speech Tagging

Sentiment analysis means classifying a text into different emotional classes. These days most of the sentiment analysis techniques divide the text into either binary or ternary classification in this paper we are classifying the movie reviews into 5 classes. Multi class sentiment analysis is a technique which can be used to know the exact sentiment of a review not just polarity of a given textual statement from positive to negative. So that one can know the precise sentiment of a review . Multi class sentiment analysis has always been a challenging task as natural languages are difficult to represent mathematically. The number of features are also generally large which requires huge computational power so to reduce the number of features we will use parts-of-speech tagging using textblob to extract the important features. Sentiment analysis is done using machine learning, where it requires training data and testing data to train a model. Various kinds of models are trained and tested at last one model is selected based on its accuracy and confusion matrix. It is important to analyze the reviews in textual form because large amount of reviews is present all over the web. Analyzing textual reviews can help the firms that are trying to find out the response of their products in the market. In this paper sentiment analysis is demonstrated by analyzing the movie reviews, reviews are taken from IMDB website.

scholarly journals ANALYSIS OF THE LOCATION OF NANNING LARGE-SCALE MALL BASED ON BP NEURAL NETWORK

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 975-979
Author(s):  
H. Huang ◽  
L. L. Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Site Selection ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Location Analysis ◽  
Analysis Model ◽  
Reference Information ◽  
Neural Network Algorithm

Abstract. Site selection is a key first step in the operation of large-scale shopping malls, and most of the existing site selection methods lack practicality and efficiency. Therefore, it is necessary to carry out a scientific modeling of the site selection problem and provide effective reference information for site selection. With the development of machine learning algorithms, the modeling of such problems becomes more and more simple. In this paper, using matlab software as a tool, based on BP neural network algorithm, Nanning urban area is selected as the research object. After analyzing the influencing factors of location problem, the large-scale mall location analysis modeling is carried out. After repeated training and testing of the training data and the test data, the data for testing the usability is input into the model and applied for analysis. It turns out that the large-scale mall location analysis model is usable and can meet the site selection needs of the mall.

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