MANHATTAN DISTANCE AND DICE SIMILARITY EVALUATION ON INDONESIAN ESSAY EXAMINATION SYSTEM

Muhammad Haidar Ali
Faisal Rahutomo


DOI: https://doi.org/10.29100/jipi.v4i2.1398

Abstract


Each learning process requires an evaluation tool to measure the level of understanding of students. The type of evaluation can be multiple choice questions, short entries and essays. Some studies reveal essay exams better than other types of evaluations. An essay assessment is automatically needed to save teacher time in correcting answers. However, the development of essay assessments is still ongoing. The aim is to obtain a better accuracy value than the method used in the assessment. Based on these problems, this study proposes a comparative analysis of similarity methods for online essay exam assessment. The similarity method compared is Similarity Dice and Manhattan Distance. Both methods produce coefficient values which are then compared to the assessment of the system with manual scales with the same scale. The data used were 2162 data. This data was obtained from 50 students who answered 40 questions (politics, sports, lifestyle and technology). The data obtained in this study can be used to support other research that can be accessed at www.indonesian-ir.org. This research shows that the Dice similarity scheme is more accurate than Manhattan Distance

Full Text:

PDF

Article Metrics :

References


N. Suzen, A. N. Gorban, J. Levesley, and E. M. Mirkes, “Automatic Short Answer Grading and Feedback,” pp. 1–20.

T. A. Roshinta and R. Faisal, “Analisis Aspek-Aspek Ujian Esai Daring Berbahasa Indonesia,” vol. 01, pp. 1–26, 2016.

T. Dalgleish et al., Text Mining Application and Theory, vol. 136, no. 1. 2007.

T. R. Muzzammil, R. V. H. Ginardi, and D. Purwitasari, “Modul Klasifikasi Aduan dengan Pendekatan Kemiripan Teks pada Aplikasi Perangkat Bergerak Suara Warga (SURGA) Kota Kediri,” vol. 5, no. 1, pp. 52–57, 2016.

G. Salton, A. Wong, and C. S. Yang, “Vector Space Model for Automatic Indexing. Information Retrieval and Language Processing,” Commun. ACM, vol. 18, no. 11, pp. 613–620, 1975.

F. Rahutomo, P. Y. Saputra, C. Febriawan, and P. Putra, “Implementasi Explicit Semantic Analysis Berbahasa Indonesia Menggunakan Corpus Wik-ipedia Indonesia,” J. Inform. Polinema, vol. 4, no. 4, pp. 252–257, 2018.

F. Amin and E. Winarno, “Rancang Bangun Sistem Temu Kembali Informasi ( Information Retrieval System ) Dokumen Berbahasa Jawa menggunakan Metode DICE Similarity,” vol. 21, no. 2, pp. 99–106, 2016.

F. Rahutomo, Z. Hanif, R. Adi, and I. F. Rozi, “Implementasi Text Mining Pada Laman Blog di,” pp. 101–109, 2018.

V. M.K and K. K, “A Survey on Similarity Measures in Text Mining,” Mach. Learn. Appl. An Int. J., vol. 3, no. 1, pp. 19–28, 2016.

R. Feldman and J. Sanger, The Text Mining handbook.

D. Alikaniotis, H. Yannakoudakis, and M. Rei, “Automatic Text Scoring Using Neural Networks,” pp. 715–725, 2016.

M. Shoaib, A. Daud, M. Sikandar, and H. Khiyal, “An Improved Similarity Measure for Text Documents,” J. Basic. Appl. Sci. Res, vol. 4, no. 6, pp. 215–223, 2014.

C. C. Aggrawal and C. Zai, Mining Text Data.

W. H.Gomaa and A. A. Fahmy, “A Survey of Text Similarity Approaches,” Int. J. Comput. Appl., vol. 68, no. 13, pp. 13–18, 2013.

K. J. Cios, W. Pedrycz, R. W. Swiniarski, and L. A. Kurgan, “Data mining: A knowledge discovery approach,” Data Min. A Knowl. Discov. Ap-proach, no. September 2017, pp. 1–606, 2007.

M. Astiningrum et al., “Implementasi nlp dengan konversi kata pada sistem chatbot konsultasi laktasi,” vol. 5, no. November, pp. 46–52, 2018.

F. Rahutomo and A. Hafidh Ayatullah, “Indonesian Dataset Expansion of Microsoft Research Video Description Corpus and Its Similarity Analysis,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 3, no. 4, p. 319, 2018.