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This Author published in this journals
All Journal Journal of Applied Data Sciences Journal of Current Research in Blockchain
Priyo Agung Prastyo
Magister of Computer Science, Universitas Amikom Purwokerto, Jawa Tengah, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Evaluating Blockchain Adoption in Indonesia's Supply Chain Management Sector Satrya Fajri Pratama; Priyo Agung Prastyo
Journal of Current Research in Blockchain Vol. 1 No. 3 (2024): Regular Issue December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i3.21

Abstract

This research evaluated the adoption of blockchain technology in the supply chain management sector, focusing on the factors that influence the intention to use blockchain, including perceived usefulness, security, facilitating conditions, cost, regulatory support, and trust. Data were collected through a cross-sectional survey distributed to 315 individuals actively involved in supply chain management, of which 309 valid responses were obtained after a validation process that included screening questions such as prior use of blockchain technology. The study employed structural equation modeling (SEM) for data analysis. The findings highlighted that trust played a significant mediating role between perceived usefulness, security, and intention to use blockchain technology. Perceived usefulness and security were found to significantly enhance trust, which in turn positively influenced the intention to adopt blockchain. Regulatory support also had a strong positive impact on adoption intentions, underscoring the importance of clear and supportive regulatory frameworks. Cost was identified as a barrier to adoption, reflecting the need for organizations to address financial concerns associated with blockchain implementation. The results contributed to the theoretical understanding of blockchain adoption by integrating trust as a key mediator in the Technology Acceptance Model and offered practical implications for supply chain management professionals and policymakers.
Sentiment Analysis on Slang Enriched Texts Using Machine Learning Approaches Priyo Agung Prastyo; Berlilana Berlilana; Imam Tahyudin
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.626

Abstract

This study explores sentiment analysis of slang-enriched user reviews using machine learning techniques, specifically Naive Bayes, Support Vector Machine (SVM), and Random Forest, to classify user sentiment into Positive, Negative, and Neutral categories while addressing challenges posed by informal and conversational language through slang normalization. A lexicon-based scoring method was employed to standardize slang terms such as “gak,” “aja,” and “banget,” ensuring consistency in sentiment analysis. The results indicate that Neutral sentiment dominates the dataset (51%), followed by Negative (28%) and Positive (21%), with lexicon-based scores confirming this distribution. Negative sentiment exhibits a broader intensity range, reflecting user dissatisfaction primarily related to network quality, service reliability, and pricing, as evident from recurring terms like “sinyal” (signal), “jaringan” (network), and “mahal” (expensive). Word cloud visualizations reinforce these findings, highlighting the prevalence of these concerns in user feedback. Performance evaluation of the machine learning models reveals that SVM and Random Forest achieved the highest accuracy (96%), significantly outperforming Naive Bayes (73%), demonstrating their effectiveness in handling high-dimensional text data and accurately classifying slang-rich content. These findings underscore the importance of slang normalization in preprocessing, as it significantly enhances sentiment classification accuracy. This study provides actionable insights for service providers, helping them identify and address key sources of user dissatisfaction. Future research can explore deep learning models such as BERT and LSTM to further enhance sentiment analysis by capturing contextual relationships within text data, while topic modeling techniques could uncover deeper thematic patterns in user feedback, enabling data-driven strategies to improve customer satisfaction.
Co-Authors Berlilana Berlilana Imam Tahyudin Satrya Fajri Pratama