Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology)
Muhammad Arif Bijaksana
Telkom University
Computer Science & IT Environmental Science

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Sequence Chunking on Quran in English Translation using Bidirectional Long Short-Term Memory Try Arie; Muhammad Arif Bijaksana
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.2020.5.2.492

Abstract

Every Moslem is obliged to read and understand the meanings of the Quran. The problem is the amount of information contained in the Quran so that ordinary people have difficulty understanding the Quran as a whole. Neural networks can be used to extract important information in the Quran to solve this problem. Therefore, the author proposes a model to identify and classify tags using sequence chunking. The system will use the Bi-LSTM model where the system will be given various token from the Quran as the inputs to be identified as the correct tags. The author is using the dataset obtained from website quran.com. The evaluation of the proposed model produces an f-measure value of 0.903.
QURANIC KNOWLEDGE GRAPH: A MULTIDISCIPLINARY APPROACH TO MAPPING SEMANTIC NETWORKS IN THE QURAN Kemas Saleh Rahmat Wiharja; Muhammad Arif Bijaksana; Kemas Muslim Lhaksmana; Hafizh Putra Ardhana; Muhammad Aqil Ghazali Anhein
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Understanding the Al-Quran is the eternal goal of every Muslim, wherever they are. Before the emergence of the Knowledge Graph, readers of the Al-Quran needed to move from the Al-Quran to Tafsir, Hadith, scholar opinions or other resources in order to grasp the full meaning of a verse or a chapter in the Al-Quran. To help the readers of the Al- Quran in pondering the meaning of the Al-Quran and linking the verses to the meaning of the word or the interpretation of the verse in a tafsir book, we propose the first multi-layer Quranic Knowledge Graph that uses the property graph format. We also add a chatbot on top of our Quranic Knowledge Graph. And to save cost in using a Large Language Model, we deploy a vector database to memorize the previously answered user queries and their corresponding Cypher translations. We evaluate our approach using three point of views: large language model, knowledge graph, and chatbot. The result of evaluation shows that our Quranic Knowledge Graph achieves 100% correctness and 90% accuracy, despite only covering 9 parts of the Al-Quran. For the interested readers, please access https://zentilax.github.io/quranic-chatbot-UI/ for exploring the quranic knowledge graph
Co-Authors Hafizh Putra Ardhana Kemas Muslim Lhaksmana Kemas Saleh Rahmat Wiharja Muhammad Aqil Ghazali Anhein Try Arie