Khazanah Journal of Religion and Technology
The Khazanah Journal of Religion and Technology is dedicated to advancing the understanding of the complex relationship between religion and technology. The journal aims to serve as a platform for publishing original research that explores the intersection of these two domains, focusing on recent and contemporary media and technology. The journal welcomes empirical research that investigates how religious ideas and practices are communicated, studied, represented, enforced, and countered through various technological means. This includes but is not limited to the examination of religion in films, social media, games, websites, applications, and television. We invite researchers to contribute studies that shed light on the diverse aspects of the interaction between religion and technology. Topics of interest include, but are not limited to: The role of technology in religious communication: Exploring how religious communities and individuals utilize technology to disseminate religious messages, engage with believers, and foster virtual religious experiences. Digital religious practices and rituals: Investigating the emergence and impact of online religious practices, virtual religious communities, and digital rituals. Religion and social media: Examining the influence of social media platforms on religious discourse, religious identity formation, and religious movements. Religious representation in media and popular culture: Analyzing the portrayal of religion, religious figures, and religious narratives in films, television shows, video games, and other forms of media. Ethical implications of religious technology: Addressing ethical considerations and challenges arising from the integration of technology into religious practices, such as data privacy, digital surveillance, and the preservation of religious authenticity. Technological innovations in religious institutions: Investigating how religious institutions adopt and adapt to new technologies, including the development of religious websites, applications, virtual reality experiences, and interactive installations. While the journal encourages research from diverse religious traditions, literary genres, and geographic areas, the emphasis remains on contemporary and recent phenomena in the realm of religion and technology. Theological writings, however, fall outside the scope of the journal and are not typically accepted for publication. The Khazanah Journal of Religion and Technology seeks to foster interdisciplinary scholarship, encouraging contributions from researchers in fields such as religious studies, media studies, communication studies, sociology, anthropology, psychology, and computer science. The journal aims to contribute to the social scientific conversation and promote a nuanced understanding of the dynamic relationship between religion and technology in today digital age.
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The Digital Sacred: A Phenomenology of Religious Experience in Algorithmic Worlds
Khatami, Mahmoud
Khazanah Journal of Religion and Technology Vol. 3 No. 2 (2025): December
Publisher : UIN Sunan Gunung Djati Bandung
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DOI: 10.15575/kjrt.v3i2.1323
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.
XGBoost and Mixed Effect Model: Can Zakat (Alms) Improve the Human Development Index in Indonesia?
Putri, Nazwa Hemalia;
Rizkco Fauzan Adhim;
Fitri, Saftana;
Puteri, Sifa Mutiasya Hendayana;
Nursyifa, Suci Ihtisabi Hida
Khazanah Journal of Religion and Technology Vol. 3 No. 2 (2025): December
Publisher : UIN Sunan Gunung Djati Bandung
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DOI: 10.15575/kjrt.v3i2.1539
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.
Islamic Chatbot Based on Reinforcement Learning Using Q-Learning Algorithm
Hamza, Aria Octavian;
Denis Firmansyah;
Abidzar Giffari;
Aisyah Muthmainnah;
Adil Zukhruf Firdaus
Khazanah Journal of Religion and Technology Vol. 3 No. 2 (2025): December
Publisher : UIN Sunan Gunung Djati Bandung
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DOI: 10.15575/kjrt.v3i2.1731
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.
Thematic Grouping of Quranic Verse Translations Based on Word2Vec and K-Means Clustering
Al Husaeni, Ahmad Badru;
Putra, Alif Firmansyah;
Purnama, Adi;
Lerian, Adly Juliarta;
Fathurohman, Diman
Khazanah Journal of Religion and Technology Vol. 3 No. 2 (2025): December
Publisher : UIN Sunan Gunung Djati Bandung
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DOI: 10.15575/kjrt.v3i2.1748
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.
Automation of Halal Food Classification Using Bidirectional Long Short-term Memory on Ingredients List
Mulyana, Devi;
Pratama, Dika;
Fujianti, Dailfhiana;
Nurhakim, Dimas;
Rabbani, Daffa
Khazanah Journal of Religion and Technology Vol. 3 No. 2 (2025): December
Publisher : UIN Sunan Gunung Djati Bandung
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DOI: 10.15575/kjrt.v3i2.1774
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.