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All Journal Knowledge Engineering and Data Science Jurnal Teknologi Dan Sistem Informasi Bisnis JASL - Journal of Applied Studies in Language
Nugroho, I Made Riyan Adi
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Potential of spherical virtual-based video reality (SVVR) through smartphone in learning Indonesian in the vocational education system Rahmanu, I Wayan Dian Eka; Laksana, I Putu Yoga; Adnyana, Ida Bagus Artha; Sutarma, I Gusti Putu; Somawati, Ni Putu; Nugroho, I Made Riyan Adi
Journal of Applied Studies in Language Vol. 6 No. 2 (2022): Dec. 2022
Publisher : Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/jasl.v6i2.643

Abstract

In the lens of technology used in classroom instruction, the 360-video virtual reality begins to use in the education field. This study aimed to identify the Indonesian language students in the higher education level perceptions of the use of SVVR (Spherical Virtual-based Video Reality) which was operated through the smartphone. The performance expectancy (PE) and effort expectancy (EE) in the UTAUT theory was employed to assess the data. There were 131 sophomores who learn the Indonesian language in the tourism department in the State Polytechnic of Bali involved to provide specific arguments through the questionnaire. The result of the study explained that learners in the Polytechnic education system were keen to engage SVVR (Spherical Virtual-Video Based Reality) during the teaching and learning process. Additionally, this learning medium elevated students’ urge significantly in learning the Indonesian language. Subsequently, the SVVR will be a promising tool employed by the lecturer who teaches the learners in the applied education system. These learning materials ought to be explored and developed more systematically by lecturers in underpinning students’ demand for knowledge and materials understanding in vocational education.
Analisis Performa Komparatif Algoritma Machine Learning untuk Deteksi Fraud dalam Transaksi Blockchain Apriyanthi, Ni Putu Eka; Dhewanty, Civica Moehaimin; Ayu, Putu Desiana Wulaning; Nugroho, I Made Riyan Adi; Wijaya, I Wayan Rizky
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2285

Abstract

The decentralized finance (DeFi) and blockchain environment encounters substantial security threats, particularly complex and expensive fraudulent activities. Conventional detection methods frequently prove insufficient when dealing with enormous transaction volumes and datasets characterized by unbalanced class distributions. This research seeks to examine and evaluate the effectiveness of three widely used machine learning techniques Logistic Regression, Random Forest, and XGBoost in identifying fraudulent activities within blockchain transactions. The investigation utilized an Ethereum transaction dataset sourced from Kaggle, where the imbalanced data distribution was addressed through the application of SMOTE methodology. Performance assessment was carried out using precision, recall, F1-score, and ROC-AUC measurements on testing data. The findings demonstrate XGBoost's superiority among the algorithms, delivering an accuracy rate of 99.46%, precision of 99.69%, recall of 97.86%, and ROC-AUC score of 99.97%, while maintaining minimal false positive occurrences (only 1 instance). These results exceeded those achieved by both Random Forest and Logistic Regression models, demonstrating that gradient boosting methodologies excel at detecting intricate fraudulent behaviors. The study's outcomes offer significant contributions toward creating resilient and autonomous fraud detection frameworks. Keywords: Blockchain, Fraud, Machine Learning, Logistic Regression, Random Forest, XGBoost.
Query Rewriting with Thesaurus-Based for Handling Semantic Heterogeneity in Database Integration Nugroho, I Made Riyan Adi; Sentana, I Wayan Budi
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Nowadays, studies on handling semantic heterogeneity still become a challenge for researcher. Several methods have been used to solve these problems, one of which is query rewriting, implemented by rewriting a query into the latest one by using the selected schema. Semantic query rewriting needs a framework in order to identify the connection through the data schema sources. This line is used as a basis for scheme selection. Also, ontology is a model which often be used in these specific cases. The lack of ontology becomes a significant problem that usually seen. Therefore, this paper will describe an alternative framework in order to identify the link of semantic, which assisted by thesaurus.
Co-Authors Civica Moehaimin Dhewanty I Gusti Putu Sutarma, I Gusti Putu I Putu Yoga Laksana I Wayan Budi Sentana, I Wayan Budi I Wayan Rizky Wijaya Ida Bagus Artha Adnyana Ni Putu Eka Apriyanthi Putu Desiana Wulaning Ayu Rahmanu, I Wayan Dian Eka Somawati, Ni Putu