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All Journal JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Islamic Khazanah Journal of Islamic Economics and Business
Ichsan Budiman
UIN Sunan Gunung Djati Bandung, 40614, Indonesia
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Public Health Social Sciences

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Exploring Classification Algorithms for Detecting Learning Loss in Islamic Religious Education: A Comparative Study Sapdi, Rohmat Mulyana; Maylawati, Dian Sa'adillah; Ramdania, Diena Rauda; Budiman, Ichsan; Al-Amin, Muhammad Insan; Fuadi, Mi'raj
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1823

Abstract

This study investigates the detection of learning loss in Islamic religious education subjects in Indonesia. Focusing on the effectiveness of multiple classification algorithms, the research assesses learning loss across literacy, numeracy, writing, and science domains. While education traditionally involves knowledge transmission, it also seeks to instill values. Given Indonesia's predominantly Islamic demographic, Islamic Religious Education (IRE) is pivotal in disseminating moral and cultural values, encompassing teachings from the Koran, Hadith, Aqedah, morality, Fiqh, and Islamic history. The study's central aim is to discern learning loss in IRE in Islamic schools, utilizing the Gradient Boosting Classifier as its primary analytical tool. Various classification algorithms, including the Cat Boost Classifier, Light Gradient Boosting Machine, Extreme Gradient Boosting, and others, were tested. The study engaged a sample of 38,326 Islamic Elementary school students, 29,350 Islamic Junior High school students, and 13,474 Islamic High school students across Indonesia. The findings revealed that the Light Gradient Boosting Machine was the most effective model for Islamic Elementary and High school data, while the Cat Boost Classifier excelled for Islamic Junior High school data. These results highlight the extent of learning loss in IRE and offer invaluable perspectives for education stakeholders. Future studies are encouraged to further explore the root causes of this learning loss and devise specific interventions to tackle these issues effectively.
Analyzing PEGASUS Model Performance with ROUGE on Indonesian News Summarization Kartamanah, Fatih Fauzan; Atmadja, Aldy Rialdy; Budiman, Ichsan
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14303

Abstract

Text summarization technology has been rapidly advancing, playing a vital role in improving information accessibility and reducing reading time within Natural Language Processing (NLP) research. There are two primary approaches to text summarization: extractive and abstractive. Extractive methods focus on selecting key sentences or phrases directly from the source text, while abstractive summarization generates new sentences that capture the essence of the content. Abstractive summarization, although more flexible, poses greater challenges in maintaining coherence and contextual relevance due to its complexity. This study aims to enhance automated abstractive summarization for Indonesian-language online news articles by employing the PEGASUS (Pre-training with Extracted Gap-sentences Sequences for Abstractive Summarization) model, which leverages an encoder-decoder architecture optimized for summarization tasks. The dataset utilized consists of 193,883 articles from Liputan6, a prominent Indonesian news platform. The model was fine-tuned and evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric, focusing on F-1 scores for ROUGE-1, ROUGE-2, and ROUGE-L. The results demonstrated the model's ability to generate coherent and informative summaries, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.439, 0.183, and 0.406, respectively. These findings underscore the potential of the PEGASUS model in addressing the challenges of abstractive summarization for low-resource languages like Indonesian language, offering a significant contribution to summarization quality for online news content.
Building Sustainable Competitive Advantage Through Innovation in Sharia Financial Institutions: Bibliometric and Literature Review Sopian, Adi; Dirgantari, Puspo Dewi; Adi Wibowo, Lili; Budiman, Ichsan; Rohmat, Siti
Journal of Islamic Economics and Business Vol. 5 No. 2 (2025): Journal of Islamic Economics and Business
Publisher : Fakultas Ekonomi dan Bisnis Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/jieb.v5i2.44994

Abstract

Innovation in Islamic Financial Institutions that are experiencing developments aims to make LKS competitive, sustainable and able to meet the needs of the community. For that, it is necessary to study innovation in Islamic financial institutions, because with innovation the company can survive. The research question is who are the authors who have contributed to the research, the development of research theme trends and future research trends regarding innovation in Islamic financial institutions. The novelty in this study is a research roadmap in the field of innovation in Islamic financial institutions. The method used in this study is bibliometric analysis by selecting data from reputable international sources and using the Scopus database, RStudio and Vosviewer tools. The findings in this study indicate that Muneeza, A. and Hassan, M.K. are the most influential authors in Islamic finance research dominated by issues of Islamic finance, banking, fintech, and zakat, with promising future research trends on the topics of digitalization of Islamic finance, productive waqf, and economic sustainability. The increasing number of research in the field of Islamic Financial Institution Innovation shows that this research theme is still very relevant in future research.
Optimizing Quranic Literacy with the Tamam Method: Leveraging Artificial Intelligence for Handwritten Arabic Recognition Azmiana, Gina Giftia; Ramdania, Diena Rauda; Budiman, Ichsan; Harika, Maisevli
International Journal of Islamic Khazanah Vol. 15 No. 2 (2025): IJIK
Publisher : UIN Sunan Gunung Djati Bandung

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

Abstract

Indonesia, boasting the world's largest Muslim population, has witnessed a swift augmentation in its Muslim demographic. As of 2020, Muslims in Indonesia numbered 209 million, which surged to 219 million in 2021. Such an observation is alarming, especially given the Quran's centrality in Islamic teachings and the profound link between grasping its tenets and the capability to read and write its verses. This paper introduces an innovative application employing the Tamam method, optimized for enhancing Quranic literacy through the recognition of handwritten Arabic texts using Convolutional Neural Networks (CNN). Involving a cohort of 144 participants, who answered 65 questions, a dataset encompassing 3,842 data points was curated for testing and validation. Preliminary results showcased the model's evolution, with a notable rise in accuracy from 14.27% in the initial epoch to 88.87% in the 20th epoch. Despite such advancements, fluctuations in the validation data hinted at potential overfitting scenarios. This study demonstrates the feasibility of integrating the Tamam method with AI-based handwritten Arabic recognition as a supportive tool for Quranic writing practice. It paves the way for more resilient and adaptive Quranic educational tools, ensuring learners grasp the Holy Text in its true essence.
Co-Authors Adi Sopian Aldy Rialdy Atmadja Diena Rauda Ramdania Gina Giftia Azmiana, Gina Giftia Kartamanah, Fatih Fauzan Lili Adi Wibowo Maisevli Harika Maylawati, Dian Sa'adillah Mi’raj Fuadi Muhammad Insan Al-Amin Puspo Dewi Dirgantari Rohmat Mulyana Sapdi Rohmat, Siti