Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
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All Journal Innovation and Applied Education Journal Artificial Intelligence in Educational Decision Sciences Journal of Digital Technology and Computer Science
Risha Febrianti
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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Analisis Pengetahuan dan Keterampilan Mahasiswa terhadap Internet of Things dalam Metaverse Sri Irmayani; Risha Febrianti; Fitria Nur Dina Salam; Jannah, Devi Miftahul
Innovation and Applied Education Journal Volume 1, Issue 3, Oktober 2024
Publisher : PT. Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/iaej.v1i3.242

Abstract

Dalam menghadapi dinamika era digital yang terus berkembang, penelitian ini bersifat mendalam terhadap keterampilan mahasiswa terkait integrasi Internet of Things (IoT) dalam lingkungan Metaverse. Konteks permasalahan muncul dari kebutuhan untuk mengevaluasi pemahaman dan keterampilan mahasiswa yang menjadi kunci dalam memahami perubahan paradigma teknologi. Tujuan penelitian adalah untuk menganalisis sejauh mana mahasiswa memahami dan memiliki keterampilan terkait IoT dalam konteks Metaverse. Pendekatan kuantitatif digunakan dengan menggunakan kuesioner sebagai instrumen penelitian, dan sampel penelitian diambil dari populasi mahasiswa Universitas Negeri Makassar. Analisis data dilakukan dengan teknik analisis statistik yang sesuai. Hasil penelitian memberikan gambaran variabilitas tingkat keterampilan, dengan sebagian mahasiswa menunjukkan kemahiran yang tinggi, sementara sebagian lainnya memerlukan pengembangan lebih lanjut.
Predicting Student Dependency on ChatGPT for Academic Tasks Using Naive Bayes Classification Risha Febrianti; Sul Fitriana; Asrafah; Stephen Amukune
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.23

Abstract

Purpose – This study aims to predict and classify the level of student dependency on ChatGPT in completing academic tasks using the Naive Bayes algorithm to support data-driven decision making in higher education.Methods – A quantitative survey approach was employed involving 254 active undergraduate students from the Department of Informatics and Computer Engineering at a public university in Indonesia. Data were collected through a Likert-scale questionnaire measuring five behavioral indicators: purpose of ChatGPT use, interaction frequency and duration, understanding of generated outputs, trust in AI responses, and learning independence. The collected data were cleaned, numerically encoded, and labeled into three dependency categories (low, medium, high). A Naive Bayes classification model was implemented using Orange Data Mining and evaluated under three data split scenarios: 90:10, 80:20, and 70:30.Findings – The results indicate that the 70:30 data split achieved the highest classification performance, with an AUC value of 0.973, accuracy of 85.3%, F1-score of 0.866, and precision of 0.909. These results demonstrate that the Naive Bayes algorithm is effective in identifying distinct patterns of student dependency on ChatGPT based on multidimensional behavioral data.Research limitations – This study is limited to a single academic program and relies on self-reported questionnaire data, which may constrain the generalizability of the findings across different educational contexts.Originality – This study provides empirical evidence on the application of probabilistic classification models to assess student dependency on generative AI, contributing to educational decision sciences by informing institutional policies on balanced and responsible AI use in higher education.
Analyze the Interactive Biography System of the President of Indonesia: Portfolio Yudha Nurfaiz; Khilma Zulfani Shobiyyah; Risha Febrianti; Dzaky Raihan Muharram
Journal of Digital Technology and Computer Science Vol. 2 No. 2 (2025): Mei 2025
Publisher : Academic Bright Collaboration

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

Abstract

Interactive biography systems have become an important tool in introducing and understanding important figures in history. This article aims to analyze and describe an interactive biography system that focuses on Indonesian presidents. By utilizing various media, including text and images, the system offers an immersive learning experience and interacts with information about Indonesian leaders. Our analysis covers content structure, user interface, and interactivity, as well as their impact on people's understanding of history and leadership. By exploring the various features and technologies used in this system, this article provides an in-depth insight into how information technology can enrich historical learning and promote a better understanding of the role of leaders in the formation of the country. The conclusions of this analysis can provide valuable guidance for the development and improvement of future interactive biography systems.
Co-Authors Asrafah Dzaky Raihan Muharram Fitria Nur Dina Salam Jannah, Devi Miftahul Khilma Zulfani Shobiyyah Sri Irmayani Stephen Amukune Sul Fitriana Yudha Nurfaiz