STEMMING IMPACT ANALYSIS ON INDONESIAN QURAN TRANSLATION AND THEIR TAFSIR CLASSIFICATION FOR ONTOLOGY INSTANCES

Authors

  • Fandy Setyo Utomo Universitas AMIKOM Purwokerto https://orcid.org/0000-0001-6347-6514
  • Nanna Suryana Center for Advanced Computing Technology (C-ACT), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia https://orcid.org/0000-0003-3695-639X
  • Mohd Sanusi Azmi Center for Advanced Computing Technology (C-ACT), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

DOI:

https://doi.org/10.31436/iiumej.v21i1.1170

Keywords:

K-Nearest Neighbor, Neural Network, Ontology Learning, Ontology Population, Support Vector Machine

Abstract

The current gap which appears in the Quran ontology population domain is stemming impact analysis on Indonesian Quran translation and their Tafsir to develop ontology instances. The existing studies of stemming effect analysis performed in various languages, dataset, stemming method, cases, and classifier. However, there is a lack of literature that studies about stemming influence on instances classification for Quran ontology with different dataset, classifier, Quran translation, and their Tafsir on Indonesian. Based on this problem, our study aims to investigate and analyze the stemming impact on instances classification results using Indonesian Quran translation and their Tafsir as datasets with multiple supervised classifiers. Our classification framework consists of text pre-processing, feature extraction, and text classification stage. Sastrawi stemmer was used to perform stemming operation in text pre-processing stage. Based on our experiment results, it was found that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation owns the best classification performance, i.e., 70.75% for accuracy and 71.55% for precision in Indonesian Quran translation dataset on 20% test data size. While in 30% test data size, SVM and TF-IDF with stemming process own the best classification performance, i.e., 67.30% for accuracy and 68.10% for precision in Ministry of Religious Affairs Indonesia dataset. Furthermore, in this study, it was also discovered that the Backpropagation Neural Network has the most precision and accuracy reduction due to the negative impact of stemming operations.

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Author Biographies

Fandy Setyo Utomo, Universitas AMIKOM Purwokerto

Fandy Setyo Utomo received his Master's degree in Computer Science at the Faculty of Mathematics and Natural Sciences, Gadjah Mada University in 2015. He is a Ph.D student in Software and Information Systems Engineering at the Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM) since 2016. Currently, he works as a lecturer at Universitas AMIKOM Purwokerto. His research interests are ontologies and semantic web and their usage in question answering system.

Nanna Suryana, Center for Advanced Computing Technology (C-ACT), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

Nanna Suryana is currently a full professor and former Director of International Office at Universiti Teknikal Malaysia Melaka (UTEM), Faculty of Information and Communication Technology. He obtained his B.Sc. in Soil & Water Engineering at Padjadjaran University – Indonesia (1980), M.Sc. in Computer Assisted for Geoinformatics & Earth Science at International Institute for Geoinformatics and Earth Observation (ITC), Enschede – the Netherlands (1987), and Ph.D. in Geographical Information System (GIS) and Remote Sensing from the Department of GIS and Remote Sensing, The Wageningen Research University(WUR) – the Netherlands (1996). He is currently holding a position of Chairman of the Center of Advanced Computing Technology (C-ACT) - Centre for Research and Innovation Management, Faculty of Information and Commuication Technology (FTMK), UTeM. His current research interest and has published articles in journals, book chapters in field of GIS, Large Spatial Data and Information Retrieval, Image Processing, Spatial Modelling and Analysis. Mobile GIS and Interoperability.

Mohd Sanusi Azmi, Center for Advanced Computing Technology (C-ACT), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

Mohd Sanusi Azmi received his PhD from Universiti Kebangsaan Malaysia in 2013. Currently, he is the Head of Software Engineering Department in Universiti Teknikal Malaysia Melaka (UTeM), Faculty of Information and Communication Technology. His specialization is in feature extraction for the Arabic/Jawi handwriting image, having proposed a novel feature in the domain. He is also interested in image processing especially preprocessing, segmentation and classification of handwriting image in the Arabic/Jawi domain.

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Published

2020-01-20

How to Cite

Utomo, F. S., Suryana, N., & Sanusi Azmi, M. (2020). STEMMING IMPACT ANALYSIS ON INDONESIAN QURAN TRANSLATION AND THEIR TAFSIR CLASSIFICATION FOR ONTOLOGY INSTANCES. IIUM Engineering Journal, 21(1), 33 -50. https://doi.org/10.31436/iiumej.v21i1.1170

Issue

Section

Electrical, Computer and Communications Engineering