PENERAPAN SENTIMENT ANALYSIS PADA HASIL EVALUASI DOSEN DENGAN METODE SUPPORT VECTOR MACHINE

Valonia Inge Santoso, Gloria Virginia, Yuan Lukito

Abstract


The quality of lectures can be determined by some feedbacks from students. From the feedbacks, we can give appreciations for those lectures who get good feedback from students, and evaluations for those who get bad feedback. The problem is classifying large size of feedbacks manually isn t effective and took a lot of time. Therefore, we need a system that can classify feedbacks automatically. These feedbacks will be classified into positive, negative, and neutral, usually called as sentiment analysis. Sentiment analysis implementation can be done by several methods, one of them that has a good accuracy is Support Vector Machine (SVM). SVM performance in this research is measured with the level of accuracy. The number of accuracy indicate the success level of system. The conclusion of this research is factors that affects the accuracy. The factors are the range of each classes and number of unique words in the training document.

Keywords


sentiment analysis, support vector machine, inverse matrix

References


Croft, W. B., Metzler, D., & Strohman, T. (2015). Search Engines Information Retrieval in Practice. Pearson Education, Inc.

Feizar, F. H., Indrianti, & Yudistira, N. (2014). Analisis Sentimen Opini Film Berbahasa Indonesia Berbasis Kamus Menggunakan Metode Neighbor-Weighted K-Nearest Neighbor. Universitas Brawijaya, Teknik Informatika. Malang: Universitas Brawijaya.

Manning, C. D., Raghavan, P., & Schutze, H. (2009). An Introduction to Information Retrieval. Cambridge, England: Cambridge University Press.

Novantirani, A., Sabariah, M. K., & Effendy, V. (n.d). Analisis Sentimen pada Twitter untuk Mengenai Penggunaan Transportasi Umum Darat Dalam Kota dengan Metode Support Vector Machine. Universitas Telkom, Teknik Informatika. Bandung: Universitas Telkom.

Putranti, N. D., & Winarko, E. (2014, January 15). Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine. IJCCS, 8, 91-100.

Saraswati, N. W. (2011). Text Mining dengan Metode Naive Bayes Classifier dan Support Vector Machines untuk Sentiment Analysis. Universitas UDAYANA, Teknik Elektro. Denpasar: Universitas UDAYANA.

Wicaksono, A. F. (2011, Januari 20). My Live Journals. Dipetik September 15, 2015, dari Apa itu Sentiment Analysis / Opinion Mining?: http://alfan- farizki.blogspot.co.id/2011/01/apa-itu-sentiment-analysis-opinion.html




DOI: http://dx.doi.org/10.26623/transformatika.v14i2.439

Refbacks

  • There are currently no refbacks.


| View My Stats |

Jurnal Transformatika : Journal Information Technology  by  Department of Information Technology, Faculty of Information Technology and Communication, Semarang University  is licensed under a  Creative Commons Attribution 4.0 International License.