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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JUTI: Jurnal Ilmiah Teknologi Informasi Transformasi: Jurnal Pengabdian Masyarakat Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) SISFOTENIKA Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Jurnal Sisfokom (Sistem Informasi dan Komputer) INTEK: Jurnal Penelitian Jurnal Linguistik Komputasional EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer eProceedings of Engineering Jurnal INFOTEL
Moch Arif Bijaksana
Universitas Telkom, Bandung, Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences

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Searching and Displaying Al-Quran Verses from All Derivative Isim Makrifat Words to Support the Quranpedia Project Dzaky Ikram; Eko Darwiyanto; Moch. Arif Bijaksana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1076

Abstract

Quranpedia is a website built to facilitate its users in searching for root words and verses containing the exact root words by following the structure of Wikipedia. This research employs the black box testing method to assess the functionality of the Quranpedia website. The results of the black box testing indicate that the website achieves a success rate of 94%, with an intuitive interface that aligns with the presentation of the Al-Quran. Additionally, this study involves root word testing to evaluate the accuracy of the search results for Quranic verses that share the exact root words. From these tests, an accuracy level of 85% is obtained. The findings of this research demonstrate that Quranpedia successfully fulfills its primary goal by providing a reliable and comprehensive reference source for the Muslim community. With an engaging interface and an ongoing commitment to improving accuracy, Quranpedia is expected to assist the Muslim community in deepening their understanding and appreciation of the sacred book, the Al-Quran.
Searching and Comparing Isim Ma’rifat with Diacritic Removal in the Quran and Sahih Muslim Hadiths Ryan Fahreza Maliki; Eko Darwiyanto; Moch. Arif Bijaksana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1090

Abstract

This research aims to address the scarcity of comprehensive websites providing detailed lists of Isim Ma’rifat in the Quran and Sahih Muslim Hadith. The absence of a comprehensive resource hinders the ability to study and compare Isim Ma’rifat between these significant Islamic texts. To overcome this issue, the study develops a natural language processing approach utilizing an integrated Java tokenizer program with a MySQL database containing the Sahih Muslim Hadith and Quranic texts. The program identifies the occurrence of the alif lam prefix, followed by diacritic removal to facilitate accurate verse comparison between the two texts. The research focuses on identifying alif lam prefixed Isim Ma’rifat exclusively present in the Quran, exclusive to Sahih Muslim Hadith, and similarities between them. The analysis yields a comprehensive understanding of the distinctions and similarities of alif lam prefixed Isim Ma’rifat between the Quran and Sahih Muslim. These findings provide valuable input for the Al-Quran project, contributing to the development of comprehensive and accessible resources for Islamic studies. It is expected that this research will enhance the understanding of Isim Ma’rifat in the religious and linguistic context, offering a significant contribution to the field of natural language processing especially in the Arabic language.
Detection of Isim in Al-Qur'anic Verses using the Isim Marking Method and Creating Hyperlinks to Support the Quranpedia Website Project Muhamad Jibril; Eko Darwiyanto; Moch Arif Bijaksana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1106

Abstract

This research aims to detect isim words containing "???" (Alif Lam) in the verses of the Al-Qur'an using the isim marking method and creating hyperlinks to support the Quranpedia website project. The research follows the Agile methodology in project development. The findings reveal that approximately 12.97% of words in the Al-Qur'an contain "???" (Alif Lam). This information provides valuable insights into the frequency and distribution of isim words in the Al-Qur'an and reinforces support for the Quranpedia project.
Quranpedia Website Development Using the Joint Application Development Method Sarja Asra Winata; Eko Darwiyanto; Moch Arif Bijaksana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1131

Abstract

Quranpedia is a website built with codeigniter framework to facilitate muslims in searching for nouns and the verses containing the same nouns in the Quran by following structure of Wikipedia. The research employs the Joint Application Development in the process of developing the website and use black box testing to asses the functionality of the website according to the requirements. During the system requirements analysis process, Joint Application Development (JAD) relies on the active engagement of stakeholders to precisely capture users' viewpoints regarding business needs and collaboratively devise solutions. The extensive participation of stakeholders guarantees the effective representation of their opinions, fostering a collective effort in the development of solutions. To evaluate the website's functionality, black box testing is employed, and the results unequivocally demonstrate its high effectiveness in meeting all the specified criteria. The user-friendly interface, coupled with its comprehensive and responsive features, significantly enhances the process of studying the Quran, making it accessible to a wider audience. As Quranpedia continues to evolve and improve, it is expected to serve as an invaluable aid for the Muslim community in deepening their understanding of the sacred Al-Quran. By providing easy access to nouns and their associated verses, Quranpedia empowers users to explore the profound meanings and teachings within the Quran's verses, fostering a stronger connection to their faith and enriching their spiritual journeys.
Searching and Displaying Bukhari’s Hadith from All Derivative Words Isim Makrifat in Quranpedia using Extreme Programming Method Muhammad Althoof Nabalah; Eko Darwiyanto; Moch. Arif Bijaksana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1140

Abstract

Quranpedia is a website designed with a similar structure to Wikipedia. This website have goals to make it easier for users to find root words and verses of the Quran that contain this specific root. This study aims to evaluate the functionality of the Quranpedia website. The method used in this study is the black box testing method and the use of CodeIgniter 4. The results obtained from the testing reveal that the website achieves a high success rate of 97%, and the appearance of the resulting website proves to be user-friendly and in accordance with the presentation of the Bukhari Hadith. In addition, this study also tested word roots to see how accurate the search results for Bukhari Hadiths that have the same root word are. The results obtained from this test show that the accuracy rate is 83%. These results show that the website is very effective. Overall, this research demonstrates that Quranpedia effectively fulfills its main objective by providing a reliable and comprehensive reference source for the Muslim community. With a simple and user-friendly interface, as well as a commitment to continuously improve accuracy, Quranpedia is expected to be a valuable tool and can helping the Muslim community deepen their understanding and appreciation of hadith.
Derivative Words Scraping of Every Quranic Root Word from the Quran Corpus Web using Python to Support the Quranpedia Project Idzhari Syaeful Ma'mun -; Eko Darwiyanto; Moch. Arif Bijaksana
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1547

Abstract

The Qur'an, as a guide to life for Muslims, has given birth to various disciplines such as tafsir science, fiqh science, hadith science, nahwu science, and balaghah science. However, the limited number of websites on learning and understanding the Qur'an is a problem that can hinder Muslims from exploring the contents of the Qur'an. To overcome this problem, the Quranpedia project was initiated. Quranpedia is a web-based application designed to resemble Wikipedia in providing in-depth explanations of derivative words in the Qur'an. Using the "Scraping" technique, Quranpedia collects data from various sources to provide a comprehensive explanation of nouns in the Qur'an and Hadith. One of the main challenges in this project was to find the common root of nouns in the Qur'an and hadith. To overcome this challenge, a method was used to transform words from sentences to their root words. Thus, Quranpedia can have the ability to look up the root word of a noun. This allows users to have a better understanding of derivative words in the Qur'?n and how they are used in different contexts. The objective of this research is to create a derivative word scraping program that scrapes all derivative words in the Quran from the Corpus Quran web accurately. The problem discussed in this research covers both how one can scrape derivative words of each root word in the Quran from the Corpus Quran web and whether the data scraped from the web is complete and accurate. The method to ensure that these problems are solved includes using the Python programming language to create the program and then testing the program itself. The interim results achieved is whether the data is complete or not
Search and Comparison of Isim Ma'rifat with Remove Diacritic in the Qur'an and Hadith of Abu Daud Teuku Muhammad Ikhsan; Eko Darwiyanto; Moch Arif Bijaksana
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.3905

Abstract

This research aims to address the issues of searching for and comparing Isim Ma'rifat (definite nouns) with the prefix "AL" (ال) in the Quran and Hadith Abu Daud. Accurate sources of information regarding nouns in Islam are crucial, and the sayings of the Prophet (Hadith) serve as the primary reference for explaining Isim Ma’rifat. The Remove Diacritics method allows the removal of punctuation marks in the Arabic language, facilitating the search for Isim Ma'rifat with the prefix "AL" (ال) in both sources. The primary objective of this study is to evaluate the ability of Hadith Abu Daud to explain the Isim Ma'rifat found in the Quran. The comparison results reveal 376 instances of identical Isim Ma'rifat between the Quran and Hadith Abu Daud. Additionally, there are 611 instances of Isim Ma'rifat found solely in the Quran and 1388 instances found exclusively in Hadith Abu Daud. These findings provide insights into the capacity of Hadith Abu Daud to explain Isim Ma'rifat in the Quran. The search and comparison of Isim Ma'rifat in the Quran and Hadith Abu Daud hold significant contributions to the development of Quranpedia. Valid information about Isim Ma'rifat and the comparisons between the Quran and Hadith Abu Daud can enrich the content on Quranpedia. In creating the Quranpedia website, the search results for Isim Ma'rifat offer accurate information about nouns mentioned in the Quran, aiding users in better understanding the meanings and uses of Isim Ma'rifat in the religious context. The comparison between the Quran and Hadith Abu Daud provides insights into Hadith Abu Daud's ability to explain Isim Ma'rifat in the Quran. This information can be presented as articles or comparisons to enrich users' knowledge about the relationship between the Quran and hadith concerning the use of nouns in Islam.
Supervised Learning Approaches for Nested People Entity Extraction in Indonesian Translated Quran Dzidny, Dimitri Irfan; Bijaksana, Moch Arif; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (433.289 KB) | DOI: 10.47065/bits.v4i1.1758

Abstract

Since the Quran is the primary holy book for Muslims, information extraction research on Quranic texts, especially in a form of People Entity Extraction, is an important task for further Quran and Tafseer understanding. The challenges in extracting people entities from the Quranic text is that many verses have a complex structure, such as nested entities, making it crucial to build a system that can extract the entity automatically, accurately, and quickly. People Entity Extraction on Quran itself is a task that aims to extract people entities in a sentence or verse, such as the name of a person, the name of a group, etc. on the Quranic texts. Example of input taken from snippet Surah Al-Baqarah verse 46 which reads “Those who believe that they will meet their Lord and that they will return to him” from that input the people entity extraction system is expected can identify people entities i.e. “Those who believe that they will meet their Lord”. Currently, People Entity Extraction research for the Quran has not been widely carried out, only a few algorithms with scattered results have been conducted. In this research, we will use several supervised models which are Conditional Random Field (CRF), BiLSTM-CRF, and a pre-trained deep learning model based on IndoBERT transformers. We apply and perform a comparative analysis for the performance of those several models. We found out that deep learning based model, namely BiLSTM-CRF perform best at extracting people entities, whilst probabilistic based model, namely CRF, had difficulty in extracting people entities, specifically nested people entities.
Sentimen Analysis Social Media for Disaster using Naïve Bayes and IndoBERT Anugerah, Sri Mulyani; Wijaya, Rifki; Bijaksana, Moch Arif
INTEK: Jurnal Penelitian Vol 11 No 1 (2024): April 2024
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v11i1.4771

Abstract

The rapid advancement of information and communication technology has resulted in a significant surge in data, especially text data from social media platforms. This paper presents a sentiment analysis approach using IndoBERT and Naïve Bayes algorithms to classify sentiment related to natural disasters, specifically from a dataset of tweets derived from social media platform X. The focus of this research is to categorize tweets as positive and negative sentiment to provide useful insights in improving disaster response and management, with a focus on tweets related to earthquakes, floods, and the eruption of Mount Merapi. The goal is to assist the government in allocating aid more efficiently and understanding public sentiment during disasters. The methodology used includes data collection, data preparation, labeling, categorization, word weighting using tf-idf, data separation, and classification using Naïve Bayes and IndoBERT algorithms. The results showed that IndoBERT achieved 91% accuracy, while Naïve Bayes achieved 74% accuracy. The study highlights the potential of sentiment analysis in improving disaster preparedness and more effective response strategies.
Analyzing Public Sentiments on Disaster Relief Efforts Through Social Media Data Fakhruddin, Muhammad Rafi; Wijaya, Rifki; Bijaksana, Moch Arif
INTEK: Jurnal Penelitian Vol 11 No 1 (2024): April 2024
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v11i1.4773

Abstract

Social media has become a source of quick but not necessarily accurate information. Especially in social media X, which is often used to share information. This research aims to conduct sentiment analysis on posts related to natural disasters that aim to maximize assistance to victims of natural disasters. This research takes datasets from tweets on social media X, the data will be labeled into positive and negative. And then the preprocessing process will be carried out, in this study, categorization will be carried out on each tweet related to the category, then the data will be divided into training and testing. Then the Term Frequency-Inverse Document Frequency (TF-IDF) feature is used to assist in reducing the weight of words that often appear in the dataset, The next step involves designing a system with a focus on applying the Support Vector Machine (SVM) Polynomial Kernel algorithm which becomes a classifier which will later be used to find the best hyperline or decision boundary that divides each review into two classes, namely positive tweets and negative tweets. Then obtained with a value of Precision of 86.49%, Recall 99.21%, F1-Score 92.42%, and Accuracy of 87.01%. This research is expected to provide involvement in making a fast and effective decision for victims of natural disasters.
Co-Authors Abdul Raffi Malikul Mulki Abdurrahman, Azzam Ade Romadhony Adelya Astari Aditya Hanif Utama Ageng Prasetio Agni Octavia Agung Wardhana Z. Nasution Akip Maulana Al Faraby, Said Alfiya El Hafsa Alfredo Primadita Ali Ridho Fauzi Rahman Angelina Sagita Sastrawan Annisa Dian Muktiari annisa Imadi Puti Anugerah, Sri Mulyani Aqila, Neca Ardhi Akmaludin Jadhira Arie Ardiayanti Suryani Arie Ardiyanti Arie Ardiyanti Suryani Arief Fatchul Huda Arief Fatchul Huda Arief Fatchul Huda Arini Rohmawati Arlinda Dwi Ardiyani aulia khemas Heikhmakhtiar Bagus Ardisaputra Bambang Ari Wahyudi Bening Suryani Pratiwi Bhudi Jati Prio Utomo Darwiyanto , Eko Dea Delvia Arifin Dhafin Putra Aldi dina juni restina Djusnimar Zultilisna Donni Richasdy Dwi Marlina Sari Dzaky Ikram Dzidny, Dimitri Irfan Eki Rifaldi Eko Darwiyanto Fairuz Ahmad Hirzani Fakhruddin, Muhammad Rafi Falia Amalia Fauzan Ramadhan Fauziah, Salma Fernandy Marbun Floribertus Yericho Pramudya Galih Rizky Prabowo Gde Surya Pramartha Grace Duma Tambunan Hafsa, Alfiya El Huda, Arief Fatchul Huda, Arief Fatchul I Gusti Ayu Chandra Devi I Komang Resnawan Tri Putra I Made Darma Yoga I Nyoman Cahyadi Wiratama I Putu Prima Ananda Ibnu Asror Idzhari Syaeful Ma'mun - Ina Rofi’atun Nasihati Indra Lukmana Sardi Intan Khairunnisa Fitriani IZZAH, NURUL Jihan Ratnasari1 KD Krisna Dwipayana Kemas M Lhaksmana Kemas Muslim Lhaksmana Khalid kurnia sari lingga Kurniawan Adina Kusuma Luh Putri Ayu Ningsih Lukman Abdurrahman Meiditia Mustika Rani Miftahul Adnan Rasyid Mochamad Agung Permana Mohamad Syahrul Mubarok Mubaroq Iqbal Muhamad Jibril Muhammad Adib Imtiyazi Muhammad Althoof Nabalah Muhammad Aris Maulana Muhammad Budi Hartanto Muhammad Fakhri Ar-Razi Muhammad Faris Abdussalam Muhammad Haerunnur Syahnur Muhammad Rizki Chairulloh Muhammad Zidny Naf'an Munirsyah Munirsyah Muthia Virliani Mutia, Aufa Naufal Rasyad Neca Aqila Nisaa' 'Ainulfithri Nur Indrawati, Nur Nurul Izzah Patra , Gifaro Andyano Pramudita Oktaviani Prasetio, Ageng Puruhita Ananda Arsaningtyas Purwita, Naila Iffah Putri Cendikia Rahmad Geri Kurniawan Ramadhyni Rifani Ramanti Dwi Indrapurasih Rendy Andrian Saputra Retno Diah Ayu Ningtias Rifki Wijaya Riska Junia Wulandari Rizky Caesar Irjayana Ryan Fahreza Maliki Said Al Farab Sakinah Rahmi Salma Fauziah Sang Made Naufal Caesarya Mahardhika Saputro3,, Widyanto Adi Sarah Suryaningsih Sarja Asra Winata Sendika Panji Anom Shaufiah . Shervano Naodias Siagian Siti Sa'adah Siti Sa’adah Suryaningsih, Sarah Tegar Graha Adiwiguna Teuku Muhammad Ikhsan Totok Suhardijanto Triawati, Candra Valentino Rossi Fierdaus Wahyu Kurniawan Wahyu Purbaningrum Warih Maharani Widi Astuti Winda Eka Samodra Wiwin Aminah Yusuf Anugrah Putra Aditama ZK Abdurahman Baizal