Khazanah Journal of Religion and Technology
type: channel
description: 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. 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.
publisher: Asosiasi Khazanah Cendekia
language: en
publication name: Khazanah Journal of Religion and Technology
issn: 2987-6060
items: The Digital Sacred: A Phenomenology of Religious Experience in Algorithmic Worlds
items: XGBoost and Mixed Effect Model: Can Zakat (Alms) Improve the Human Development Index in Indonesia?
items: Islamic Chatbot Based on Reinforcement Learning Using Q-Learning Algorithm
items: Thematic Grouping of Quranic Verse Translations Based on Word2Vec and K-Means Clustering
items: Automation of Halal Food Classification Using Bidirectional Long Short-term Memory on Ingredients List
items: CRNN Algorithm and MFCC Feature Extraction in Classifying Hijaiyah Letter Pronunciation: A Systematic Literature Review
items: Shaping Identities, Shaping Futures: The Socio-Religious Impact of Digital Culture on Nigerian Youth
items: Sharia Stock Investment Decision Making Using the Deep Recurrent Q-Network Model
items: Finding Important Patterns of Sadaqah and Waqf Transactions in the Eid Charity Global Donation using Frequent Pattern Growth
items: Systematic Literature Review: Deep Learning Models in Arabic Script Classification
items: Leveraging Artificial Intelligence Technologies in the Service of the Holy Quran and Its Sciences
items: IndoT5 (Text-to-Text Transfer Transformer) Algorithm for Paraphrasing Indonesian Language Islamic Sermon Manuscripts
items: Text-to-Speech Technology Development Using FastSpeech2 Algorithm for the Story of the Prophet
items: Slang Spelling Detection in Indonesian Religious Content Using Sequence-to-Sequence Algorithm
items: Sentiment Analysis of Marketplace Review with Islamic Perspective using Fine-Tuning DistilBERT
items: Optimizing Qur'an Recitation Monitoring with Random Forest Algorithm
items: Digital Transformation: Digitising Witness Statements in South African Policing in Alignment with Religious Principles
items: Classifying Hijaiyah Letters Handwritten Detection of Children Using CNN Algorithm
items: SoulScripture: Chatbot using Bidirectional Encoder Representations from Transformers as a Medium of Spiritual Guidance
items: The Cultural Relationship between Christianity and Technology for the Practice of Christian Mission
items: Religious and Ecclesiastical Media in the Hungarian Electronic Media Ecosystem
items: Deux ex Machina (“ God From the Machine”): Exploring Digital Worship in the Salvation Ministries, Port Harcourt, Nigeria
items: Prediction of Skin Diseases using Convolutional Neural Networks as an Effort to Prevent Their Spread in Islamic Boarding School Environments
items: A Determining the Law of Reading Tajweed (Idgham Qomariyah and Syamsiyah) in the Qur'an Using the Naïve Bayes Algorithm
items: Convolutional Neural Network (CNN) for Detecting Al-Qur'an Reciting and Memorizing
items: The Influence of Transformational Education Prediction on Softskills of Madrasa Student using Data Mining
items: Comparison of the Fisher-Yates Shuffle and the Linear Congruent Algorithm for Randomizing Questions in Nahwu Learning Multimedia
items: Welfare Classification of Muslim Majority Communities using Decision Tree Algorithm
items: The Influence of the Use of Social Media on the Intensity of Worshipping the Millenial Generation using Linear Regression
items: Maintaining Religious Harmony through Predicting the Level of Lawlessness using Linear Regression

The Digital Sacred: A Phenomenology of Religious Experience in Algorithmic Worlds
type: item
description: 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.
subject: Articles
  topic: section
subject: AI hermeneutics
  topic: keywords
subject: algorithmic spirituality
  topic: keywords
subject: digital religion
  topic: keywords
subject: digital sacred
  topic: keywords
subject: techno-theology
  topic: keywords
subject: virtual religious experience
  topic: keywords
creator: Mahmoud Khatami
rights: Copyright (c) 2026 Mahmoud Khatami https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 45
ending page: 55

XGBoost and Mixed Effect Model: Can Zakat (Alms) Improve the Human Development Index in Indonesia?
type: item
description: 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.
subject: Articles
  topic: section
subject: Human Development Index (HDI)
  topic: keywords
subject: Mixed Effect Model
  topic: keywords
subject: panel data
  topic: keywords
subject: sustainable development
  topic: keywords
subject: XGBoost
  topic: keywords
subject: zakat
  topic: keywords
creator: Nazwa Hemalia Putri
creator: Rizkco Fauzan Adhim
creator: Saftana Fitri
creator: Sifa Mutiasya Hendayana Puteri
creator: Suci Ihtisabi Hida Nursyifa
rights: 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
date: 2026-03-04
publication date: 2026-03-04
starting page: 56
ending page: 63

Islamic Chatbot Based on Reinforcement Learning Using Q-Learning Algorithm
type: item
description: 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.
subject: Articles
  topic: section
subject: Chatbot
  topic: keywords
subject: Q-Learning
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: sentence-transformers
  topic: keywords
creator: Aria Octavian Hamza
creator: Denis Firmansyah
creator: Abidzar Giffari
creator: Aisyah Muthmainnah
creator: Adil Zukhruf Firdaus
rights: Copyright (c) 2026 Aria Octavian Hamza, Denis Firmansyah, Abidzar Giffari, Aisyah Muthmainnah, Adil Zukhruf Firdaus https://creativecommons.org/licenses/by/4.0
date: 2026-03-08
publication date: 2026-03-08
starting page: 71
ending page: 76

Thematic Grouping of Quranic Verse Translations Based on Word2Vec and K-Means Clustering
type: item
description: 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.
subject: Articles
  topic: section
subject: Al-Qur'an
  topic: keywords
subject: Digital Interpretation
  topic: keywords
subject: K-Means Clustering
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Thematic Clustering
  topic: keywords
subject: Word2Vec
  topic: keywords
creator: Ahmad Badru Al Husaeni
creator: Alif Firmansyah Putra
creator: Adi Purnama
creator: Adly Juliarta Lerian
creator: Diman Fathurohman
rights: Copyright (c) 2026 Ahmad Badru Al Husaeni, Alif Firmansyah Putra, Adi Purnama, Adly Juliarta Lerian, Diman Fathurohman https://creativecommons.org/licenses/by/4.0
date: 2026-03-07
publication date: 2026-03-07
starting page: 64
ending page: 70

Automation of Halal Food Classification Using Bidirectional Long Short-term Memory on Ingredients List
type: item
description: 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.
subject: Articles
  topic: section
subject: BiLSTM
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: food ingredient list
  topic: keywords
subject: halal classification
  topic: keywords
subject: halal food detection
  topic: keywords
subject: NLP
  topic: keywords
subject: word embedding
  topic: keywords
creator: Devi Mulyana
creator: Dika Pratama
creator: Dailfhiana Fujianti
creator: Dimas Nurhakim
creator: Daffa Rabbani
rights: Copyright (c) 2026 Devi Mulyana, Dika Haekal Firza Pratama, Dailfhiana Fujianti, Dimas Arya Nurhakim, Daffa Rabbani https://creativecommons.org/licenses/by/4.0
date: 2026-03-01
publication date: 2026-03-01
starting page: 36
ending page: 44

CRNN Algorithm and MFCC Feature Extraction in Classifying Hijaiyah Letter Pronunciation: A Systematic Literature Review
type: item
description: 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.
subject: Articles
  topic: section
subject: Audio Classification
  topic: keywords
subject: Al-Qur'an literacy
  topic: keywords
subject: CRNN
  topic: keywords
subject: Hijaiyah Letters
  topic: keywords
subject: MFCC
  topic: keywords
subject: Voice Recognition
  topic: keywords
creator: Mohammad Putra Fauzan Fatah
rights: Copyright (c) 2025 Mohammad Putra Fauzan Fatah https://creativecommons.org/licenses/by/4.0
date: 2025-07-17
publication date: 2025-07-17
starting page: 31
ending page: 35

Shaping Identities, Shaping Futures: The Socio-Religious Impact of Digital Culture on Nigerian Youth
type: item
description: 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.
subject: Articles
  topic: section
subject: Cultural Hybridity
  topic: keywords
subject: Digital Identity
  topic: keywords
subject: Nigeria
  topic: keywords
subject: Online Communities
  topic: keywords
subject: Religious Expression
  topic: keywords
subject: Youth Agency
  topic: keywords
creator: Tonbrapade Japheth
creator: Ndidi Justice Gbule
creator: Christopher Opara
rights: Copyright (c) 2025 Tonbrapade Japheth, Ndidi Justice Gbule, Christopher Opara https://creativecommons.org/licenses/by/4.0
date: 2025-07-17
publication date: 2025-07-17
starting page: 9
ending page: 17

Sharia Stock Investment Decision Making Using the Deep Recurrent Q-Network Model
type: item
description: 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.
subject: Articles
  topic: section
subject: Deep Reinforcement Learning
  topic: keywords
subject: DRQN
  topic: keywords
subject: Islamic Finance
  topic: keywords
subject: Islamic Stocks
  topic: keywords
subject: Reward Function
  topic: keywords
subject: Trading
  topic: keywords
creator: Jasmein Al-baar Putri Rus'an
creator: Muhammad Zidan
creator: Muhammad Arkan Raihan
creator: Marleni Sukarya
creator: Sendi Ahmad Rafiudin
rights: 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
date: 2025-07-17
publication date: 2025-07-17
starting page: 23
ending page: 30

Finding Important Patterns of Sadaqah and Waqf Transactions in the Eid Charity Global Donation using Frequent Pattern Growth
type: item
description: 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.
subject: Articles
  topic: section
subject: Association Rule Mining
  topic: keywords
subject: Data Science
  topic: keywords
subject: FP-Growth
  topic: keywords
subject: Sadaqah
  topic: keywords
subject: Waqf
  topic: keywords
creator: Rafif Sunu Fauzi
creator: Sabilah Arianisa
creator: Viqriza Ahmad Vahira
creator: Mohamed
rights: Copyright (c) 2025 Rafif Sunu Fauzi, Sabilah Arianisa, Viqriza Ahmad Vahira, Mohamed https://creativecommons.org/licenses/by/4.0
date: 2025-07-16
publication date: 2025-07-16
starting page: 1
ending page: 8

Systematic Literature Review: Deep Learning Models in Arabic Script Classification
type: item
description: 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.
subject: Articles
  topic: section
subject: AI-generated art
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Image Classification
  topic: keywords
subject: Islamic Calligraphy
  topic: keywords
creator: Muhamad Khansa Khalifaturohman
rights: Copyright (c) 2025 Muhamad Khansa Khalifaturohman https://creativecommons.org/licenses/by/4.0
date: 2025-07-16
publication date: 2025-07-16
starting page: 18
ending page: 22

Leveraging Artificial Intelligence Technologies in the Service of the Holy Quran and Its Sciences
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Holy Qur’an
  topic: keywords
subject: Islamic Sciences
  topic: keywords
creator: Basheer Adnan
rights: Copyright (c) 2025 Basheer Adnan https://creativecommons.org/licenses/by/4.0
date: 2025-01-06
publication date: 2025-01-06
starting page: 36
ending page: 44

IndoT5 (Text-to-Text Transfer Transformer) Algorithm for Paraphrasing Indonesian Language Islamic Sermon Manuscripts
type: item
description: 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.
subject: Articles
  topic: section
subject: IndoT5
  topic: keywords
subject: Text-to-Text Transfer Transformer
  topic: keywords
subject: Sistem Parafrase
  topic: keywords
subject: Pemrosesan Bahasa Alami
  topic: keywords
creator: Winda Puspitasari
creator: Dani Ramdani
creator: Aditya Muhamad Maulana
rights: Copyright (c) 2025 Winda Puspitasari, Dani Ramdani, Aditya Muhamad Maulana https://creativecommons.org/licenses/by/4.0
date: 2025-01-07
publication date: 2025-01-07
starting page: 63
ending page: 73

Text-to-Speech Technology Development Using FastSpeech2 Algorithm for the Story of the Prophet
type: item
description: 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.
subject: Articles
  topic: section
creator: Muhammad Raihan Firdaus
creator: Muhammad Rihap Firdaus
creator: Pancadrya Yashoda Pasha
rights: Copyright (c) 2025 Muhammad Raihan Firdaus, Muhammad Rihap Firdaus, Pancadrya Yashoda Pasha https://creativecommons.org/licenses/by/4.0
date: 2025-01-06
publication date: 2025-01-06
starting page: 55
ending page: 62

Slang Spelling Detection in Indonesian Religious Content Using Sequence-to-Sequence Algorithm
type: item
description: 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.
subject: Articles
  topic: section
subject: Religious Content
  topic: keywords
subject: Sequence-to-Sequence
  topic: keywords
subject: Slang
  topic: keywords
subject: Spelling Detection
  topic: keywords
creator: Nanda Tiara Sabina Hidayatulloh
creator: Nur Halizah
creator: Pitriani
rights: Copyright (c) 2025 Nanda Tiara Sabina Hidayatulloh, Nur Halizah, Pitriani https://creativecommons.org/licenses/by/4.0
date: 2025-01-08
publication date: 2025-01-08
starting page: 74
ending page: 79

Sentiment Analysis of Marketplace Review with Islamic Perspective using Fine-Tuning DistilBERT
type: item
description: 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.
subject: Articles
  topic: section
subject: DistilBERT
  topic: keywords
subject: e-Commerce
  topic: keywords
subject: Islamic Perspective
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: Shopee
  topic: keywords
subject: Tokopedia
  topic: keywords
creator: Reza Fahlevi Reza
creator: Muhmmad Thoriq
creator: Rd. Imam Saepul Millah
rights: Copyright (c) 2025 Reza Fahlevi Reza, Muhmmad Thoriq, Rd. Imam Saepul Millah https://creativecommons.org/licenses/by/4.0
date: 2025-01-06
publication date: 2025-01-06
starting page: 45
ending page: 54

Optimizing Qur'an Recitation Monitoring with Random Forest Algorithm
type: item
description: 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%.
subject: Articles
  topic: section
subject: Classification
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Reciting Al-Qur’an
  topic: keywords
creator: Ivan Wijayana
creator: Muhammad Ardiansyah
rights: Copyright (c) 2024 Ivan Wijayana, Muhammad Ardiansyah https://creativecommons.org/licenses/by/4.0
date: 2024-06-20
publication date: 2024-06-20
starting page: 1
ending page: 5

Digital Transformation: Digitising Witness Statements in South African Policing in Alignment with Religious Principles
type: item
description: 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.
subject: Articles
  topic: section
subject: Digitisation
  topic: keywords
subject: Performance
  topic: keywords
subject: Policing
  topic: keywords
subject: Religious Principles
  topic: keywords
subject: Witness Statements
  topic: keywords
creator: Nomsa Ingrid Zikalala
creator: Jacob Tseko Mofokeng
creator: Moses Morero Motseki
creator: Enoch Zenzile
rights: Copyright (c) 2024 Nomsa Ingrid Zikalala, Jacob Tseko Mofokeng, Moses Morero Motseki, Enoch Zenzile https://creativecommons.org/licenses/by/4.0
date: 2024-07-10
publication date: 2024-07-10
starting page: 6
ending page: 17

Classifying Hijaiyah Letters Handwritten Detection of Children Using CNN Algorithm
type: item
description: 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.
subject: Articles
  topic: section
subject: Children's Handwriting
  topic: keywords
subject: Classification
  topic: keywords
subject: CNN
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Hijaiyah Letters
  topic: keywords
creator: Ahmad Maulidi Roofiad
creator: Annisa Sabillah
creator: Aprian Nur Rohman
creator: Elmi Wahyu Triyani
rights: Copyright (c) 2024 Ahmad Maulidi Roofiad, Annisa Sabillah, Aprian Nur Rohman, Elmi Wahyu Triyani https://creativecommons.org/licenses/by/4.0
date: 2024-08-07
publication date: 2024-08-07
starting page: 18
ending page: 22

SoulScripture: Chatbot using Bidirectional Encoder Representations from Transformers as a Medium of Spiritual Guidance
type: item
description: 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.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: BERT
  topic: keywords
subject: mental health
  topic: keywords
subject: self-supervised learning
  topic: keywords
subject: transformer
  topic: keywords
subject: Quran
  topic: keywords
subject: Hadith
  topic: keywords
creator: Andhika Malik
creator: Andhika Putra Gefadri
creator: Elman Sidik
creator: Alika Putie Syadrina
rights: Copyright (c) 2024 Andhika Malik, Andhika Putra Gefadri, Elman Sidik, Alika Putie Syadrina https://creativecommons.org/licenses/by/4.0
date: 2024-08-15
publication date: 2024-08-15
starting page: 23
ending page: 27

The Cultural Relationship between Christianity and Technology for the Practice of Christian Mission
type: item
description: 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.
subject: Articles
  topic: section
subject: Christianity
  topic: keywords
subject: Christian Mission
  topic: keywords
subject: Philosophy of Technology
  topic: keywords
subject: Public Theology
  topic: keywords
subject: Technology
  topic: keywords
creator: Min Hyoung Lee
rights: Copyright (c) 2024 Min Hyoung Lee https://creativecommons.org/licenses/by/4.0
date: 2024-08-16
publication date: 2024-08-16
starting page: 28
ending page: 35

Religious and Ecclesiastical Media in the Hungarian Electronic Media Ecosystem
type: item
description: 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.
subject: Articles
  topic: section
subject: content
  topic: keywords
subject: ecclesiastical media
  topic: keywords
subject: Hungary
  topic: keywords
subject: media law
  topic: keywords
subject: media economy
  topic: keywords
subject: religious
  topic: keywords
creator: András Radetzky
rights: Copyright (c) 2023 András Radetzky https://creativecommons.org/licenses/by/4.0
date: 2023-11-24
publication date: 2023-11-24
starting page: 33
ending page: 39

Deux ex Machina (“ God From the Machine”): Exploring Digital Worship in the Salvation Ministries, Port Harcourt, Nigeria
type: item
description: 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.
subject: Articles
  topic: section
subject: Deus ex Machina
  topic: keywords
subject: Digitalization
  topic: keywords
subject: Digital Media
  topic: keywords
subject: God from Machine
  topic: keywords
subject: Pentecostalis
  topic: keywords
subject: Religious Experience
  topic: keywords
subject: Salvation Ministries
  topic: keywords
creator: Christopher Onyewuchi Opara
creator: Ndidi Justice Gbule
creator: Rowland Olumati
rights: Copyright (c) 2023 Christopher Onyewuchi Opara, Ndidi Justice Gbule, Rowland Olumati https://creativecommons.org/licenses/by/4.0
date: 2023-11-24
publication date: 2023-11-24
starting page: 26
ending page: 32

Prediction of Skin Diseases using Convolutional Neural Networks as an Effort to Prevent Their Spread in Islamic Boarding School Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: convolutional neural network
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: skin disease
  topic: keywords
subject: vgg-16
  topic: keywords
creator: Ilham Rizky Agustin
creator: Muhammad Bayu Nurdiansyah Putra
rights: Copyright (c) 2023 Ilham Rizky Agustin, Muhammad Bayu Nurdiansyah Putra https://creativecommons.org/licenses/by/4.0
date: 2023-12-25
publication date: 2023-12-25
starting page: 49
ending page: 53

A Determining the Law of Reading Tajweed (Idgham Qomariyah and Syamsiyah) in the Qur'an Using the Naïve Bayes Algorithm
type: item
description: 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%.
subject: Articles
  topic: section
subject: Naive Bayes
  topic: keywords
subject: Tajweed
  topic: keywords
subject: Qur'an
  topic: keywords
creator: Haikal Azhar
creator: Ibham Bathsyi Hizbullah
rights: Copyright (c) 2023 Haikal Azhar, Ibham Bathsyi Hizbullah https://creativecommons.org/licenses/by/4.0
date: 2023-12-25
publication date: 2023-12-25
starting page: 40
ending page: 43

Convolutional Neural Network (CNN) for Detecting Al-Qur'an Reciting and Memorizing
type: item
description: 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.
subject: Articles
  topic: section
subject: classification
  topic: keywords
subject: convolutional neural network
  topic: keywords
subject: memorizing
  topic: keywords
subject: reciting
  topic: keywords
subject: Qur'an
  topic: keywords
creator: Rizal Hadiyansah
creator: Rafi Andamira
rights: Copyright (c) 2023 Rizal Hadiyansah, Rafi Andamira https://creativecommons.org/licenses/by/4.0
date: 2023-12-25
publication date: 2023-12-25
starting page: 44
ending page: 48

The Influence of Transformational Education Prediction on Softskills of Madrasa Student using Data Mining
type: item
description: 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%.
subject: Articles
  topic: section
subject: data mining
  topic: keywords
subject: education
  topic: keywords
subject: k-nearest neigbor
  topic: keywords
subject: madrasa
  topic: keywords
subject: naive bayes
  topic: keywords
subject: soft skills
  topic: keywords
creator: Andin Aprila Sari
creator: Annisa Sugi Pramesty
creator: Asma Zulfiah Malik
creator: Dini Nurul Haq Al-Hidayah
creator: Laela Shintia Alviani
creator: Rifka Alia Assyifa
rights: 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
date: 2023-06-01
publication date: 2023-06-01
starting page: 20
ending page: 25

Comparison of the Fisher-Yates Shuffle and the Linear Congruent Algorithm for Randomizing Questions in Nahwu Learning Multimedia
type: item
description: 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.
subject: Articles
  topic: section
subject: fisher yates shuffle
  topic: keywords
subject: learning multimedia
  topic: keywords
subject: nahwu
  topic: keywords
subject: linear congruent method
  topic: keywords
creator: Ukan Saokani
creator: Mohamad Irfan
creator: Dian Sa'adillah Maylawati
creator: Rachmat Jaenal Abidin
creator: Ichsan Taufik
creator: Riyan Naufal Hay's
rights: 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
date: 2023-06-01
publication date: 2023-06-01
starting page: 10
ending page: 14

Welfare Classification of Muslim Majority Communities using Decision Tree Algorithm
type: item
description: 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.
subject: Articles
  topic: section
subject: decision tree algorithm
  topic: keywords
subject: muslim communities
  topic: keywords
subject: welfare classification
  topic: keywords
creator: Naufal Berlian
creator: Andi Malia Fadilah Bahari
creator: Jidan Ramdani
creator: Rico Candra
creator: Intan Permata Sari
creator: Maulana Aditya
rights: 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
date: 2023-06-01
publication date: 2023-06-01
starting page: 1
ending page: 4

The Influence of the Use of Social Media on the Intensity of Worshipping the Millenial Generation using Linear Regression
type: item
description: 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.
subject: Articles
  topic: section
subject: linear regression
  topic: keywords
subject: millenial
  topic: keywords
subject: social media
  topic: keywords
subject: worship
  topic: keywords
creator: Fakhri Faisal Rochdiana
creator: Muhammad Rizki Isa Darmawan
creator: Nurul Aulia Dewi
creator: Wafda Zulfa Husna
creator: Zulfa Dwi Audina
creator: Nunik Destria Arianti
rights: 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
date: 2023-06-01
publication date: 2023-06-01
starting page: 15
ending page: 19

Maintaining Religious Harmony through Predicting the Level of Lawlessness using Linear Regression
type: item
description: 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.
subject: Articles
  topic: section
subject: forcasting
  topic: keywords
subject: lawlessness prediction
  topic: keywords
subject: linear regression
  topic: keywords
subject: religious harmony
  topic: keywords
creator: Adam Wardana
creator: Dekis Aldamawan
creator: M. Rival Ridautal Lillah
creator: Mochamad Anwar Wirahadi
creator: Mohamad Rasyid Ridho
creator: Naufal Rizqullah
rights: 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
date: 2023-06-01
publication date: 2023-06-01
starting page: 5
ending page: 9