Web Scraping and Naïve Bayes Classification for Job Search Engine

Slamet, Cepy; Andrian, Rico; Maylawati, Dian Sa’adillah; Suhendar,; Darmalaksana, Wahyudin; Ramdhani, Muhammad Ali

doi:10.1088/1757-899x/288/1/012038

Cited by 63 publications

(33 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Previous studies on algorithms of classification have proven that Naïve Bayes is one of the best algorithms in comparison with the other ones such of Decision Tree, Naïve Bayes, and K-NN. It has also been found that accuracy and speed are the most supporting and helpful features of the algorithm in classifying data [12]. The advantage of Bayesian classifier is that it requires small training data set for classification.…”

Section: Naïve Bayes Classification Approachmentioning

confidence: 99%

Classification of Twitter Data Belonging to Sudanese Revolution Using Text Mining Techniques

Abdelhameed¹,

Muñoz-Hernández²

2019

International Journal of New Technology and Research

View full text Add to dashboard Cite

Twitter has attracted a great deal of attention recently. It is one of the most common social networking sites for chats, sharing ideas, and transfer of information and news through text. This paper focused on sentiment classification of twitter data belonging to Sudanese revolution which written using either Modern Standard Arabic or Sudanese dialectical Arabic. Twitter's API was used to collect tweets related to Sudanese revolution. The dataset consists of 6482 tweets with a good balance of positive and negative sentiments. Three different classifiers were used on the dataset namely; Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT) to classify the tweets based on its polarity into positive or negative. We evaluated our work by four different measures which are Precision, Recall, Accuracy and F-measure. We made a comparison between the three classifiers based on those measures. The results show that, SVM achieved the best Accuracy and F-measure and it equals 75.2%, 83.9% respectively. While NB achieved best Precision and it equals 75.2%. Also, DT achieved best Recall and it equals 99.9%. In addition, the percentages of positive and negative opinions toward the government was calculated. 9.4% represents the percentage of positive opinions related the government, while 90.6% represents the percentage of negative opinions related the same government.

show abstract

Section: Naïve Bayes Classification Approachmentioning

confidence: 99%

Classification of Twitter Data Belonging to Sudanese Revolution Using Text Mining Techniques

Abdelhameed¹,

Muñoz-Hernández²

2019

International Journal of New Technology and Research

View full text Add to dashboard Cite

show abstract

“…Expert systems are systems that process data in an organized manner [8], which seeks to adapt human knowledge to a computer, so that the computer can solve problems as is usually done by experts [9]. Based on many research results, the expert system has a very good ability in decision making, the system has advantages in terms of: good data accessibility, efficient in time [10], accurate [11], support decisions appropriately [12], more economical [13], broad accessibility [14], improving user understanding [15], improving productivity [16], presenting data and information well [17], and in certain cases can be utilized as data storage media [18].…”

Section: System Designmentioning

confidence: 99%

Comparison of principal component analysis algorithm and local binary pattern for feature extraction on face recognition system

et al. 2018

View full text Add to dashboard Cite

Characteristic extraction in face recognition is a step to get characteristic information from the image. The characteristic extraction algorithm is tested against several scenarios of different sunlight and lights, objects facing the camera and not facing the camera. The sample test data were performed on 4 people using a video file or frame numbering 70 for recognizable faces using Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms. The result of the research shows that Local Binary Pattern (LBP) algorithm in object scenario facing camera with sunlighting in room has accuracy of 98.59%, recognition time of 812,817 milliseconds, FAR of 1,41% and FRR of 0%, while at Principal Component Analysis (PCA) 98.59% accuracy, recognition time of 1275,761 milliseconds, FAR of 1.41% and FRR of 0%. Based on these results, the Local Binary Pattern (LBP) algorithm is more efficient than Principal Component Analysis (PCA) for face recognition of the scenarios to be implemented in real-time video.

show abstract

“…DSS is the part of information system for supporting decision for decision making. Refers to several research, an information system has a high capability in decision making, the system has an accurate data accessibility and efficient run-time [10], high accuracy [11], and to support a proper decision [12], low cost [13], extended accessibility [14], intensify user knowledge, increase productivity [15], provide a better data and information [16], and in the certain cases are potentially used as data storage [17].…”

Section: Introductionmentioning

confidence: 99%

Design of prototype decision support system for flood detection based on ultrasonic sensor

Septiana

2018

MATEC Web Conf.

View full text Add to dashboard Cite

The system of awareness of the overflow of river water that causes flooding cannot work automatically and in real time in providing a warning about the elevation of river surface that potentially flood. It's caused residents who live around the river area do not know the situation when the river overflow. There are several alternatives to detect river overflow, one of them is by using microcontroller ultrasonic sensor based. The research aims to design prototype decision support system based on the ultrasonic sensor for flood detection that works automatically detects the river overflow. The software development model used is Prototype Model. The result of this research is a prototype in the form of microcontroller ultrasonic sensor based which can be used as flood detection decision support system that will know the height of river surface designed to detect certain level about flood potency with a low-level error.

show abstract

Web Scraping and Naïve Bayes Classification for Job Search Engine

Cited by 63 publications

References 6 publications

Classification of Twitter Data Belonging to Sudanese Revolution Using Text Mining Techniques

Classification of Twitter Data Belonging to Sudanese Revolution Using Text Mining Techniques

Comparison of principal component analysis algorithm and local binary pattern for feature extraction on face recognition system

Design of prototype decision support system for flood detection based on ultrasonic sensor

Contact Info

Product

Resources

About