scholarly journals PREDIKSI FITUR KEMASAN PRODUK MINYAK KAYU PUTIH DENGAN SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Vol 4 ◽  
pp. 76-82
Author(s):  
Wilma Latuny ◽  
Victor O. Lawalata ◽  
Daniel B. Paillin ◽  
Rahman Ohoirenan
Keyword(s):  
Support Vector Machine ◽  
Online Survey ◽  
Accurate Method ◽  
Survey Method ◽  
Support Vector ◽  
Consumer Sentiment ◽  
Product Packaging ◽  
Eucalyptus Oil ◽  
Positive Class ◽  
Negative Class

UD Sinar Baru has eucalyptus oil products with various sizes from 30 ml to 550 ml, and the size of 550 ml is the most consumed eucalyptus oil product. However, this product has been criticized by consumers for its packaging which has not met their expectations. This study aims to obtain an accurate method of classifying consumer sentiment and obtain features that affect the redesign of the 550 ml eucalyptus oil product packaging. Collecting data using an online survey method from social media Facebook to get consumer comments using power queries. Data analysis uses the concept of the Support Vector Machine (SVM) method with the support of the WEKA application to provide sentiment analysis and accuracy of consumer comments. The results of the study present the tendency of comments on each attribute with an assessment of 83% accuracy for the entire class, 3% for positive class comments, and 57% comments for negative class. The sentiment that shows the packaging tends to be normal at 20% which is interpreted as neutral. The conclusion from the results of this study is that SMO has a very accurate prediction rate to analyze consumer sentiment about the features of the 550 ml eucalyptus oil packaging, and it is necessary to redesign the current packaging by considering the features of shape, color, size, and efficiency.

scholarly journals SENTIMENT ANALYSIS OF THE LARGE PRIEST OF FPI’S RETURN USING SUPPORT VECTOR MACHINE WITH OVERSAMPLING METHOD

2021 ◽  
Vol 4 (1) ◽  
pp. 17-22
Author(s):  
Zetta Nillawati Reyka Putri ◽  
Muhammad Muhajir
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Test Results ◽  
Text Data ◽  
Machine Method ◽  
Svm Classification ◽  
The Public ◽  
Positive Class ◽  
Negative Class

At the end of 2020, Habib Rizieq's return to Indonesia drew criticism from the public for causing crowds during the Covid-19 pandemic. News and opinions about Habib Rizieq fill internet platforms, including Twitter. The researcher wants to classify the opinion text data of Habib Rizieq's return from Twitter into positive and negative sentiments using the Support Vector Machine method. Opinion data comes from Twitter, so the data is analyzed by text mining through the preprocessing stage. The SVM classification of unbalanced data between positive and negative classes resulted in 95.06% accuracy with a negative class precision value of 84% and better than 72% recall, in the positive class the precision value was 96% less than 2% of recall 98%. While the SVM classification with the oversampling method gets 100% accuracy, precision, and recall. The results of positive sentiments are known that the public will always support and want freedom for Rizieq, for negative sentiments it is known that many people are disappointed with Rizieq regarding the lies of his swab test results.

scholarly journals An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1102-1110 ◽  
Author(s):  
Yu Wu ◽  
Yanjie Lu
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Rapid Development ◽  
Training Model ◽  
Support Vector ◽  
Ideal Condition ◽  
Product Packaging

Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

scholarly journals Multi-aspect sentiment analysis on netflix application using latent dirichlet allocation and support vector machine methods

2021 ◽  
Vol 13 (3) ◽  
pp. 128-133
Author(s):  
Attala Rafid Abelard ◽  
Yuliant Sibaroni
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
The Other ◽  
Support Vector ◽  
Performance Score ◽  
Negative Class ◽  
Google Play ◽  
Dirichlet Allocation

Among many film streaming platforms that have sprung up, Netflix is ​​the platform that has the most subscribers compared to the other platforms. However, not all reviews provided by the Netflix users are good reviews. These reviews will later be analyzed to determine what aspects are reviewed by the users based on reviews written on the Google Play Store, using the Latent Dirichlet Allocation (LDA) method. Then, the classification process using the Support Vector Machine (SVM) method will be carried out to determine whether each of these reviews is included in the positive or negative class (Sentiment Analysis). There are 2 scenarios that were carried out in this study. The first scenario resulted that the best number of LDA topics to be used is 40, and the second scenario resulted that the use of filtering process in the preprocessing stage reduces the score of the f1-score. Thus, this study resulted in the best performance score on LDA and SVM testing with 40 topics, and without running the filtering process with the score of 78.15%.

scholarly journals Mapping problem using text mining to boost tourism industry: is it possible?

2021 ◽  
Vol 778 (1) ◽  
pp. 012009
Author(s):  
M Tamrin ◽  
L Septianasari
Keyword(s):  
Text Mining ◽  
Information Extraction ◽  
Online Reviews ◽  
Tourism Industry ◽  
Support Vector ◽  
Mapping Problem ◽  
Tourist Destinations ◽  
Online Platforms ◽  
Positive Class ◽  
Negative Class

Abstract TripAdvisor has become a credential traveling platform for tourists worldwide to set travel plans. The widespread of big data in online platforms urges the use of text mining to benefit some sectors, including in the tourism industry. This study aimed to investigate the information extraction based on the online reviews on TripAdvisor for Gili Trawangan tourist destinations. The method used in this research was text mining with Support Vector Machine (SVM) to classify the online reviews that categorized into two classes, positive class and negative class. The results of information extraction show that the issue of horse cruelty, bad waste management, and ecosystem vulnerability dominated the negative sentiments. These negative sentiments need to be handled professionally by the tourism enterprise to boost the tourism industry in Gili Trawangan.

Rapid Detection of Melamine in Milk Powder by near Infrared Spectroscopy

2009 ◽  
Vol 17 (2) ◽  
pp. 59-67 ◽  
Author(s):  
Chenghui Lu ◽  
Bingren Xiang ◽  
Gang Hao ◽  
Jianping Xu ◽  
Zhengwu Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Rapid Detection ◽  
Near Infrared ◽  
Milk Powder ◽  
Nir Spectroscopy ◽  
Accurate Method ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector

This paper establishes a novel and rapid method for detecting pure melamine in milk powder using near infrared (NIR) spectroscopy based on least squares-support vector machine (LS-SVM). Partial least square discriminant analysis (PLS-DA) was used for the extraction of principal components (PCs). The scores of the first two PCs have been applied as inputs to LS-SVM. Compared to PLS-DA, the performance of LS-SVM was better, with higher classification accuracy, both 100% for the training and testing set. The detection limit was lower than 1 ppm. Based on the results, it was concluded that NIR spectroscopy combined with LS-SVM could be used as a rapid and accurate method for detecting pure melamine in milk powder.

scholarly journals Cyberbullying identification in twitter using support vector machine and information gain based feature selection

2020 ◽  
Vol 18 (3) ◽  
pp. 1494
Author(s):  
Ni Made Gita Dwi Purnamasari ◽  
M. Ali Fauzi ◽  
Indriati Indriati ◽  
Liana Shinta Dewi
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Information Gain ◽  
Support Vector ◽  
Term Weighting ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Positive Class ◽  
Media Applications ◽  
F Measure

<span>Cyberbullying is one of the actions that violate the ITE Law where the crime is committed on social media applications such as Twitter. This action is difficult to detect if no one is reporting the tweet. Cyberbullying tweet identification aims to classify tweets that contain bullying. Classification is done using Support Vector Machine method where this method aims to find the dividing hyperplane between negative and positive class. This study is a text classification where more data is used, the more features are produced, therefore this research also uses Information Gain as feature selection to select features that are not relevant to the classification. The process of the system starts from text preprocessing with tokenizing, filtering, stemming and term weighting. Then perform the information gain feature selection by calculating the entropy value of each term. After that perform the classification process based on the terms that have been selected, and the output of the system is identification whether the tweet is bullying or not. The result of using SVM method is accuracy 75%, precision 70.27%, recall 86.66% and f-measure 77.61% on experiment maximum iteration = 20, λ = 0.5, γ = 0.001, ε = 0.000001, and C = 1. The best threshold of information gain is 90%, with accuracy 76.66%, precision 72.22%, recall 86.66% and f-measure 78.78%.</span>

scholarly journals PERCEIVING DIGITAL WATERMARK DETECTION AS IMAGE CLASSIFICATION PROBLEM

2016 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
P. Then ◽  
Y.C. Wang
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
False Negative ◽  
Digital Watermark ◽  
Classification Problem ◽  
Support Vector ◽  
Svm Classifier ◽  
Operating Characteristics ◽  
Negative Class ◽  
Correlation Detector

Digital watermark detection is treated as classification problem of image processing. For image classification that searches for a butterfly, an image can be classified as positive class that is a butterfly and negative class that is not a butterfly. Similarly, the watermarked and unwatermarked images are perceived as positive and negative class respectively. Hence, Support Vector Machine (SVM) is used as the classifier of watermarked and unwatermarked digital image due to its ability of separating both linearly and non-linearly separable data. Hyperplanes of various detectors are briefly elaborated to show how SVM's hyperplane is suitable for Stirmark attacked watermarked image. Cox’s spread spectrum watermarking scheme is used to embed the watermark into digital images. Then, Support Vector Machine is trained with both the watermarked and unwatermarked images. Training SVM eliminates the use of watermark during the detection process. Receiver Operating Characteristics (ROC) graphs are plotted to assess the false positive and false negative probability of both the correlation detector of the watermarking schemes and SVM classifier. Both watermarked and unwatermarked images are later attacked under Stirmark, and then tested on the correlation detector and SVM classifier. Remedies are suggested to preprocess the training data. The optimal setting of SVM parameters is also investigated and determined besides preprocessing. The preprocessing and optimal parameters setting enable the trained SVM to achieve substantially better results than those resulting from the correlation detector.

P-SMOTE: One Oversampling Technique for Class Imbalanced Text Classification

2011 ◽  
Author(s):  
Jingjing Wang ◽  
Wen Feng Lu ◽  
Han Tong Loh
Keyword(s):  
Text Classification ◽  
Information Source ◽  
Support Vector ◽  
Svm Classifier ◽  
Imbalanced Dataset ◽  
Patent Classification ◽  
Basic Issue ◽  
Positive Class ◽  
Standard Text ◽  
Negative Class

The importance of mining patents to support product design has been recognized, because patents are the major information source to support innovation and contain novel ideas, which usually cannot be found in published academic papers. In patent text mining, a basic issue is patent classification. However, automatic patent classification is difficult. One potential cause of the difficulty is the imbalanced dataset i.e. the interested positive class is minor while uninterested negative class is major. To alleviate the problem of imbalanced dataset and improve the performance of a Support Vector Machine (SVM) classifier, this study proposes P-SMOTE, a new oversampling technique which focuses on the blank spaces along positive borderline of a SVM. The proposed technique was firstly investigated on Reuters-21578, which is a standard text classification dataset. Then, P-SMOTE was applied to a design patent document dataset. It was observed that a SVM classifier with P-SMOTE, compared to a SVM classifier only, successfully achieved better results.

scholarly journals Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Divya Tomar ◽  
Sonal Agarwal
Keyword(s):  
Support Vector Machine ◽  
Multiple Instance Learning ◽  
Experimental Results ◽  
Training Dataset ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Learning Approaches ◽  
Class Label ◽  
Positive Class ◽  
Post Hoc

In multiple instance learning (MIL) framework, an object is represented by a set of instances referred to as bag. A positive class label is assigned to a bag if it contains at least one positive instance; otherwise a bag is labeled with negative class label. Therefore, the task of MIL is to learn a classifier at bag level rather than at instance level. Traditional supervised learning approaches cannot be applied directly in such kind of situation. In this study, we represent each bag by a vector of its dissimilarities to the other existing bags in the training dataset and propose a multiple instance learning based Twin Support Vector Machine (MIL-TWSVM) classifier. We have used different ways to represent the dissimilarity between two bags and performed a comparative analysis of them. The experimental results on ten benchmark MIL datasets demonstrate that the proposed MIL-TWSVM classifier is computationally inexpensive and competitive with state-of-the-art approaches. The significance of the experimental results has been tested by using Friedman statistic and Nemenyi post hoc tests.

scholarly journals Algoritma Naïve Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah

2019 ◽  
Vol 7 (2) ◽  
pp. 77-82 ◽  
Author(s):  
Nurajijah Nurajijah ◽  
Dwiza Riana
Keyword(s):  
Support Vector Machine ◽  
High Risk ◽  
Decision Tree ◽  
Naive Bayes ◽  
Accurate Method ◽  
Naïve Bayes ◽  
Support Vector ◽  
History Data ◽  
Bayes Algorithm

The decision on financing approval in sharia cooperatives has a high risk of the inability of customers to pay their credit obligations at maturity or referred to as bad credit. To maintain and minimize risk, an accurate method is needed to determine the financing agreement. The purpose of this study is to classify sharia cooperative loan history data using the Naïve Bayes algorithm, Decision Tree and SVM to predict the credibility of future customers. The results showed the accuracy of Naïve Bayes algorithm 77.29%, Decision Tree 89.02% and the highest Support Vector Machine (SVM) 89.86%.

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