Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Boldson Herdianto Situmorang, Andi Chairunnas

Judul : Sentiment Analysis of User Preferences on Learning Management System (LMS) Platform Data

Abstrak :
The Covid-19 has resulted in universities shut all across the world. Globally, over 100 million college students are out of classroom. As a result, education has changed dramatically, with the distinctive rise of Learning Management System (LMS), whereby teaching is undertaken remotely and on digital platforms. As online learning has gone mainstream, it has never been more important to choose an educational LMS tailored to institution’s mission and goals. Sentiment analysis tells user whether the information about the product is satisfactory or not before they choose it. Sentiment analysis can classify the polarity of the text in sentences or documents to see whether the opinion on the sentences or documents are positive or negative. This research focuses mainly on sentiment analysis of user preferences on Google Classroom data using Improved K-Nearest Neighbor method which is helpful to classify opinions in text into positive or negative categories. From the results obtained, testing with different input values of k results in different accuracy percentage values, for k = 2 of 74.00%, for k = 5 of 79.00%, for k = 10 of 83.00%, for k = 15 of 83.00%, for k = 20 of 84.00%, with the highest accuracy with a value of k = 20. Based on the test results, it can be taken conclusion that the k value has an effect on the accuracy of the classification system.

Tahun : 2020 Media Publikasi : Prosiding

Kategori : Jurnal No/Vol/Tahun : 1 / 1 / 2020

ISSN/ISBN : 2169-8767/ 978-1-7923-6123-4

PTN/S : Universitas Pakuan Program Studi : ILMU KOMPUTER

Bibliography :
PÄƒvÄƒloaia, V-D., Teodor, E-M., Fotache, D., and DanileÅ£, M., Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences, Sustainability 2019, 11, 4459; doi:10.3390/su11164459, pp. 1-21, 2019.

Arora, T., Dhawan, S., and Singh, K., Sentiment Analysis of Online Movie’s Reviews Using Improved k-Nearest Neighbor Classifier, Advances in Computer Science and Information Technology (ACSIT), vol. 3, issue 4, pp. 242-245, 2016.

Sebastiani, F., Machine Learning in Automated Text Categorization, Consiglio Nazionale delle Ricerche, 2002.

Muktafin, E. H., Pramono, and Kusrini, Sentiment analysis of customer satisfaction in public services using Knearest neighbors algorithm and natural language processing approach, TELKOMNIKA Telecommunication, Computing, Electronics and Control, vol. 19, no. 1, pp. 146-154, 2020.

Baesa, E. M., Bigueras, R. T., Cruz, J. D., Maligat Jr, D. E., and Torio, J. O., Sentiment Analysis of LMS Users Using Support Vector Algorithm, Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, Hong Kong, 15-17 June, 2019 , pp. 288-294.

Prananda, A. R., and Thalib, I., Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion, Journal of Information Systems Engineering and Business Intelligence, vol. 6, no. 1, pp. 1-8, 2020.

Riadi, I., Sunardi, S., and Widiandana, P., Mobile Forensics for Cyberbullying Detection using Term FrequencyInverse Document Frequency (TF-IDF), Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol.5, pp. 68- 76, 2019.

Qaiser, S., and Ali, R., Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents, International Journal of Computer Applications, vol. 181, no. 1, pp. 25-29, 2018.

Joseph, F., and Ramakrishnan, N., Text Categorization Using Improved K Nearest Neighbor Algorithm, International Journal for Trends in Engineering & Technology, vol. 4, issue 2, pp. 61-64, 2015.

Li, B., Yu, S., and Lu, Q., An Improved k-Nearest Neighbor Algorithm for Text Categorization, Proceedings of the 20th International Conference of Computer Processing of Oriental Language, Shenyang, China, June 16, 2003.

Kechaou, Z., Ammar, M. B., and Alimi, A. M., Improving e-learning with sentiment analysis of users’ opinions, Proceedings of IEEE Global Engineering Education Conference (EDUCON), pp. 10321038, Amman, Jordan, April 4-6, 2010.

Santra, A. K., and Christy, C. J., Genetic Algorithm and Confusion Matrix for Document Clustering, IJCSI International Journal of Computer Science Issues, vol. 9, issue 1, pp. 322-328, 2012.

URL : http://www.ieomsociety.org/harare2020/papers/428.pdf

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Detail Karya Ilmiah Dosen

Boldson Herdianto Situmorang, Andi Chairunnas

Judul	:	Sentiment Analysis of User Preferences on Learning Management System (LMS) Platform Data
Abstrak	:	The Covid-19 has resulted in universities shut all across the world. Globally, over 100 million college students are out of classroom. As a result, education has changed dramatically, with the distinctive rise of Learning Management System (LMS), whereby teaching is undertaken remotely and on digital platforms. As online learning has gone mainstream, it has never been more important to choose an educational LMS tailored to institution’s mission and goals. Sentiment analysis tells user whether the information about the product is satisfactory or not before they choose it. Sentiment analysis can classify the polarity of the text in sentences or documents to see whether the opinion on the sentences or documents are positive or negative. This research focuses mainly on sentiment analysis of user preferences on Google Classroom data using Improved K-Nearest Neighbor method which is helpful to classify opinions in text into positive or negative categories. From the results obtained, testing with different input values of k results in different accuracy percentage values, for k = 2 of 74.00%, for k = 5 of 79.00%, for k = 10 of 83.00%, for k = 15 of 83.00%, for k = 20 of 84.00%, with the highest accuracy with a value of k = 20. Based on the test results, it can be taken conclusion that the k value has an effect on the accuracy of the classification system.
Tahun	:	2020	Media Publikasi	:	Prosiding
Kategori	:	Jurnal	No/Vol/Tahun	:	1 / 1 / 2020
ISSN/ISBN	:	2169-8767/ 978-1-7923-6123-4
PTN/S	:	Universitas Pakuan	Program Studi	:	ILMU KOMPUTER
Bibliography	:	PÄƒvÄƒloaia, V-D., Teodor, E-M., Fotache, D., and DanileÅ£, M., Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences, Sustainability 2019, 11, 4459; doi:10.3390/su11164459, pp. 1-21, 2019. Arora, T., Dhawan, S., and Singh, K., Sentiment Analysis of Online Movie’s Reviews Using Improved k-Nearest Neighbor Classifier, Advances in Computer Science and Information Technology (ACSIT), vol. 3, issue 4, pp. 242-245, 2016. Sebastiani, F., Machine Learning in Automated Text Categorization, Consiglio Nazionale delle Ricerche, 2002. Muktafin, E. H., Pramono, and Kusrini, Sentiment analysis of customer satisfaction in public services using Knearest neighbors algorithm and natural language processing approach, TELKOMNIKA Telecommunication, Computing, Electronics and Control, vol. 19, no. 1, pp. 146-154, 2020. Baesa, E. M., Bigueras, R. T., Cruz, J. D., Maligat Jr, D. E., and Torio, J. O., Sentiment Analysis of LMS Users Using Support Vector Algorithm, Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, Hong Kong, 15-17 June, 2019 , pp. 288-294. Prananda, A. R., and Thalib, I., Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion, Journal of Information Systems Engineering and Business Intelligence, vol. 6, no. 1, pp. 1-8, 2020. Riadi, I., Sunardi, S., and Widiandana, P., Mobile Forensics for Cyberbullying Detection using Term FrequencyInverse Document Frequency (TF-IDF), Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol.5, pp. 68- 76, 2019. Qaiser, S., and Ali, R., Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents, International Journal of Computer Applications, vol. 181, no. 1, pp. 25-29, 2018. Joseph, F., and Ramakrishnan, N., Text Categorization Using Improved K Nearest Neighbor Algorithm, International Journal for Trends in Engineering & Technology, vol. 4, issue 2, pp. 61-64, 2015. Li, B., Yu, S., and Lu, Q., An Improved k-Nearest Neighbor Algorithm for Text Categorization, Proceedings of the 20th International Conference of Computer Processing of Oriental Language, Shenyang, China, June 16, 2003. Kechaou, Z., Ammar, M. B., and Alimi, A. M., Improving e-learning with sentiment analysis of users’ opinions, Proceedings of IEEE Global Engineering Education Conference (EDUCON), pp. 10321038, Amman, Jordan, April 4-6, 2010. Santra, A. K., and Christy, C. J., Genetic Algorithm and Confusion Matrix for Document Clustering, IJCSI International Journal of Computer Science Issues, vol. 9, issue 1, pp. 322-328, 2012.
URL	:	http://www.ieomsociety.org/harare2020/papers/428.pdf