Khazanah Journal of Religion and Technology
Khazanah Journal of Religion and Technology In understanding Religion, we must understand how religious ideas and practices are communicated, studied, represented, enforced, and countered through technology. Currently, religion circulates in the technology and media space, religion is found in films, social media, games and even religious institutions also produce their own media and technology, starting from websites, social media, and television to applications and games. Khazanah Journal of Religion and Technology (E-ISSN 2987-6060) strives to be a place for various empirical research related to the intersection between religion and technology. Khazanah Journal of Religion and Technology published by the Asosiasi Khazanah Cencekia, Indonesia and has been accredited Sinta 4 by Ministry of Higher Education, Science, and Technology, Republic of Indonesia as an academic journal based on Decree of the Director General of Research and Development Number 156/C/C3/KPT/2026 dated April 7, 2026. Original writing that adds to the social-scientific conversation about how religion and technology interact is published in the Khazanah Journal of Religion and Technology. Any research on a particular religious tradition, literary genre, or geographic area is welcome. Though historical studies may also be taken into consideration, the journal's major concentration is on recent and contemporary media and technology. Theological writings will not normally be accepted for publication.
You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Notices: You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
The Influence of Transformational Education Prediction on Softskills of Madrasa Student using Data Mining
Currently, transformative education is oriented towards students' independence in solving a problem they face. In other words, transformative education has its own influence on the soft skills of students. This research was conducted with the aim of providing predictions regarding the effect of transformative education on the soft skills possessed by students. The subjects in this study were students of Madrasah Aliyah Negeri 2 Bandung, ranging from grade 10 to grade 12. The research method used was the Naïve Bayes and K-Nearest Neighbor algorithm by taking datasets from an independent survey that had been carried out previously. The Naïve Bayes and K-Nearest Neighbor algorithms themself are included in supervised learning and can be used to predict with a high degree of accuracy. Supervised learning is one of the existing methods in machine learning by means of labeling the data trained by the machine. Testing the data using the Naïve Bayes and K-Nearest Neighbor algorithm obtains predictions that transformative education affects students' soft skills and produces a very high level of accuracy for the Naïve Bayes algorithm, namely 98% of the 100 existing datasets and accuracy of the K-Nearest Neighbor is 76,67%.
Copyright (c) 2023 Andin Aprila Sari, Annisa Sugi Pramesty, Asma Zulfiah Malik, Dini Nurul Haq Al-Hidayah, Laela Shintia Alviani, Rifka Alia Assyifa https://creativecommons.org/licenses/by/4.0
Comparison of the Fisher-Yates Shuffle and the Linear Congruent Algorithm for Randomizing Questions in Nahwu Learning Multimedia
Nahwu Quiz is a basic Arabic learning application that can be played by the public over the age of 12 years. In the question practice menu, there are questions and 4 multiple choice questions. The user only needs to choose one of the multiple choices that the user thinks is correct/matches the question at hand. In one game, there are 5 questions. After answering all these questions, you will immediately see the score. The purpose of developing this application apart from being a medium of entertainment as well as a medium of learning and memory training for game users (users). To make this Nahwu Quiz application, the authors use the Fisher Yates Shuffle (FYS) algorithm which is used to perform the randomization function in multiple choice and the Linear Congruent Method (LCM) algorithm as a comparison. White box and black box testing were applied to see the feasibility of the program and to obtain efficiency in the comparison of randomization methods. The results of white box and black box testing on the application show that the application is feasible. with reference to the white box test results that the FYS algorithm and the LCM have the same complexity as the result of cyclomatic complexity = 2.
Copyright (c) 2023 Ukan Saokani, Mohamad Irfan, Dian Sa'adillah Maylawati, Rachmat Jaenal Abidin, Ichsan Taufik, Riyan Naufal Hay's https://creativecommons.org/licenses/by/4.0
Welfare Classification of Muslim Majority Communities using Decision Tree Algorithm
Community welfare is a benchmark of various conditions that can represent a quality community life. It is undeniable that welfare in a comprehensive sense can be realized if certain economic conditions can be achieved, one of which is prosperity. Economic freedom is a framework in which principles compatible with the ideals of prosperity are implemented in economic institutions and processes. Important components in economic freedom such as size of government, legal system and property rights, sound money, freedom to trade internationally, and regulation have a significant influence on the welfare of the people of a country. This study aims to examine the effect of economic freedom on the welfare of the people in several countries with the Muslim majority population. By using unsupervised learning methods and decision tree algorithms in the classification, the experiment result found that 35% of countries with a majority Muslim population in the world were classified as Moderately Free.
Copyright (c) 2023 Naufal Berlian, Andi Malia Fadilah Bahari, Jidan Ramdani, Rico Candra, Intan Permata Sari, Maulana Aditya https://creativecommons.org/licenses/by/4.0
The Influence of the Use of Social Media on the Intensity of Worshipping the Millenial Generation using Linear Regression
Worship is an activity carried out by every religious community. Sometimes there are activities that can interfere with worship activities, especially in the current era of globalization. In the current era of globalization, many people use their smartphones or gadgets for their daily needs. The millennial generation is one of the generations that has begun to be introduced to this gadget or smartphone, so that many of these millennial generations cannot be separated from what is called social media. During the COVID-19 pandemic, almost all our daily time is used to view social media. The purpose of this study was to see the effect of using social media on the intensity of worship of the millennial generation. The method used to assist this research is to use linear regression. The result of this research shows that the use of gadgets for a long time is accompanied by an extraordinary intensity of worship. This research also concludes that there is no influence given using social media on the intensity of worship of the millennial generation.
Copyright (c) 2023 Fakhri Faisal Rochdiana, Muhammad Rizki Isa Darmawan, Nurul Aulia Dewi, Wafda Zulfa Husna, Zulfa Dwi Audina, Nunik Destria Arianti https://creativecommons.org/licenses/by/4.0
Maintaining Religious Harmony through Predicting the Level of Lawlessness using Linear Regression
Crimes are basically committed due to low human faith and ultimately disrupt harmony, including inter-religious harmony. Therefore, the purpose of this study is to predict and determine the number of law violations that occurred in Yogyakarta Province in 2022 as a case study using a linear regression algorithm based on data ranging from 2012 to some of the data on law violations in 2022. The method used is the forecasting method in which the stages of data collection, data preprocessing, data modeling, and prediction processes are carried out. The accuracy of the linear regression model is 90%. However, there is a difference in accuracy between the training data and the test data. The training data gets an accuracy of 99% while the accuracy achieved by the test data is at 94% which is 5% lower than the training data. The prediction results using the linear regression model shows the number of the data on law violation that will occur in 2022 with correlation between crimes.
Copyright (c) 2023 Adam Wardana, Dekis Aldamawan, M. Rival Ridautal Lillah, Mochamad Anwar Wirahadi, Mohamad Rasyid Ridho, Naufal Rizqullah https://creativecommons.org/licenses/by/4.0
Convolutional Neural Network (CNN) for Detecting Al-Qur'an Reciting and Memorizing
This research aims to make it easier to memorize the Koran without having to need other people. Memorizers of the Koran (hafiz) often need other people to memorize them to find out if there are errors in their reading. Therefore, this research utilizes machine learning technology to make it easier to read and memorize the Al-Qur'an using the Convolutional Neural Network (CNN) algorithm. CNN was chosen because it is very good at classifying images and audio and can learn and extract features from raw data, such as image and audio data automatically. As a result, the model created succeeded in distinguishing one verse from another very well. The validation results show that the model can correctly detect 57 verses from 64 recorded data, which means it has an accuracy rate of 89.06%. With this verse classification model, it can then be implemented into an application to help memorize the Al-Qur'an even without using the internet.
Copyright (c) 2023 Rizal Hadiyansah, Rafi Andamira https://creativecommons.org/licenses/by/4.0
A A Determining the Law of Reading Tajweed (Idgham Qomariyah and Syamsiyah) in the Qur'an Using the Naïve Bayes Algorithm
Learning tajweed to recite the Qur’an is important. Because if there is a mispronunciation, the meaning will be different. This research aims to detect two of the many tajweed, namely idghom qomariyah and syamsiyah using machine learning technology with a classification approach. This research uses the Naive Bayes algorithm to classify idghom qomariyah and syamsiyah in Al-Quran text documents. Based on experimental results using 82,173 text data, Naive Bayes was able to classify idgham qomariyah and syamsiyah with an accuracy rate of 96,80%.
Copyright (c) 2023 Haikal Azhar, Ibham Bathsyi Hizbullah https://creativecommons.org/licenses/by/4.0
Religious and Ecclesiastical Media in the Hungarian Electronic Media Ecosystem
Many academic works examine religious media institutions and media content producers operating in European countries, including Hungary, but most of them do so in an isolated way, mostly focusing on a particular content producer. The present study aims to provide a comprehensive picture of the operation of religious content producers in Hungary in a broader context, which considers the overall media system from a legal and economic point of view. First, we give a short retrospective overview about the history of Hungarian church press. Afterwards we examine the current legal framework for audiovisual regulation in Hungary. The conclusions are based on data obtained using three different methodologies. We have analyzed the freely accessible records of the Hungarian media authority (NMHH) and legislation, conducted a total of 6 in-depth interviews and an analysis of the content produced by each media outlet. The results of our research showed that in Hungary there is a disconnect between the concepts of church and religious media and that churches have hardly any comprehensive strategy to exploit the potential of digitalization. It can be concluded that church-owned media with religious content cannot be included in the present legal and economic framework without problems. Finally, an important question is whether and how church media are adapting to the changed environment. Our case studies show that the picture is mixed in this respect, because there is a lack of a coherent strategy among the actors on how service providers can adapt as effectively as possible to the new technological consumption patterns.
Copyright (c) 2023 András Radetzky https://creativecommons.org/licenses/by/4.0
Deux ex Machina (“ God From the Machine”): Exploring Digital Worship in the Salvation Ministries, Port Harcourt, Nigeria
This paper presents a sociological exploration of the digital worship practices of the Salvation Ministries in Port Harcourt. It investigates the impact of technology on the members’ and how it engages their relationship with the Divine. Utilizing qualitative research, including interviews with members and analysis of online reviews, the study examines the role of digital worship in fostering relationships between members and the divine. It employs the Media and Technological Determinism theories to examine this relationship in depth. The paper also examines the implications of the emergence of the “God from Machine” in worship services, arguing that worshippers are constructing a new type of connection that transforms their religious experiences. The findings demonstrate that digital worship has significantly expanded the reach of Salvation Ministries beyond its physical boundaries. Virtual sermons and prayer sessions have strengthened the sense of community among members, facilitating easier connections with leaders and fellow worshippers. However, caution is advised regarding the potential risks of excessive reliance on technology, diminished personal interaction, and substituting genuine spiritual experiences. In conclusion, the paper emphasizes the importance of balancing embracing technology and preserving the authenticity and sincerity of spiritual practices.
Copyright (c) 2023 Christopher Onyewuchi Opara, Ndidi Justice Gbule, Rowland Olumati https://creativecommons.org/licenses/by/4.0
Prediction of Skin Diseases using Convolutional Neural Networks as an Effort to Prevent Their Spread in Islamic Boarding School Environments
Skin disease is a common health problem in Islamic boarding school environments. This disease can spread quickly among students due to close contact and sharing the same facilities. Preventing the spread of skin diseases is a top priority in efforts to maintain the health and welfare of students in Islamic boarding schools. In this research, we propose the use of machine learning techniques to predict skin diseases in Islamic boarding school students. The main goal of this research is to develop a predictive model that can help identify skin diseases quickly and accurately. It is hoped that this will enable the prevention of the spread of skin diseases in the Islamic boarding school environment. The method used in this research involves the following steps: skin disease image data collection, data processing and cleaning, feature extraction from patient data, and machine learning model training and evaluation. We will use a Convolutional Neural Network (CNN) machine learning algorithm to build a predictive model. The dataset used in this research consists of images of melanoma, acne and acne skin diseases. In addition, validation will be carried out using data that has never been seen before to test the performance of the predictive model. It is hoped that the results of this research can make a significant contribution in preventing the spread of skin diseases in the Islamic boarding school environment. With accurate predictive models, health workers in Islamic boarding schools can take appropriate preventive measures to control skin diseases effectively. Apart from that, this research can also be a basis for developing a health information system that supports preventive measures for skin diseases in Islamic boarding schools more widely.
Copyright (c) 2023 Ilham Rizky Agustin, Muhammad Bayu Nurdiansyah Putra https://creativecommons.org/licenses/by/4.0
Optimizing Qur'an Recitation Monitoring with Random Forest Algorithm
Every Muslim should improve their worship, including reciting the Al-Qur'an. Reciting the Al-Qur'an is a deep spiritual practice that provides spiritual benefits and blessings daily. To maximize the benefits of reciting Al-Qur'an during this holy month, implementing Machine Learning can make a significant contribution. This research explores the application of Machine Learning using the Random Forest algorithm to improve the practice of reciting the Al-Qur'an. By collecting data through surveys using questionnaires, this research identifies important factors that influence an individual's success in completing reading the Al-Qur'an. The research results show that the Random Forest algorithm can be used to predict the number of individuals who have the potential to complete reciting Al-Qur'an with an accuracy value of 80%.
Copyright (c) 2024 Ivan Wijayana, Muhammad Ardiansyah https://creativecommons.org/licenses/by/4.0
Digital Transformation: Digitising Witness Statements in South African Policing in Alignment with Religious Principles
The utilisation of new technologies towards supporting effective and accountable policing is increasingly dependent on e-government development and other technological advancements. England and Wales have long moved from paper witness statements to digitally recorded statements, known as Digital Witness Statements (DWS). DWS refers to the digital capture, representation, and storage of the information needed for its use as an evidential witness statement in courts in England and Wales. This paper presents a prototype system that could be employed in the South African policing context, aligning with religious principles to ensure cultural sensitivity and ethical considerations. The aim of this study was to create awareness that digitisation has been an efficient method of capturing witness statements in the European policing context. Additionally, this study aimed to highlight the status quo of taking indigenous witness statements in South Africa. The objective was to explore the phenomenon of taking indigenous witness statements and to establish the South African Police Service (SAPS) members’ perceptions of the current methods. The study also examined how incorporating religious principles can enhance the acceptance and effectiveness of these digital methods within diverse communities. Thematic content analysis was conducted using Atlas.ti, and measures of trustworthiness were applied. Three dominant themes—“skills,” “resources,” and “duration”—emerged. The Theory of Performance (ToP) was used, and results indicated that SAPS members identified resources as a barrier to their performance. Integrating religious principles into the digitisation process may address some cultural barriers, fostering a more inclusive and respectful approach to modernising witness statement collection.
Copyright (c) 2024 Nomsa Ingrid Zikalala, Jacob Tseko Mofokeng, Moses Morero Motseki, Enoch Zenzile https://creativecommons.org/licenses/by/4.0
Classifying Hijaiyah Letters Handwritten Detection of Children Using CNN Algorithm
Learning the Hijaiyah letters is an important basis because in learning the Qur'an, these abilities must be mastered before they can be introduced and taught to children. However, the recognition of Hijaiyah letters in children's handwriting is still a challenge due to the variations and inconsistencies that are often found. Deep learning technology, particularly Convolutional Neural Network (CNN), has demonstrated its ability to classify letters with a high degree of accuracy. Therefore, this research aims to develop a CNN-based Hijaiyah letter classification model to help children learn to write and read Hijaiyah letters properly. This research uses a CNN model that is optimized with data augmentation techniques and hyperparameter tuning. The model was trained using a standard dataset totaling 1,740 samples of Hijaiyah letters. Model evaluation is done by calculating accuracy, precision, recall, and F1-Score on the validation dataset. The results showed that the proposed CNN model achieved almost 94.35% accuracy on the validation dataset. This research is expected to improve children's ability to learn Hijaiyah letters.
Copyright (c) 2024 Ahmad Maulidi Roofiad, Annisa Sabillah, Aprian Nur Rohman, Elmi Wahyu Triyani https://creativecommons.org/licenses/by/4.0
SoulScripture: Chatbot using Bidirectional Encoder Representations from Transformers as a Medium of Spiritual Guidance
Mental health is an important aspect of human life. Many people face stress, anxiety, and distress daily without adequate support to manage these conditions. Islamic teachings from the Quran and Hadith provide wisdom as a source of inspiration and inner peace. However, accessing and understanding these teachings requires specialized knowledge and often the help of experts. With the advancement of machine learning, these teachings can be made more accessible and accurate. The SoulScripture app offers an innovative solution to support mental health by combining the wisdom of the Quran and Hadith through AI technology. Using the Bidirectional Encoder Representations from Transformers (BERT) model and Transformer architecture, the app can understand and provide relevant advice that anyone can access anytime. This research is significant because it offers a new approach to leveraging technology to support mental well-being, especially for communities underserved by conventional mental health services. The app was developed using self-supervised learning to understand the text of the Hadith without external annotation. This process involves several stages, such as user input, data preprocessing, and text analysis, to generate relevant answers. It is hoped that the SoulScripture application can serve as a source of inspiration and support for individuals in controlling stress and maintaining peace of mind, as well as supporting the achievement of the Sustainable Development Goals (SDGs) related to mental health.
Copyright (c) 2024 Andhika Malik, Andhika Putra Gefadri, Elman Sidik, Alika Putie Syadrina https://creativecommons.org/licenses/by/4.0
The Cultural Relationship between Christianity and Technology for the Practice of Christian Mission
The involvement of all members of society in the advancement of technology is becoming a crucial aspect since the pace of technological advancement has accelerated markedly. Christianity, as a constituent of society, is also being called upon to reflect on technology and to apply it in tangible ways within the social context. This study examines how Christianity can contribute to the formation of social norms reflected in the development and operation of technology. The methods that this study uses include an initial investigation undertaken into the concept of technology based on the theory of the philosopher of technology, Langdon Winner. Subsequently, the study elucidates the interrelationship between Christianity and technology by demonstrating that technology and Christianity are human cultural systems on an equal footing. Finally, this study endeavors to facilitate an interdisciplinary dialogue between the philosophy of technology and public theology to postulate the potential for technology and Christianity to exert reciprocal influence on one another. Throughout these methods, this research defines the cultural relationship between technology and Christianity and finds influencing the trajectory of technological advancement from a Christian perspective constitutes a missional practice.
Copyright (c) 2024 Min Hyoung Lee https://creativecommons.org/licenses/by/4.0
Leveraging Artificial Intelligence Technologies in the Service of the Holy Quran and Its Sciences
The research addresses the growing role of artificial intelligence in serving the Holy Quran and its sciences, reviewing the enormous potential that this field provides in improving the understanding, teaching, and learning of the Holy Quran. The research also aimed to demonstrate the importance of employing artificial intelligence technologies in serving the Holy Quran and its sciences, by presenting the importance of artificial intelligence technology and investing it in Islamic sciences, Islamic applications, serving the Noble Prophetic Hadith and its sciences, serving the objective interpretation of the Holy Quran, memorizing the Holy Quran and reciting it, in addition to issuing fatwas and spreading religious awareness. The research relied on a mixed research methodology, which included a qualitative analysis of the data, and the theoretical framework was prepared by referring to the literature that addressed the topic, including academic studies, websites, and electronic applications. By reviewing the applications of artificial intelligence and its importance to the topic under study, the results of the study showed great potential for using artificial intelligence in serving the Holy Quran and its sciences. These technologies are capable of facilitating the process of learning and teaching the Holy Quran and improving the understanding of meanings and interpretations .Based on these results, the study recommends intensifying research efforts in this field with a focus on areas such as analyzing Quranic narratives and understanding rhetorical meanings, encouraging cooperation between religious scholars and artificial intelligence experts to develop integrated applications that meet the needs of students and researchers, as well as encouraging religious and educational institutions to adopt these technologies and provide the necessary support for their development and application.
Copyright (c) 2025 Basheer Adnan https://creativecommons.org/licenses/by/4.0
IndoT5 (Text-to-Text Transfer Transformer) Algorithm for Paraphrasing Indonesian Language Islamic Sermon Manuscripts
Pengembangan sistem parafrase otomatis dalam bahasa Indonesia semakin relevan seiring dengan meningkatnya kebutuhan aplikasi pemrosesan bahasa alami. Penelitian ini berfokus pada penerapan algoritma IndoT5 (Text-to-Text Transfer Transformer) untuk membangun sistem parafrase otomatis dalam bahasa Indonesia. Sistem ini dievaluasi menggunakan metrik BLEU, ROUGE, dan METEOR untuk mengukur kesamaan antara teks parafrase yang dihasilkan model dengan teks target yang diinginkan. Hasil evaluasi menunjukkan nilai BLEU sebesar 0,28, ROUGE-1 sebesar 0,59, ROUGE-2 sebesar 0,40, ROUGE-L sebesar 0,55, dan METEOR sebesar 0,50. Hal ini menunjukkan bahwa model mampu menghasilkan parafrase dengan tingkat kesamaan yang moderat dan mempertahankan makna semantik yang cukup baik.
Copyright (c) 2025 Winda Puspitasari, Dani Ramdani, Aditya Muhamad Maulana https://creativecommons.org/licenses/by/4.0
Text-to-Speech Technology Development Using FastSpeech2 Algorithm for the Story of the Prophet
With the SDGs target point 4.6 for 2030, literacy is a very important thing to improve. With today's technological advancements, improving the accessibility of reading in the digital age is becoming increasingly important, especially for individuals with time constraints. Text-to-Speech (TTS) technology allows users to enjoy text content, such as books or journals, in audio format, which can be listened to while doing other activities. This research develops a TTS model based on the FastSpeech2 algorithm, a non-autoregressive deep learning architecture that utilizes Transformers to generate high-quality audio quickly and efficiently. The LJSpeech dataset, which consists of 13,100 audio chunks with a total duration of 24 hours, is used as the training base. The preprocessing process involves text normalization, audio feature extraction, and data synchronization, while evaluation is performed using objective metrics such as Mel Cepstral Distortion (MCD) and Pitch Error to ensure the quality of the results. The results show that FastSpeech2 can provide fast and accurate performance in generating synthesized voices, making it potential to be used in various audio literacy applications. A key application of this TTS technology is in narrating the stories of the Prophets, which are essential in Islamic teachings for imparting moral values, fostering spiritual connection, and offering timeless lessons. The results show that FastSpeech2 is able to produce high-quality audio quickly, making it an effective alternative for improving audio literacy and providing a solution for individuals with limited reading time.
Copyright (c) 2025 Muhammad Raihan Firdaus, Muhammad Rihap Firdaus, Pancadrya Yashoda Pasha https://creativecommons.org/licenses/by/4.0
Slang Spelling Detection in Indonesian Religious Content Using Sequence-to-Sequence Algorithm
Spelling errors are a common problem in text processing, one of which is in Indonesian. The increasing use of non-standard language, especially in digital text communication, is the background for this research. Spelling errors in sensitive religious content can even cause misunderstandings. This article examines the development of a model for spelling correction with a deep learning-based approach using Sequence-to-Sequence with a GRU-based encoder-decoder architecture and attention mechanism. A dataset containing standard and non-standard text pairs is used to test the model. The experimental results show that the proposed model produces 76.82% accuracy, but is able to recognize and correct spelling errors. However, this research is expected to contribute in the future, so that it can improve improvements in the Indonesian spelling correction system.
Copyright (c) 2025 Nanda Tiara Sabina Hidayatulloh, Nur Halizah, Pitriani https://creativecommons.org/licenses/by/4.0
Sentiment Analysis of Marketplace Review with Islamic Perspective using Fine-Tuning DistilBERT
E-commerce apps have become an important part of modern life, with user reviews playing a crucial role in assessing platform performance and identifying areas for improvement. This research aims to apply the Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) model to analyze the sentiment contained in user reviews of Tokopedia and Shopee apps on the Google Play Store. Along with the rapid growth of e-commerce in Indonesia, platforms such as Tokopedia and Shopee have become an important component in facilitating fast and easy online transactions. User reviews on these platforms are a valuable source of information for evaluating customer satisfaction, but the increasing volume of reviews makes manual sentiment analysis inefficient. This research uses a dataset of Indonesian-language reviews on Tokopedia and Shopee Apps on Google Play Store to classify sentiments into positive and negative categories by utilizing DistilBERT, which is a lightweight variant of BERT with the ability to efficiently process large data without sacrificing accuracy. The results of this analysis provide insights that can assist e-commerce platforms in improving user experience as well as support data-driven decision-making for application development. This research contributes to the application of natural language processing (NLP) technology for sentiment analysis in the context of e-commerce in Indonesia.
Copyright (c) 2025 Reza Fahlevi Reza, Muhmmad Thoriq, Rd. Imam Saepul Millah https://creativecommons.org/licenses/by/4.0
Finding Important Patterns of Sadaqah and Waqf Transactions in the Eid Charity Global Donation using Frequent Pattern Growth
This study aims to explore and uncover hidden patterns in charitable donation transactions (sadaqah and waqf) using the Frequent Pattern Growth (FP-Growth) algorithm as a method of association rule mining. The research employs an experimental design conducted on the Google Colaboratory platform using Python version 3.11.3. The dataset used is the global donation data from Eid Charity, available on the Kaggle platform, comprising 45,842 rows and 33 attributes. The experimental results yielded six association patterns, all of which have the consequence of "mosques," indicating that various forms of donations consistently culminate in fund allocations to mosques. The most significant pattern was found in the combination of “Ensuring preachers and imams are conditional” and “Well Drilling,” which produced the highest metric values in confidence (0.896), lift (3.39), conviction (7.08), and Zhang’s metric (0.714). These findings suggest that mosques serve as the central hub of philanthropic activity within the dataset. The discovered patterns provide deep insights into donor behavior tendencies and can serve as a foundation for more targeted and effective strategies in managing and developing future donation programs.
Copyright (c) 2025 Rafif Sunu Fauzi, Sabilah Arianisa, Viqriza Ahmad Vahira, Mohamed https://creativecommons.org/licenses/by/4.0
Systematic Literature Review: Deep Learning Models in Arabic Script Classification
Arabic calligraphy is an essential element of Islamic art, and is now widely developed in digital form. With the advancement of artificial intelligence technology, particularly Convolutional Neural Networks (CNNs), several studies have been conducted to classify the styles, characters, and authenticate Arabic calligraphy. This study aims to conduct a systematic literature review on the application of CNNs in the recognition and classification of Arabic calligraphy. The identification process was carried out by searching several scientific databases and screening 152 articles, but only five studies met the criteria for relevance and eligibility. The results of the study indicate that the application of CNNs in this domain is still limited and dominated by a focus on style or letter classification, while topics such as authenticity of original works and AI-generated calligraphy detection are still very rarely researched. The limited number of available studies indicates that this topic is an open area for further exploration in the academic realm and the development of digital Islamic art preservation technology.
Copyright (c) 2025 Muhamad Khansa Khalifaturohman https://creativecommons.org/licenses/by/4.0
CRNN Algorithm and MFCC Feature Extraction in Classifying Hijaiyah Letter Pronunciation: A Systematic Literature Review
The ability to read the hijaiyah letters correctly is an important foundation in learning the Qur'an. However, the low Qur'an literacy in Indonesia indicates the need for technological innovation to support the learning process. This article presents a systematic literature review on the application of the Convolutional Recurrent Neural Network (CRNN) algorithm and the Mel-Frequency Cepstral Coefficients (MFCC) feature extraction technique in speech classification, with a focus on their potential implementation for recognizing the pronunciation of the hijaiyah letters. The analysis was conducted based on ten relevant studies selected using the PRISMA method. The results of the study indicate that MFCC is effective in representing the phonetic characteristics of sounds, including in the Arabic context. Meanwhile, CRNN has proven superior in managing audio data with tempo and sequential structure. The combination of the two has strong potential to build an accurate and adaptive hijaiyah letter sound classification system, especially in supporting speech-based tajweed learning. This study provides a conceptual basis for the development of an artificial intelligence-based Qur'an learning application.
Copyright (c) 2025 Mohammad Putra Fauzan Fatah https://creativecommons.org/licenses/by/4.0
Sharia Stock Investment Decision Making Using the Deep Recurrent Q-Network Model
This study aims to design and evaluate a Deep Recurrent Q-Network (DRQN) agent for automated trading decision-making on Islamic stocks, training it with daily historical price data from the Indonesian Islamic Stock Index (ISSI) and integrating a Long Short-Term Memory (LSTM) layer. Although the agent successfully learns a profitable strategy during the training phase, on unseen test data, it exhibits passive behavior by only choosing the 'hold' action, resulting in zero profit—a phenomenon known as policy stagnation. This finding indicates that the used reward function implicitly encourages excessive risk aversion. The study concludes that the success of the DRQN architecture relies heavily on sophisticated reward engineering, underscoring the need for future research on dynamic and adaptive reward mechanisms to develop robust and generalizable trading agents in the complex Islamic finance domain.
Copyright (c) 2025 Jasmein Al-baar Putri Rus'an, Muhammad Zidan, Muhammad Arkan Raihan, Marleni Sukarya, Sendi Ahmad Rafiudin https://creativecommons.org/licenses/by/4.0
Shaping Identities, Shaping Futures: The Socio-Religious Impact of Digital Culture on Nigerian Youth
In the digital age, social media platforms have revolutionized the way young people form and express their identities. This study examines the complex and multifaceted relationship between digital culture and youth identity formation in Nigeria, a country with a rapidly growing youth population and increasing social media penetration. Using a mixed-methods approach, this research explores how digital platforms shape and reshape youth identities, influencing their self-concept, relationships, and worldviews. The study draws on two theoretical frameworks: technological determinism and social learning theory, providing a rich understanding of the interplay between digital culture and youth identity. The findings of this study reveal a paradoxical impact of digital culture on youth identities in Nigeria. On the one hand, digital platforms offer opportunities for self-expression, activism, entrepreneurship, and innovation. On the other hand, they also pose significant risks, including the erosion of cultural and religious identity, social media pressure, cyber-bullying, and other negative consequences. This study contributes to the growing body of research on digital culture and youth identity, highlighting the need for a much clearer understanding of the complex relationships between digital media, identity formation, and socio-religious contexts. The findings of this study have implications for policymakers, educators, and parents seeking to navigate the challenges and opportunities of digital culture for young people in Nigeria.
Copyright (c) 2025 Tonbrapade Japheth, Ndidi Justice Gbule, Christopher Opara https://creativecommons.org/licenses/by/4.0
The Digital Sacred: A Phenomenology of Religious Experience in Algorithmic Worlds
This article examines how digital technologies are fundamentally transforming the nature of religious experience, creating new modes of encounteringthe sacred that challenge traditional phenomenological and theological frameworks. Moving beyond debates about authenticity or mediation, weargue that algorithmic environments actively reshape the very conditions of spiritual engagement, generating hybrid forms of religiosity where humanand machine agencies intertwine. Through an analysis of virtual rituals, AI-generated scripture interpretation, and platform-governed devotion, thestudy reveals how digital infrastructures reconfigure core concepts of presence, intentionality, and transcendence. The sacred emerges not merely ascontent within digital spaces but as a dynamic product of networked interactions between users, algorithms, and data architectures. This technologicalshift demands new theoretical approaches that account for distributed agency, quantified spirituality, and the posthuman dimensions of contemporaryworship. By synthesizing insights from phenomenology, philosophy of technology, and religious studies, we propose a framework for understandingdigital religion as neither authentic nor simulated, but as a distinct ontological category—one that requires reimagining traditional notions ofembodiment, ritual efficacy, and divine encounter in light of computational systems. The article ultimately calls for developing a techno-theologycapable of addressing both the transformative potential and ethical challenges of spiritually significant algorithms, while recognizing digitalenvironments as legitimate sites for sacred experience in the 21st century.
Copyright (c) 2026 Mahmoud Khatami https://creativecommons.org/licenses/by/4.0
XGBoost and Mixed Effect Model: Can Zakat (Alms) Improve the Human Development Index in Indonesia?
This study aims to analyze the effect of zakat distribution on the Human Development Index (HDI) in Indonesia by comparing two modeling approaches: the Mixed Effect Model (LMM) and Extreme Gradient Boosting (XGBoost). The data used are panel data from 34 provinces during the 2021–2024 period, which includes sectoral zakat distribution and HDI variables. Initial exploration results indicate spatial variation and a non-linear correlation between zakat distribution and HDI. The Mixed Effect model demonstrated more stable predictive performance with an RMSE of 1.633 and an R² of 0.762 on the test data. While XGBoost, despite its high accuracy on the training data, exhibited overfitting with a decrease in accuracy on the test data. These findings indicate that the LMM approach is superior in capturing spatial and temporal variations between regions and provides more applicable interpretations in the context of public policy. This study contributes to the use of statistical approaches and machine learning to evaluate the effectiveness of zakat in supporting sustainable human development in Indonesia.
Copyright (c) 2026 Nazwa Hemalia Putri, Rizkco Fauzan Adhim, Saftana Fitri, Sifa Mutiasya Hendayana Puteri, Suci Ihtisabi Hida Nursyifa https://creativecommons.org/licenses/by/4.0
Islamic Chatbot Based on Reinforcement Learning Using Q-Learning Algorithm
This study develops a retrieval-based chatbot using a Reinforcement Learning (RL) approach with a Q-Learning algorithm combined with a Sentence-Transformer (SBERT) model to understand the semantic context of user questions. The system is designed to map questions into vector representations, calculate meaning similarity with training data, and select answers based on learned Q-values. The dataset used consists of question-answer pairs in JSON format. The training process is carried out in a question-and-answer simulation environment, where the RL agent is rewarded based on the suitability of the selected answer. Test results show that the chatbot is able to provide relevant and contextual responses even though the sentence structure differs from the training data. To improve accessibility, the system is packaged as a REST API using Flask and integrated into a Flutter-based mobile application as a user interface. This approach has proven to be efficient and computationally lightweight, and offers a promising alternative for developing responsive retrieval-based educational chatbots.
Copyright (c) 2026 Aria Octavian Hamza, Denis Firmansyah, Abidzar Giffari, Aisyah Muthmainnah, Adil Zukhruf Firdaus https://creativecommons.org/licenses/by/4.0
Thematic Grouping of Quranic Verse Translations Based on Word2Vec and K-Means Clustering
This study aims to group thematically translated texts of Indonesian Quranic verses using a Word2Vec-based machine learning approach and the KMeans Clustering algorithm. The process begins with text preprocessing, creating vector representations using Word2Vec, and then clustering using KMeans with quality evaluation using the Silhouette Score metric. The experimental results show that the model is able to form six main thematic clusters that semantically describe themes such as prayer and hope, moral evil, social law, the teachings of revelation, divinity, and the stories of figures and ethics. Two-dimensional visualization with PCA strengthens the interpretation of the formed clustering patterns. This study proves that the unsupervised learning approach can be relied upon to support the automation of digital thematic interpretation objectively and systematically. In addition, the results of this clustering have the potential to become the basis for the development of topic-based verse search systems, contextual Quranic learning applications, and technology-based exploration of Islamic studies. This study also supports the achievement of Sustainable Development Goals (SDGs) point 4 regarding increasing access to inclusive and quality education through information technology.
Copyright (c) 2026 Ahmad Badru Al Husaeni, Alif Firmansyah Putra, Adi Purnama, Adly Juliarta Lerian, Diman Fathurohman https://creativecommons.org/licenses/by/4.0
Automation of Halal Food Classification Using Bidirectional Long Short-term Memory on Ingredients List
The global demand for halal food products continues to increase, particularly among Muslim consumers, necessitating an efficient and accurate halal classification system. This study proposes a deep learning-based automatic classification approach using Bidirectional Long Short-Term Memory (BiLSTM) to determine the halal or haram status of a product based on its ingredient list. The system utilizes comprehensive text preprocessing techniques such as normalization, stopword removal, and dictionary-based term mapping. Word representations are converted into dense semantic vectors using word embeddings such as Word2Vec and GloVe. A BiLSTM model is used to capture bidirectional contextual relationships in ingredient sequences, thereby enhancing semantic understanding. Testing results on a dataset of 3,979 samples show that the proposed model achieves a classification accuracy of 99.75%, outperforming traditional machine learning methods such as Naive Bayes and SVM. The system is proven effective in handling ingredient ambiguity and context-based classification, and has potential for real-world applications such as mobile-based halal scanners. Future research can adopt attention mechanisms and transform-based models to improve performance and interpretability.
Copyright (c) 2026 Devi Mulyana, Dika Haekal Firza Pratama, Dailfhiana Fujianti, Dimas Arya Nurhakim, Daffa Rabbani https://creativecommons.org/licenses/by/4.0