Arifa Nur Hasanah
Abdurahman Baizal - [ ]
Ramanti Dharayani



In Indonesia, there were many kingdoms in the past, one of which was the Sumedang Larang Kingdom. Sumedang Larang is an Islamic kingdom under the control of the Pajajaran Kingdom. Through history, a nation will be able to recognize the origin of its own nation. Therefore, teaching about history is very important to be instilled from an early age. Through the rapid development of technology, teaching today is not only in the form of formal teaching, but also informal teaching. Nowadays, informal teaching can be done through various media, one of the media that is often used is gadgets. The utilization of gadgets as learning media allows a person to learn independently. One form of history learning by utilizing gadgets can be in the form of a Question Answering System (QAS). QAS allows users to ask questions using natural language and the system will answer the questions. Therefore, our research aims to help introduce the history of the Sumedang Larang kingdom to the public. We build a QAS by utilizing the n-gram model, Multilayer Perceptron (MLP) algorithm, and ontology. N-gram is used to cut words/sentences and convert them into a matrix, while MLP is used to classify texts, and ontology is used as knowledge representation. In this study, the system was able to answer 35 questions out of 61 questions, so it had an accuracy of 57.37%.


Multilayer perceptron; ontology; question answering system

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El Soufi, Nada, and Beng Huat See. "Does explicit teaching of critical thinking improve critical thinking skills of English language learners in higher education? A critical review of causal evidence." Studies in educational evaluation 60 (2019): 140-162.

Diefenbach, Dennis, et al. "Core techniques of question answering systems over knowledge bases: a survey." Knowledge and Information systems 55.3 (2018): 529-569.

Lidimilah, Lukman Fakih. "Question Answering Terjemah Al Qur'an Menggunakan Named Entity Recognition." Jurnal Ilmiah Informat-ika 2.2 (2017): 139-145

Kamath, Cannannore Nidhi, Syed Saqib Bukhari, and Andreas Dengel. "Comparative study between traditional machine learning and deep learning approaches for text classification." Proceedings of the ACM Symposium on Document Engineering 2018. 2018.

Sebastian, Carol, et al. "Virtual assistance using question generation Answering." 2021 International Conference on Communication infor-mation and Computing Technology (ICCICT). IEEE, 2021. 10.1109/ICCICT50803.2021.9510131

Yu, Jianfei, et al. "Modelling domain relationships for transfer learning on retrieval-based question answering systems in e-commerce." Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 2018.

Nguyen, Van-Tu, Anh-Cuong Le, and Ha-Nam Nguyen. "A Model of Convolutional Neural Network Combined with External Knowledge to Measure the Question Similarity for Community Question Answering Systems." International Journal of Machine Learning and Compu-ting 11.3 (2021): 194-201.

Wynne, Hnin Ei, and Zar Zar Wint. "Content based fake news detection using n-gram models." Proceedings of the 21st International Con-ference on Information Integration and Web-based Applications & Services. 2019.

Abdi, Asad, Norisma Idris, and Zahrah Ahmad. "QAPD: an ontology-based question answering system in the physics domain." Soft Com-puting 22.1 (2018): 213-230.

H. Kahaduwa, D. Pathirana, P. L. Arachchi, V. Dias, S. Ranathunga and U. Kohomban, "Question Answering System for the travel domain," 2017 Moratuwa Engineering Research Conference (MERCon), 2017, pp. 449-454,