scholarly journals Penentuan Tes Kepribadian Calon Mahasiswa Berdasarkan Sidik Jari Menggunakan Minutie dan Support Vector Machine

2020 ◽  
Vol 4 (1) ◽  
pp. 28-32
Author(s):  
Lukman Hakim
Keyword(s):  
Support Vector Machine ◽  
Input Image ◽  
The Body ◽  
Training Data ◽  
Support Vector ◽  
Human Being ◽  
Prospective Students ◽  
Study Program ◽  
Svm Algorithm ◽  
Fingerprint Pattern

Every human being is given its own uniqueness by an almighty god, one of which is a part of the body organs such as the fingerprint pattern of the hand, the fingerprint pattern of each human being determines personality, this can be known from many previous studies, which use fingerprints or someone's detection by the police to capture the perpetrators with the biometry approach in the form of footprint fingerprint records attached to other objects. Determination of a person's personality can be known through fingerprints, and also can adjust prospective students in choosing the study program correctly. Fingerprint student personality identification application provides convenience in determining the choice of prospective students of the study program. The minutie method and the Support Vector Machine algorithm are used in clustering personalities according to training data in the application. The minutie test on the fingerprint pattern shows 100% compatibility, with a precision input image source. SVM algorithm in testing reached 80,9% in grouping personality types accordingly.

scholarly journals Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Christ Memory Sitorus ◽  
Adhi Rizal ◽  
Mohamad Jajuli
Keyword(s):  
Support Vector Machine ◽  
User Satisfaction ◽  
Internet Technology ◽  
Training Data ◽  
Support Vector ◽  
Data Set ◽  
K Value ◽  
Movement Data ◽  
Svm Algorithm ◽  
Rbf Kernel

The ride-hailing service is now booming because it has been helped by internet technology, therefore many call this service online transportation. The magnitude of the potential for growth in online transportation service users also increases the risk of user satisfaction which could have declined therefore the company is increasing in its service. Both in terms of application and services provided by partners/drivers of the company. During each trip, the online transportation application will record device movement data and send it to the server. This data set is usually called telematic data. This telematics data if processed can have enormous benefits. In this study, an analysis will be conducted to predict the risk of online transportation trips using the Support Vector Machine (SVM) algorithm based on the obtained telematic data. The data obtained is telematic data so it must be processed first using feature engineering to obtain 51 features, then trained using the SVM algorithm with RBF kernel and modified C values. Every C value that is changed will be used K-Fold cross-validation first to separate the testing data and training data. The specified k value is 5. The results for each trial obtained accuracy, Receiver Operating Characteristic (ROC) and Area Under the Curves (AUC), for the best that is at C = 100 while the worst at C = 0.001.

scholarly journals Analisis Kinerja Support Vector Machine dalam Mengidentifikasi Komentar Perundungan pada Jejaring Sosial

2021 ◽  
Vol 5 (2) ◽  
pp. 475
Author(s):  
Ade Clinton Sitepu ◽  
Wanayumini Wanayumini ◽  
Zakarias Situmorang
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Confusion Matrix ◽  
Process Research ◽  
Training Data ◽  
Support Vector ◽  
Media Technology ◽  
Svm Classification ◽  
Svm Algorithm ◽  
Using Data

Cyberbullying is the same as bullying but it is done through media technology. Bullying has often occurred along with the development of social media technology in society. Some technique are needed to filter out bully comments because it will indirectly affect the psychological condition of the reader, morover it is aimed at the person concerned. By using data mining techniques, the system is expected to be able to classify information circulating in the community. This research uses the Support Vector Machine (SVM) classification because the algorithm is good at performing the classification process. Research using about 1000 dataset comments. Data are grouped manually first into the labels "bully" and "not bully" then the data divide into training data and test data. To test the system capability, data is analyzed using confusion matrix. The results showed that the SVM Algorithm was able to classify with an level of accuracy 87.75%, 89% precision and 91% Recal. The SVM algorithm is able to formulate training data with level of accuracy 98.3%

scholarly journals Hyperspectral Imaging for Color Adulteration Detection in Red Chili

2020 ◽  
Vol 10 (17) ◽  
pp. 5955 ◽  
Author(s):  
Muhammad Hussain Khan ◽  
Zainab Saleem ◽  
Muhammad Ahmad ◽  
Ahmed Sohaib ◽  
Hamail Ayaz ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Storage Conditions ◽  
Classification Performance ◽  
Training Data ◽  
Support Vector ◽  
Validation Data ◽  
Svm Algorithm ◽  
Sudan Dye ◽  
And Storage

The quality of red chili is characterized based on its color and pungency. Several factors like humidity, temperature, light, and storage conditions affect the characteristic qualities of red chili, thus preservation required several measures. Instead of ensuring these measures, traders are using oil and Sudan dye in red chili to increase the value of an inferior product. Thus, this work presents the feasibility of utilizing a hyperspectral camera for the detection of oil and Sudan dye in red chili. This study describes the important wavelengths (500–700 nm) where different adulteration affects the response of the reflected spectrum. These wavelengths are then utilized for training an Support Vector Machine (SVM) algorithm to detect pure, oil-adulterated, and Sudan dye-adulterated red chili. The classification performance achieves 97% with the reduced dimensionality and 100% with complete validation data. The trained algorithm is further tested on separate data with different adulteration levels in comparison to the training data. Results show that the algorithm successfully classifies pure, oil-adulterated, and Sudan-adulterated red chili with an accuracy of 100%.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

scholarly journals Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

2019 ◽  
Vol 6 (5) ◽  
pp. 190001 ◽  
Author(s):  
Katherine E. Klug ◽  
Christian M. Jennings ◽  
Nicholas Lytal ◽  
Lingling An ◽  
Jeong-Yeol Yoon
Keyword(s):  
Heavy Metal ◽  
Support Vector Machine ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Mie Scattering ◽  
Training Data ◽  
Scattering Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Analysis

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

Lung Cancer Detection using Image Processing Techniques and its Classification

2021 ◽  
Vol 9 (9) ◽  
pp. 1578-1583
Author(s):  
Aishwarya .R
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Support Vector Machine ◽  
Cell Carcinoma ◽  
Mortality Rates ◽  
The Body ◽  
Support Vector ◽  
Lung Tumour ◽  
Image Processing Techniques ◽  
Processing Techniques

Abstract: Lung cancer has been a major contribution to mortality rates world-wide for many years now. There is a need for early diagnosis of lung cancer which if implemented, will help in reducing mortality rates. Recently, image processing techniques have been widely applied in various medical facilities for accurate detection and diagnosis of abnormality in the body images like in various cancers such as brain tumour, breast tumour and lung tumour. This paper is a development of an algorithm based on medical image processing to segment the lung tumour in CT images due to the lack of such algorithms and approaches used to detect tumours. The work involves the application of different image processing tools in order to arrive at the desired result when combined and successively applied. The segmentation system comprises different steps along the process. First, Image preprocessing is done where some enhancement is done to enhance and reduce noise in images. In the next step, the different parts in the images are separated to be able to segment the tumour. In this phase threshold value was selected automatically. Then morphological operation (Area opening) is implemented on the thresholded image. Finally, the lung tumour is accurately segmented by subtracting the opened image from the thresholded image. Support Vector Machine (SVM) classifier is used to classify the lung tumour into 4 different types: Adenocarcinoma(AC), Large Cell Carcinoma(LCC) Squamous Cell Carcinoma(SCC), and No tumour (NT). Keywords: Lung tumour; image processing techniques; segmentation; thresholding; image enhancement; Support Vector Machine; Machine learning;

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Analisis Sentimen Data Twitter Tentang Pasangan Capres-Cawapres Pemilu 2019 Dengan Metode Lexicon Based Dan Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 144
Author(s):  
Danar Wido Seno ◽  
Arief Wibowo
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Vice President ◽  
Training Data ◽  
Support Vector ◽  
New Words ◽  
Textual Data ◽  
Data Content ◽  
Combination Of Methods

Social media writing content growing make a lot of new words that appear on Twitter in the form of words and abbreviations that appear so that sentiment analysis is increasingly difficult to get high accuracy of textual data on Twitter social media. In this study, the authors conducted research on sentiment analysis of the pairs of candidates for President and Vice President of Indonesia in the 2019 Elections. To obtain higher accuracy results and accommodate the problem of textual data development on Twitter, the authors conducted a combination of methods to conduct the sentiment analysis with unsupervised and supervised methods. namely Lexicon Based. This study used Twitter data in October 2018 using the search keywords with the names of each pair of candidates for President and Vice President of the 2019 Elections totaling 800 datasets. From the study with 800 datasets the best accuracy was obtained with a value of 92.5% with 80% training data composition and 20% testing data with a Precision value in each class between 85.7% - 97.2% and Recall value for each class among 78, 2% - 93.5%. With the Lexicon Based method as a labeling dataset, the process of labeling the Support Vector Machine dataset is no longer done manually but is processed by the Lexicon Based method and the dictionary on the lexicon can be added along with the development of data content on Twitter social media.

scholarly journals Structural Damage Detection Using Supervised Nonlinear Support Vector Machine

2021 ◽  
Vol 5 (11) ◽  
pp. 303
Author(s):  
Kian K. Sepahvand
Keyword(s):  
Support Vector Machine ◽  
Damage Detection ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Training Data ◽  
Support Vector ◽  
Lightweight Structures ◽  
Straightforward Method ◽  
Classification Boundary ◽  
Nonlinear Support

Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.

scholarly journals Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine

2014 ◽  
Vol 8 (1) ◽  
pp. 91 ◽  
Author(s):  
Noviah Dwi Putranti ◽  
Edi Winarko
Keyword(s):  
Support Vector Machine ◽  
Maximum Entropy ◽  
Social Networking Site ◽  
Training Data ◽  
Classification Model ◽  
Support Vector ◽  
Public Sentiment ◽  
Pos Tagger ◽  
Negative Sentiment ◽  
Bahasa Indonesia

AbstrakAnalisis sentimen dalam penelitian ini merupakan proses klasifikasi dokumen tekstual ke dalam dua kelas, yaitu kelas sentimen positif dan negatif.  Data opini diperoleh dari jejaring sosial Twitter berdasarkan query dalam Bahasa Indonesia. Penelitian ini bertujuan untuk menentukan sentimen publik terhadap objek tertentu yang disampaikan di Twitter dalam bahasa Indonesia, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data yang sudah terkumpul dilakukan proses preprocessing dan POS tagger untuk menghasilkan model klasifikasi melalui proses pelatihan. Teknik pengumpulan kata yang memiliki sentimen dilakukan dengan pendekatan berdasarkan kamus, yang dihasilkan dalam penelitian ini berjumlah 18.069 kata. Algoritma Maximum Entropy digunakan untuk POS tagger dan algoritma yang digunakan untuk membangun model klasifikasi atas data pelatihan dalam penelitian ini adalah Support Vector Machine. Fitur yang digunakan adalah unigram dengan fitur pembobotan TFIDF. Implementasi klasifikasi diperoleh akurasi 86,81 %  pada pengujian 7 fold cross validation untuk tipe kernel Sigmoid. Pelabelan kelas secara manual dengan POS tagger menghasilkan akurasi 81,67%.  Kata kunci—analisis sentimen, klasifikasi, maximum entropy POS tagger, support vector machine, twitter.  AbstractSentiment analysis in this research classified textual documents into two classes, positive and negative sentiment. Opinion data obtained a query from social networking site Twitter of Indonesian tweet. This research uses  Indonesian tweets. This study aims to determine public sentiment toward a particular object presented in Twitter businesses conduct market. Collected data then prepocessed to help POS tagged to generate classification models through the training process. Sentiment word collection has done the dictionary based approach, which is generated in this study consists 18.069 words. Maximum Entropy algorithm is used for POS tagger and the algorithms used to build the classification model on the training data is Support Vector Machine. The unigram features used are the features of TFIDF weighting.Classification implementation 86,81 % accuration at examination of 7 validation cross fold for the type of kernel of Sigmoid. Class labeling manually with POS tagger yield accuration 81,67 %. Keywords—sentiment analysis, classification, maximum entropy POS tagger, support vector machine, twitter.

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