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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Bulletin of Network Engineer and Informatics (BUFNETS)
Agung Wibowo, Mochamad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Mapping Bitcoin Research in Information Systems: A Comprehensive Bibliometric Analysis (2008–2025) Munazilin, Akhlis; Agung Wibowo, Mochamad; Parlika, Rizky
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2538

Abstract

Bitcoin has been a major focus of interdisciplinary research in information systems, finance, and economics since its emergence in 2008. Despite the extensive literature on Bitcoin, patterns of intellectual collaboration, the evolution of research themes, and research gaps have not been comprehensively mapped. This study presents a bibliometric analysis of 3,312 scientific articles indexed by Scopus from 2008 to May 2025, using a quantitative approach based on Bibliometrix. The analysis includes publication trends, author and institutional collaboration networks, co-citation mapping, and thematic clusters based on keywords. The results reveal five dominant themes: (1) blockchain development beyond crypto, (2) regulatory challenges and global adoption, (3) Bitcoin price volatility, (4) impacts on the global financial system, and (5) social implications in developing countries. The study also identifies an epistemological fragmentation between technical and policy approaches. These findings reinforce the need for an integrated multimodal approach that combines market data, sentiment analysis, and regulatory context to develop more robust predictive models. This study is the first comprehensive bibliometric review of Bitcoin in global scope that explicitly links findings to information systems research opportunities.
Comparative Analysis of Seven Machine Learning Algorithms for Morphology-Based Classification of Cammeo and Osmancik Rice Varieties Kalua, Aditya; Agung Wibowo, Mochamad; Alexander Latumakulita, Luther; Widsli Kalengkongan, Wisard; Ijon Turnip, Rama
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April - September 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/scp7n107

Abstract

Accurate varietal identification of rice grains is crucial for quality assessment and data-driven decision-making in agricultural informatics. This study aims to comparatively eval-uate seven machine learning algorithms for morphology-based classification of Cammeo and Osmancik rice varieties and to identify the most suitable model for structured numerical grain-feature data. Using a dataset of 3,810 instances with seven image-derived morpho-logical features, a systematic comparison was conducted across Logistic Regression, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree, Random Forest, Naive Bayes, and k-Nearest Neighbors. The models were evaluated based on classification quality and computational efficiency. Results show that MLP achieved the highest overall predictive performance with an accuracy of 93.03% and an F1-score of 94.17%. However, when balancing accuracy against computational overhead, SVM emerged as the optimal” sweet spot” for industrial implementation, offering a competitive 92.50% accuracy with a 93-fold reduction in execution time compared to MLP. Naive Bayes demonstrated the fastest computational runtime (0.0022 seconds total). The study identifies a distinct trade-off between predictive quality and runtime efficiency, recommending MLP for high-fidelity research and SVM for real-time agricultural informatics applications.
Co-Authors Akhlis Munazilin, Akhlis Alexander Latumakulita, Luther Ijon Turnip, Rama Kalua, Aditya Parlika, Rizky Wisard Widsli Kalengkongan