Determining the Best Answers for Balinese Language Problems using Latent Semantic Analysis

Authors

  • Made Agus Putra Subali Institut Teknologi dan Bisnis STIKOM Bali
  • I Ketut Putu Suniantara Institut Teknologi dan Bisnis STIKOM Bali

DOI:

https://doi.org/10.31294/paradigma.v24i2.1437

Keywords:

automated essay scoring, latent semantic analysis, balinese language

Abstract

In Balinese, descriptions or essays are formed in an interrogative format using question words such as “akuda”, “apa”, “dija”, “kenken”, “kuda”, dan “nyen”. The assessment process on description questions tends to be more difficult and complex than multiple choice questions, this is because the description questions are described in sentence form. The solution to facilitate the assessment process on description questions can be done using automated essay scoring. Based on the results of previous studies, the Latent Semantic Analysis (LSA) method provides a better level of accuracy, because the LSA method uses the Singular Value Decomposition (SVD) method to obtain a new pattern of relationships between terms and reference terms. The data used in this study are five questions and their answer keys and there are five candidate answers for each question in Balinese. Based on the tests that have been carried out, the method used obtained an overall average accuracy of 70.26%, this shows that the LSA method can be used well in the assessment process or automatic essay assessment.

References

Chen, H., Xu, J., & He, B. (2014). Automated Essay Scoring by Capturing Relative Writing Quality. The Computer Journal, 57(9), 1318–1330.

Citawan, R. S., Mawardi, V. C., & Mulyawan, B. (2017). Automatic Essay Scoring in E-learning System Using LSA Method with N-Gram Feature for Bahasa Indonesia. International Conference on Electrical Systems, Technology and Information (ICESTI).

Contreras, J. O., Hilles, S., & Abubakar, Z. B. (2018). Automated Essay Scoring with Ontology based on Text Mining and NLTK Tools. International Conference on Smart Computing and Electronic Enterprise (ICSCEE).

Fauzi, M. A., Arifin, A. Z., & Yuniarti, A. (2014). Term Weighting Berbasis Indeks Buku dan Kelas untuk Perangkingan Dokumen Berbahasa Arab. Lontar Komputer, 5(2), 435–442.

Fauzi, M. A., Utomo, D. C., Setiawan, B. D., & Pramukantoro, E. S. (2017). Automatic Essay Scoring System Using N-Gram and Cosine Similarity for Gamification Based E-Learning. International Conference on Advances in Image Processing (ICAIP).

Granoka, I. W. O., Naryana, I. B. U., Jendera, I. W., Bawa, I. W., Medera, I. N., Putrayasa, I. G. N., Anom, I. G. K., Tama, I. W., Denes, I. M., Purwa, I. M., Sukayana, I. N., & Indra, I. B. K. M. (1996). Tata Bahasa Baku Bahasa Bali. Balai Penelitian Bahasa Pusat Pembinaan dan Pengembangan Bahasa Departemen Pendidikan dan Kebudayaan.

McNamara, D. S., Crossley, S. A., Roscoe, R. D., Allen, L. K., & Dai, J. (2015). A hierarchical classification approach to automated essay scoring. Assessing Writing, 23, 35–59.

Putra, I. B. G. W., Sudarma, M., & Kumara, I. N. S. (2016). Klasifikasi Teks Bahasa Bali dengan Metode Supervised Learning Naive Bayes Classifier. Teknologi Elektro, 15(2), 81–86.

Subali, M. A. P., & Fatichah, C. (2019). Kombinasi Metode Rule-Based dan N-Gram Stemming untuk Mengenali Stemmer Bahasa Bali. Jurnal Teknologi Informasi Dan Ilmu Komputer (JTIIK), 6(2).

Subali, M. A. P., & Wijaya, P. (2021). Sistem Question Answering untuk Bahasa Bali menggunakan Metode Rule-Based dan String Similarity. Techno.COM, 20(2), 300–308.

Downloads

Published

2022-09-20

How to Cite

Subali, M. A. P., & Suniantara, I. K. P. (2022). Determining the Best Answers for Balinese Language Problems using Latent Semantic Analysis. Paradigma, 24(2), 175-181. https://doi.org/10.31294/paradigma.v24i2.1437