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All Journal Bulletin of Electrical Engineering and Informatics ILKOM Jurnal Ilmiah JURNAL MASYARAKAT NEGERI ROKANIA Buletin Sistem Informasi dan Teknologi Islam Journal of Technology Research in Information System and Engineering KOPEMAS
Lutfi Budi Ilmawan
Universitas Muslim Indonesia
Computer Science & IT Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : ILKOM Jurnal Ilmiah

 1 
Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter Rifqatul Mukarramah; Dedy Atmajaya; Lutfi Budi Ilmawan
ILKOM Jurnal Ilmiah Vol 13, No 2 (2021)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i2.851.168-174

Abstract

Sentiment analysis is a technique to extract information of one’s perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify society’s response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.
MEMBANGUN WEB CRAWLER BERBASIS WEB SERVICE UNTUK DATA CRAWLING PADA WEBSITE GOOGLE PLAY STORE Lutfi Budi Ilmawan
ILKOM Jurnal Ilmiah Vol 10, No 2 (2018)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v10i2.282.215-224

Abstract

At this time, Google Play Store is not providing API that can be used for accessing datas from applications on it’s application store. With that plenty application’s data, it could be used to make it a good research object, specially on data mining field. In this research, the system that is built is the system that can retrieve that applications’ data. For multiplatform’s purpose, web services are used for being an interface between client and server. Finally, the built system is working as expected. The system can retrive data from Google Play Store and it is suitable from requirements of data analysis stage. It can also integrated with REST web service to provide multiplatform access.
Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store Lutfi Budi Ilmawan; Muhammad Aliyazid Mude
ILKOM Jurnal Ilmiah Vol 12, No 2 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i2.597.154-161

Abstract

In this research, the performance of SVM classification method will be compared with other classification methods, by using the Naïve Bayes classification method. Naïve Bayes classification method is a light classification method and has a high accuracy if applied to the text classification according to some previous studies. The accuracy of the classifier is measured using the K-fold cross validation method whose results will be tabulated in a confusion matrix table, with a value of K = 3. In this study, the data processed are textual reviews of applications in the Indonesian language Google Play Store obtained from previous research. The test results obtained from the 3-fold cross-validation method produce that SVM Classifier has a higher value of accuracy when compared with the accuracy of the Naïve Bayes classifier, the SVM classifier gets an accuracy of 81.46% and Naïve Bayes classifier by 75.41%.
Co-Authors Anak Agung Istri Sri Wiadnyani Dedy Atmajaya Dolly Indra Hadyan Mardhi Fadlillah Harlinda Lahuddin Irsal Ardah Kasman Kasman Moh Zul Fitrah Muhammad Fajrul Ramdan Satra Rifqatul Mukarramah St. Hajrah Mansyur Tasrif Hasanuddin Umar Mansyur Yulita Salim Zaenal Zaenal