Pembuatan Aplikasi Complaint Management System pada Universitas Kristen Petra dengan menggunakan Metode Support Vector Machine Multiclass One vs Rest

Isak Imanuel Leong(1*), Rolly Intan(2), Leo Willyanto Santoso(3),


(1) Program Studi Informatika
(2) Program Studi Informatika
(3) Program Studi Informatika
(*) Corresponding Author

Abstract


One of the facilities at Petra Christian University for the accommodation of aspirations from the academic community is the suggestion box. However, the use of suggestion boxes is considered less effective and objective because it has a reluctance to fill out and lacks strong evidence through photo documentation.

To answer the above problem, an Android-based complaint management system was created that supports the aspiration process that is more effective and objective through the use of mobile devices that are more widely used by the academic community. Application supported by the model. Supported by Vector Support Engine (SVM) with the approach of multiclass One vs. Rest to classify the bureau or related units to overcome errors in determining the recipient of the aspirational recipient.

The results of the research conducted show that preprocessing parameter such as normalization, stemming and stopwords removal affect the accuracy of the model. The best kernel type in SVM for aspiration text classification is linear with value of C = 1 which results in an accuracy of 95,441%. In addition, the results of a survey to administrator who manage the management of aspiration shows that the application created already answer the needs and problems, in this case supporting the objectivity and effectiveness of aspiration data delivery.


Keywords


Complaint Management System; Support Vector Machine; Android Application; Text Classification; Feature Extraction; TF-IDF

Full Text:

PDF

References


Bam, S. B., & Shahi, T. B. 2014. Named Entity Recognition for Nepali Text Using Support Vector Machines. Intelligent Information Management, 06, 21-29.

Evantio, Y. B., Rokhmawati, R. I., & Saputra, C. M. 2017. Pengembangan Sistem Informasi E-Complaint Management (Studi Kasus: Batching Plant Produksi Beton P.T. Holcim Indonesia Regional Jawa Timur). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(7), 2801-2810.

Fabris, F., Magalhaes, J. P., & Freitas, A. A. 2017. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188. DOI= https://doi.org/10.1007/s10522-017-9683-y.

Fatima, S., & Srinivasu, B. 2017. Text Document categorization using support vector machine. International Research Journal of Engineering and Technology, 4(2), 141-147.

Goswami, A. K., Joshi, H., & Mishra, S. P. 2016. Neural Network Approach for Automatic Landuse Classification of Satellite Images: One-Against-Rest and Multi-Class Classifiers. International Journal of Computer Applications, 134(11), 35-42. DOI=https://doi.org/10.5120/ijca2016908148.

Gupta, V., & Lehal, G. 2009. A Survey of Text Mining Techniques and Applications. Journal of Emerging Technologies in Web Intelligence, 60-76.

Jumeilah, F. S. 2017. Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian. Jurnal Rekayasa Sistem dan Teknologi Informasi, 1(1), 19-25. DOI=https://doi.org/10.29207/resti.v1i1.11.

Liao, S., Chu, P., & Hsiao, P. 2012. Data mining techniques and applications - A decade review from 2000 to 2011. Expert Syst. Appl., 39, 11303-11311. DOI=https://doi.org/10.1016/j.eswa.2012.02.063.

Mandal, A. K., & Sen, R. 2014. Supervised learning Methods for Bangla Web Document Categorization. International Journal of Artificial Intelligence & Applications (IJAIA), 5(5), 93-105.

Mehra, N., & Gupta, S. 2013. Survey on Multiclass Classification Methods. International Journal of Computer Science and Information Technologies (IJCSIT), 4(4), 572-576.

Prajitno, I. S., Megawati, C., Dhini, A., & Hardaya, I. S. 2016. Application of text mining for classification of textual reports: A study of Indonesia's national complaint handling system. 6th International Conference on Industrial Engineering and Operations Management in Kuala Lumpur, IEOM 2016 (pp. 1147-1156). Kuala Lumpur: IEOM Society.

Pratama, T., & Purwarianti, A. 2017. Topic classification and clustering on Indonesian complaint tweets for bandung government using supervised and unsupervised learning. 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA), 1-6. DOI=https://doi.org/10.1109/ICAICTA.2017.8090981.

Salloum, S. A., Alhamad, A. Q., Al-Emran, M., & Shaalan, K. 2018. A Survey of Arabic Text Mining. In K. Shaalan, A. E. Hassanien, & F. Tolba (Eds.), Intelligent Natural Language Processing: Trends and Applications (pp. 417-431). Springer International Publishing AG.

Text Preprocessing dengan Python NLTK. 2019. Anggri Yulio P. Retrieved April 17, 2020, from https://devtrik.com/python/text-preprocessing-dengan-python-nltk/

What is a Complaint Management System. 2019. Andra Picincu. Retrieved April 17, 2020, from https://bizfluent.com/facts-6401656-complaint-management-system-.html


Refbacks

  • There are currently no refbacks.


Jurnal telah terindeks oleh :