Lilyani Asri Utami, Lilyani Asri
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Published : 4 Documents
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ANALISIS SENTIMEN OPINI PUBLIK BERITA KEBAKARAN HUTAN MELALUI KOMPARASI ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR BERBASIS PARTICLE SWARM OPTIMIZATION Utami, Lilyani Asri
Jurnal Pilar Nusa Mandiri Vol 13 No 1 (2017): PILAR Periode Maret 2017
Publisher : PPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1420.177 KB) | DOI: 10.33480/pilar.v13i1.153

Abstract

Sentiment analysis is a process to determine the content of text-based datasets which are positive or negative. At present, public opinion be an important resource in the decision of a person in finding a solution. Classification algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) is proposed by many researchers to be used in sentiment analysis for review opinion. The problem in this research is the selection of feature selection to improve accuracy values Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) and compare the highest accuracy for sentiment analysis review public opinion about the news of forest fires. The comparison algorithms, SVM produces an accuracy of 80.83% and AUC 0.947, then compared with SVM based on PSO with an accuracy of 87.11% and AUC 0.922. The test result data for K-NN algorithm accuracy was 85.00% and the AUC 0.918, then compared for accuracy by k-NN-based PSO amounted to 73.06% and the AUC 0.500. The results of the testing of the PSO algorithm can improve the accuracy of SVM, but are not able to improve the accuracy of the algorithm K-NN. SVM algorithm based on PSO proven to provide solutions to the problems of classification review news opinion forest fires in order to more accurately and optimally.
Property Sales Data Processing Information System (SiPendar) Suparni, Suparni; Utami, Lilyani Asri; Selviana, Elsa Dwi
SinkrOn Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (523.422 KB) | DOI: 10.33395/sinkron.v3i2.10084

Abstract

PT. Pratama Mega Konstruksindo is one of the companies engaged in Property, especially Housing. One of the fields that requires technological progress is one of them is the property sector, the rapid development in the property sector is currently urging property service companies to meet the demands of the wider community. Implementation of work related to housing sales. In managing the data, this company still uses a manual system, starting from the recording and calculation aspects so that its performance has not been effective. At PT Pratama Mega Konstruksindo this still manages data using Ms Excel. As well as down payment, cash payments and consumer data are recorded using Ms Excel. This can cause errors in recording transactions, data security that is not guaranteed confidentiality, ineffective employees at work because it requires more time to input and make sales reports and even loss of data. Therefore, PT. Pratama Mega Konstruksindo requires a system that can solve the problem. This data processing system is designed web-based using the PHP and MySql programming languages as data storage databases. With the existence of this website, it can help processing sales data more effectively and efficiently, reports can be printed in realtime and data security can be maintained
ANALISIS SENTIMEN OPINI PUBLIK BERITA KEBAKARAN HUTAN MELALUI KOMPARASI ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR BERBASIS PARTICLE SWARM OPTIMIZATION Utami, Lilyani Asri
Jurnal Pilar Nusa Mandiri Vol 13, No 1 (2017): PILAR Periode Maret
Publisher : STMIK Nusa Mandiri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v13i1.344

Abstract

Sentiment analysis is a process to determine the content of text-based datasets which are positive or negative. At present, public opinion be an important resource in the decision of a person in finding a solution. Classification algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) is proposed by many researchers to be used in sentiment analysis for review opinion. The problem in this research is the selection of feature selection to improve accuracy values Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) and compare the highest accuracy for sentiment analysis review public opinion about the news of forest fires. The comparison algorithms, SVM produces an accuracy of 80.83% and AUC 0.947, then compared with SVM based on PSO with an accuracy of 87.11% and AUC 0.922. The test result data for K-NN algorithm accuracy was 85.00% and the AUC 0.918, then compared for accuracy by k-NN-based PSO amounted to 73.06% and the AUC 0.500. The results of the testing of the PSO algorithm can improve the accuracy of SVM, but are not able to improve the accuracy of the algorithm K-NN. SVM algorithm based on PSO proven to provide solutions to the problems of classification review news opinion forest fires in order to more accurately and optimally.