Question Answering using Ontology for Sumedang Larang History with Support Vector Machine Based on Telegram Bot

Authors

DOI:

https://doi.org/10.30865/mib.v6i4.4574

Keywords:

Sumedang Larang History, Telegram Bot, Question Answering System, Ontology, Support Vector Machine

Abstract

Technological developments affect many aspects, one of which is historical education. History lessons can shape students' personalities and encourage an interest in historical knowledge. There are many stories from Indonesian history, one of which is the Sumedang Larang Kingdom. The Sumedang Larang Kingdom is one of the Islamic kingdoms in Pasundan. However, not many people know about this kingdom. The millennial generation is technologically advanced, so they can take advantage of technological advances to quickly introduce the history of Sumedang Larang. One of them utilizes the telegram bot using the Application Programming Interface (API), which can connect the system to the telegram platform. In addition, this technology can be used as a history learning attraction using the question answering system (QA). Our research aims to build a QA system that can introduce the history of Sumedang Larang to the millennial generation. Because this system uses ontology knowledge with concepts related to the Sumedang Larang domain, it can focus on the history of Sumedang Larang. Applying the support vector machine (SVM) algorithm to process classification text can make it easier to search for text categories. The test results show the performance of the SVM method with a test size parameter of 0.5, such as 74% and 78%. The performance test results are accuracy scores in the subject category and object classification.

Author Biography

Z. K. A Baizal, Telkom University, Bandung

School of Computing

References

R. Hernandez, “Impact of ICT on Education: Challenges and Perspectives,†J. Educ. Psychol., vol. 5, no. 1, pp. 325–347, 2017.

C. Calvo-Porral and R. Pesqueira-Sanchez, “Generational differences in technology behaviour: comparing millennials and Generation X,†Kybernetes, vol. 49, no. 11, pp. 2755–2772, 2020, doi: 10.1108/K-09-2019-0598.

J. Shaturaev, “Indonesia: Superior Policies and Management for Better Education (Community development through Education),†Ðрхив Ðаучных ИÑÑледований, no. 20, pp. 1–10, 2021.

Karaton Sumedang Larang (KSL), “Selayang Pandang Keraton Sumedang Larang,†vol. 2, no. 1473, pp. 1–7, 2020.

C. Ratu, M. C. Parahiyangan, G. Haji, and A. Putih, “Selayang pandang sejarah karaton sumedang larang,†2020.

A. Jalil, T. Tohara, S. M. Shuhidan, F. Diana, S. Bahry, and M. Norazmi Bin Nordin, “Exploring Digital Literacy Strategies for Students with Special Educational Needs in the Digital Age.,†Turkish J. Comput. Math. Educ., vol. 12, no. 9, pp. 3345–3358, 2021.

A. Bansal, Z. Eberhart, L. Wu, and C. McMillan, “A Neural Question Answering System for Basic Questions about Subroutines,†Proc. - 2021 IEEE Int. Conf. Softw. Anal. Evol. Reengineering, SANER 2021, pp. 60–71, 2021, doi: 10.1109/SANER50967.2021.00015.

Y. Wen, X. Zhu, and L. Zhang, “CQACD : A Concept Question-Answering System for Intelligent Tutoring Using a Domain Ontology With Rich Semantics,†IEEE Access, vol. 10, no. June, pp. 67247–67261, 2022, doi: 10.1109/ACCESS.2022.3185400.

M. Schubotz, P. Scharpf, K. Dudhat, Y. Nagar, F. Hamborg, and B. Gipp, “Introducing MathQA: a Math-Aware question answering system,†Inf. Discov. Deliv., vol. 46, no. 4, pp. 214–224, 2018, doi: 10.1108/IDD-06-2018-0022.

M. Neves et al., “Olelo: A question answering application for biomedicine,†ACL 2017 - 55th Annu. Meet. Assoc. Comput. Linguist. Proc. Syst. Demonstr., pp. 61–66, 2017, doi: 10.18653/v1/P17-4011.

N. A. Othman, M. Z. N. Al-Dabagh, and I. Aydin, “A New Embedded Surveillance System for Reducing COVID-19 Outbreak in Elderly Based on Deep Learning and IoT,†2020 Int. Conf. Data Anal. Bus. Ind. W. Towar. a Sustain. Econ. ICDABI 2020, no. May 2021, 2020, doi: 10.1109/ICDABI51230.2020.9325651.

T. N. Manjunath, D. Yogish, S. Mahalakshmi, and H.K. Yogish, “Smart question answering system using vectorization approach and statistical scoring method,†Proc. Ninth Text Retr. Conf., pp. 655–664, 2021.

D. Rajpathak, Y. Xu, and I. Gibbs, “An integrated framework for automatic ontology learning from unstructured repair text data for effective fault detection and isolation in automotive domain,†Comput. Ind., vol. 123, p. 103338, 2020, doi: 10.1016/j.compind.2020.103338.

A. Abdi, N. Idris, and Z. Ahmad, “QAPD: An ontology-based question answering system in the physics domain,†Soft Comput., vol. 22, no. 1, pp. 213–230, 2018, doi: 10.1007/s00500-016-2328-2.

H. Jung and W. Kim, “Automated conversion from natural language query to SPARQL query,†J. Intell. Inf. Syst., vol. 55, no. 3, pp. 501–520, 2020, doi: 10.1007/s10844-019-00589-2.

H. Kahaduwa, D. Pathirana, P. L. Arachchi, V. Dias, S. Ranathunga, and U. Kohomban, “Question Answering system for the travel domain,†3rd Int. Moratuwa Eng. Res. Conf. MERCon 2017, pp. 449–454, 2017, doi: 10.1109/MERCon.2017.7980526.

S. T. Alam Monisha, S. Sarker, and M. M. Hasan Nahid, “Classification of Bengali Questions towards a Factoid Question Answering System,†1st Int. Conf. Adv. Sci. Eng. Robot. Technol. 2019, ICASERT 2019, 2019, doi: 10.1109/ICASERT.2019.8934567.

J. I. Pengetahuan and D. A. N. T. Komputer, “Algoritma Klasifikasi Naive Bayes Dan Support Vector,†vol. 5, no. 2, pp. 211–220, 2020.

S. Dey, S. Wasif, D. S. Tonmoy, S. Sultana, J. Sarkar, and M. Dey, “A Comparative Study of Support Vector Machine and Naive Bayes Classifier for Sentiment Analysis on Amazon Product Reviews,†2020 Int. Conf. Contemp. Comput. Appl. IC3A 2020, no. February, pp. 217–220, 2020, doi: 10.1109/IC3A48958.2020.233300.

L. Mohan, J. Pant, P. Suyal, and A. Kumar, “Support Vector Machine Accuracy Improvement with Classification,†Proc. - 2020 12th Int. Conf. Comput. Intell. Commun. Networks, CICN 2020, pp. 477–481, 2020, doi: 10.1109/CICN49253.2020.9242572.

P. Wang et al., “Classification of Proactive Personality: Text Mining Based on Weibo Text and Short-Answer Questions Text,†IEEE Access, vol. 8, pp. 97370–97382, 2020, doi: 10.1109/ACCESS.2020.2995905.

F. Teknik and I. T. Del, “Impact of Text Preprocessing on Named Entity Recognition Based on Conditional Random Field in Indonesian Text Samuel Indra Gunawan Situmeang,†vol. 6, no. 36, pp. 423–430, 2022.

M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing for Student Complaint Document Classification Using Sastrawi,†IOP Conf. Ser. Mater. Sci. Eng., vol. 874, no. 1, 2020, doi: 10.1088/1757-899X/874/1/012017.

J. Garner, S. Crossley, and K. Kyle, “N-gram measures and L2 writing proficiency,†System, vol. 80, pp. 176–187, 2019, doi: 10.1016/j.system.2018.12.001.

D. Ruswanti, “Pengukuran Performa Support Vector Machine Dan Neural Netwok Dalam,†Gaung Inform., vol. 13, no. 1, pp. 66–75, 2020.

D. WiÅ›niewski, J. Potoniec, A. Åawrynowicz, and C. M. Keet, “Analysis of Ontology Competency Questions and their formalizations in SPARQL-OWL,†J. Web Semant., vol. 59, no. xxxx, p. 100534, 2019, doi: 10.1016/j.websem.2019.100534.

J. Potoniec, D. WiÅ›niewski, A. Åawrynowicz, and C. M. Keet, “Dataset of ontology competency questions to SPARQL-OWL queries translations,†Data Br., vol. 29, no. January, pp. 1–13, 2020, doi: 10.1016/j.dib.2019.105098.

M. W. Hasyim, S. Pramono, and Sutrisno, “Web-Based Telegram Chatbot Management System: Create Chatbot Without Programming Language Requirements,†IOP Conf. Ser. Mater. Sci. Eng., vol. 1096, no. 1, p. 012075, 2021, doi: 10.1088/1757-899x/1096/1/012075.

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Published

2022-10-25

Issue

Vol. 6 No. 4 (2022): Oktober 2022

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Articles

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