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Arief Fatchul Huda
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Journal : Indonesian Journal on Computing (Indo-JC)

 1 
Entity Recognition for Quran English Version with Supervised Learning Approach Muhammad Aris Maulana; Moch. Arif Bijaksana; Arief Fatchul Huda
Indonesia Journal on Computing (Indo-JC) Vol. 4 No. 3 (2019): December, 2019
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2019.4.3.362

Abstract

The Quran is a Muslim holy book that consists of 6236 ayat or verses which divides into 144 surahs or chapters. In each chapter, there are many entities scattered in each verse. For a person, finding a particular entity will be difficult without a classification process, Resulting in difficulties in understanding the Quran. A system can be modeled to extract the information on entities in the Quran to solve this problem. Therefore, we want to offer a method to identify and classify entities using Entity recognition. The system will use the SVM techniques where the system will be given various entities from the Quran as an input to be able to identify correct entities. We are using the dataset obtained from website tanzil.net consists of 19.473 tokens and 720 entities. The classification scenario using a linear kernel with unigram produces the highest f-measure value of 0.75.Al-Quran merupakan kitab suci Muslim yang terdiri dari 6236 ayat atau bait yang dibagi menjadi 144 surah atau bab. Di setiap bab, ada banyak entitas yang tersebar di setiap ayat. Bagi seorang individu, menemukan entitas tertentu akan sulit tanpa proses klasifikasi yang membuat kesulitan dalam memahami Quran. Sebuah sistem dapat dimodelkan untuk mengekstrak informasi tentang entitas dalam Al-Quran untuk menyelesaikan masalah ini. Oleh karena itu, kami menawarkan sistem untuk mengidentifikasi dan mengklasifikasikan entitas menggunakan Entity Recognition. Sistem akan menggunakan teknik SVM di mana sistem akan diberikan berbagai entitas dari Quran sebagai input untuk dapat mengidentifikasi entitas yang benar. Kami menggunakan dataset yang diperoleh dari situs web tanzil.net terdiri dari 19.473 tokens dan 720 entitas. Skenario klasifikasi yang menggunakan linear kernel dengan unigram memperoleh nilai f-measure tertinggi sebesar 0,75.
Anaphora Resolution on Al-Quran with Indonesian Translation Arlinda Dwi Ardiyani; Moch Arif Bijaksana; Arief Fatchul Huda
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 2 (2020): September, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.2.496

Abstract

Al-Quran is the holy book of Islam, in Al-Quran we often find many cases of anaphora. Anaphora is a pronoun, for example “it” which refers to an object (antecedent) in the previous sentence. Antecedent of a pronoun is very important to understand the Al-Quran. Coreference Resolution with the classification model using the support vector machine method are needed to find out the antecedent. In this research, we use i feature and j feature for the extraction process. Based on the evaluation results, the system is able to find the antecedent of an anaphor with the best accuracy value of 86.36%.
Implementation of Dependency Parser Using Artificial Neural Network Methods Nurul Izzah; Moch Arif Bijaksana; Arief Fatchul Huda
Indonesia Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.504

Abstract

In recent years, parsing has become very popular within the scope of NLP (Natural Language Processing) with the presence of Dependency Parser. However, almost all existing Dependency Parser do classifications based on millions of sparse indicator features. This feature is not only bad in drawing conclusions, but also significantly limits the speed of parsing so that the resulting parsing is not optimal. To overcome these problems, changing the use of sparse features becomes dense features to reduce sparsity between words. The Artificial Neural Network classification method is used to produce fast and concise parsing in the Transition-Based Dependency Parser by using 2 hyperparameters. The dataset used in this study is Arabic, Chinese, English, and Indonesian. Based on the evaluation that has been done, it shows a higher result using the second hyperparameter. In testing with English test data, the accuracy value of LAS (Labeled Attachment Score) is 80.4% and UAS (Unlabelled Attachment Score) is 83%, Then with dev data obtained an accuracy value of LAS 81.1% and UAS 83.7%, and parsing speed of 98 sentences per second (sent/s).Keywords: Parsing, dependency parser, transition-based dependency parsing.
Co-Authors Arlinda Dwi Ardiyani Asep Solih Awaludin Moch Arif Bijaksana Muhammad Aris Maulana Nurul Izzah Qonita Ummi Safitri