scholarly journals Sentiment analysis on myindihome user reviews using support vector machine and naïve bayes classifier method

2021 ◽  
Vol 2 (2) ◽  
pp. 141
Author(s):  
Murman Dwi Prasetio ◽  
Rais Yufli Xavier ◽  
Haris Rachmat ◽  
Wiyono Wiyono ◽  
Denny Sukma Eka Atmaja
Keyword(s):  
Support Vector Machine ◽  
Industrial Development ◽  
Rapid Development ◽  
Raspberry Pi ◽  
Support Vector ◽  
Linear Kernel ◽  
Svm Algorithm ◽  
Supervised Learning Algorithms ◽  
Clay Tiles

The strength of the company's competitiveness is needed because the current industrial development is very rapid. It is necessary to maintain the quality and quantity of the products produced according to company standards.  One of the companies that must maintain the quality and quantity is PT. XYZ is a clay tile company. The classification of products used by this company to maintain good quality is three classes: good tile, white stone tile, and cracked tile. However, quality control based on classification still uses the traditional way by relying on sight.  It can increase errors and slow down the process. It can be overcome with artificial visual detectors. It is a result of the rapid development of automation. So to detect defects, this research can use image preprocessing, supervised learning algorithms, and measurement methods.  Support Vector Machine (SVM) is used in this study to perform classification, while feature extraction on clay tiles used the Local Binary Pattern (LBP) method. The algorithm is made using python, while for image retrieval, raspberry pi is used. The linear kernel on the SVM algorithm is used in this study. The conclusion in this study obtained 86.95% is the highest accuracy with a linear kernel. It takes 10.625 seconds to classify.

Analysis of HCV’s Linearity Using Support Vector Machine (SVM)

2014 ◽  
Vol 548-549 ◽  
pp. 1265-1269
Author(s):  
Yun Sik Hwang ◽  
Byeong Joo Jun ◽  
Tae Seon Yoon
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Hepatitis C Virus ◽  
Data Analysis ◽  
Hepatitis C ◽  
Amino Acid ◽  
Support Vector ◽  
Svm Algorithm ◽  
Genetic Patterns

As the stage of bioinformatics has been upgraded, classification of certain pathogen has been improved into a new manner. The main topic of this research is genetic singularity of HCV (Hepatitis C Virus) and our objective is to assay features of the HCV's amino acid under usage of Support Vector Machine (SVM) algorithm. HCV data used in our experiment has 10 kinds of sequences and 257 kinds of data. According to data analysis, some peculiar genetic patterns of HCV’s linearity that discord pre-existing neural network and C5.0 were found.

scholarly journals Auto Classification of Biomass through Characterization of their Pyrolysis Behaviors by using Thermogravimetric Analysis with Support Vector Machine Algorithm: Case Study for Tobacco

2020 ◽  
Author(s):  
Chao Yin ◽  
Xiaohua Deng ◽  
Zhiqiang Yu ◽  
Ruting Chen ◽  
Hongxiang Zhong ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Thermogravimetric Analysis ◽  
Oil Quality ◽  
Support Vector ◽  
Analysis Method ◽  
Biomass Pyrolysis ◽  
Svm Algorithm ◽  
Bio Oil

Abstract Background: During the biomass-to-bio-oil conversion process, many researches focus on the study of the association between the biomass and the bio-products by using near infrared spectra (NIR) and chemical analysis method. However, the characterization of biomass pyrolysis behaviors by using thermogravimetric analysis (TGA) with support vector machine (SVM) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar and gases) released by tobacco pyrolysis reactions decide the sensory quality, which is similar to the use of biomass as a renewable resource through the pyrolysis process. Results: Support vector machine (SVM) has been employed to automatically classify the planting area and growing position of tobacco leaves by using thermogravimetric analysis data as the information source for the first time. 88 single-grade tobacco samples belonging to 4 grades and 8 categories were split into the training, validation and blind testing set. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower and middle positions. Error only occurs in the classification of subgrades of the middle position. Conclusions: Our results not only validated the feasibility of using thermogravimetric analysis with SVM algorithm as an objective and rapid method for automatic classification of tobacco planting area and growing position, but also showed this new analysis method would be a promising way to exploring bio-oil quality prior to biomass pyrolysis production.

scholarly journals Alumni Info-Com with Distinct Classification of Data using Support Vector Machine Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 3363-3367
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Single System ◽  
Web Based ◽  
Svm Algorithm ◽  
Alumni Associations

This paper presents to create a centralized alumni network for betterment of institutions and upcoming student’s community. The System is able to collect and store alumni information for future communication. Former students of institution can communicate with their immediate friends as well as forthcoming students and various members involved in the institution community. Apart from the alumni, the institutions/organization also benefitted when sustains this network. This single system can satisfy almost every requirement of the alumni. Usually, alumni associations are organized in colleges, but may also be organized in a place where the alumni can meet each other. Despite the fact that there are many existing systems in colleges to maintain the alumni information, they are manual and more time consuming to current students to reach out their alumni and maintaining the privacy of the alumni. To overcome these issues, we proposed a web based application which allows alumni to update their information and students can connect with them and can view the filtered events posted by alumni and admin through Support Vector Machine algorithm (SVM). Proposed method, SVM algorithm used to classify the alumni members and their posting message from others in this community.

scholarly journals Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA

2020 ◽  
Vol 12 (4) ◽  
pp. 623 ◽  
Author(s):  
Mutiara Syifa ◽  
Mahdi Panahi ◽  
Chang-Wook Lee
Keyword(s):  
Support Vector Machine ◽  
Hybrid Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Burned Area ◽  
Support Vector ◽  
Landsat 8 ◽  
Svm Algorithm ◽  
Mitigation Planning ◽  
Sentinel 2

On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a pre- and post-wildfire maps to provide basic data for evacuation and mitigation planning. This study used Landsat-8 and Sentinel-2 imagery to map the pre- and post-wildfire conditions. A support vector machine (SVM) optimized by the imperialist competitive algorithm (ICA) hybrid model was compared with the non-optimized SVM algorithm for classification of the pre- and post-wildfire map. The SVM–ICA produced a better accuracy (overall accuracies of 83.8% and 83.6% for pre- and post-wildfire using Landsat-8 respectively; 90.8% and 91.8% for pre- and post-wildfire using Sentinel-2 respectively), compared to SVM without optimization (overall accuracies of 80.0% and 78.9% for pre- and post-wildfire using Landsat-8 respectively; 83.3% and 84.8% for pre- and post-wildfire using Sentinel-2 respectively. In total, eight pre- and post-wildfire burned area maps were generated; these can be used to assess the area affected by the Camp Fire wildfire as well as for wildfire mitigation planning in the future.

scholarly journals ANALISIS SENTIMEN PENGGUNA GOPAY MENGGUNAKAN METODE LEXICON BASED DAN SUPPORT VECTOR MACHINE

KOMPUTEK ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 52
Author(s):  
Rachmad Mahendrajaya ◽  
Ghulam Asrofi Buntoro ◽  
Moh Bhanu Setyawan
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Open Access ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Negative Comments

Go-Pay is part of the Gojek application and one of the most popular finteches in Indonesia. Although the most popular, not all users have positive or even negative comments. Now users can submit various media opinions, one of which is Twitter. Twitter media has the advantage of a simple display, updated topics, open access to tweets and express opinions quickly. From a variety of comments on Twitter it takes a technique to divide into classes positive or negative opinions. This study uses prepocessing and labeling opinions into positive and negative classes with the lexicon Based method. As for the classification using the Support Vector Machine (SVM) method. The data used in the form of opinions about Go- Pay reviews from social media Twitter, amounting to 1210. The results of labeling with Lexicon Based amounted to 923 for positive and 287 for negative. While the classification of the SVM method using the Linear kernel produces 89.17% and 84.38% for the Polynomial kernel.

scholarly journals The Classification of Public Complaints through Smart City Application

2020 ◽  
Author(s):  
Evaristus Didik Madyatmadja ◽  
Cristofer Wijaya
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Classification Algorithm ◽  
Support Vector ◽  
Search Process ◽  
Linear Kernel ◽  
K Nearest Neighbor ◽  
Mining Methods ◽  
Algorithm Search

Abstract This research aimed to classify the data of public complaints of people in Tangerang City by applying a pattern of the complaint data from the LAKSA application that has been categorized. In finding the pattern, it used one of the data mining methods, namely classification. The classification algorithm search process was performed by comparing the accuracy of several selected algorithms. The algorithms were k-nearest neighbor, random forest, support vector machine, and AdaBoost. These algorithms were tested to achieve maximum potential. Thus, the results showed support vector machine with linear kernel is a classification algorithm with the highest accuracy that reached 89.2%

Comparative Study on Classification of Remote Sensing Image by Support Vector Machine

2013 ◽  
Vol 773 ◽  
pp. 893-898 ◽  
Author(s):  
Yong Xu ◽  
Chang Chun Cheng ◽  
Xiao Ming Wang ◽  
Meng Qi Mao ◽  
Chang Jing Zhang
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
Remote Sensing Image ◽  
Support Vector ◽  
Svm Classification ◽  
Tm Image ◽  
Svm Algorithm ◽  
Traditional Algorithm

In this paper, the TM image of Landsat-5 was used for classification by the method of support vector machine (SVM). The results and precisions of classification were compared between the different parameter combinations. Further more, precisions are compared between the SVM and traditional algorithm. The results indicate that SVM classification algorithm has the advantage of broad parameters range, without prior knowledge of image and samples. The precision of SVM algorithm is much higher than traditional algorithm, especially adapt to the area without in situ measurement.

scholarly journals Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth

2019 ◽  
Vol 5 (2) ◽  
pp. 90-99
Author(s):  
Putroue Keumala Intan
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Kernel Functions ◽  
Support Vector ◽  
Medical Team ◽  
Classification Methods ◽  
Higher Dimension ◽  
Linear Kernel ◽  
Good Decision

The maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth process. One of the classification methods that can be used is the Support Vector Machine (SVM) method which is able to determine a hyperplane that will form a good decision boundary so that it is able to classify data appropriately. In SVM, there is a kernel function that is useful for solving non-linear classification cases by transforming data to a higher dimension. In this study, four kernel functions will be used; Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid in the classification process of childbirth in order to determine the kernel function that is capable of producing the highest accuracy value. Based on research that has been done, it is obtained that the accuracy value generated by SVM with linear kernel functions is higher than the other kernel functions.

scholarly journals Auto-classification of biomass through characterization of their pyrolysis behaviors using thermogravimetric analysis with support vector machine algorithm: case study for tobacco

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chao Yin ◽  
Xiaohua Deng ◽  
Zhiqiang Yu ◽  
Zechun Liu ◽  
Hongxiang Zhong ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Thermogravimetric Analysis ◽  
Near Infrared ◽  
Information Source ◽  
Support Vector ◽  
Fast Method ◽  
Svm Algorithm

Abstract Background During the biomass-to-bio-oil conversion process, many studies focus on studying the association between biomass and bio-products using near-infrared spectra (NIR) and chemical analysis methods. However, the characterization of biomass pyrolysis behaviors using thermogravimetric analysis (TGA) with support vector machine (SVM) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar, and gases) released by tobacco pyrolysis reactions decides the sensory quality, which is similar to biomass as a renewable resource through the pyrolysis process. Results SVM algorithm has been employed to automatically classify the planting area and growing position of tobacco leaves using thermogravimetric analysis data as the information source for the first time. Eighty-eight single-grade tobacco samples belonging to four grades and eight categories were split into the training, validation, and blind testing sets. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower, and middle positions. The error only occurs in the classification of subgrades of the middle position. Conclusions From the case study of tobacco, our results validated the feasibility of using TGA with SVM algorithm as an objective and fast method for auto-classification of tobacco planting area and growing position. In view of the high similarity between tobacco and other biomasses in the compositions and pyrolysis behaviors, this new protocol, which couples the TGA data with SVM algorithm, can potentially be extrapolated to the auto-classification of other biomass types.

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