cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Khairan Marzuki
Contact Email
upgrade.journal@universitasbumigora.ac.id
Phone
+6285933083240
Journal Mail Official
upgrade.journal@universitasbumigora.ac.id
Editorial Address
Universitas Bumigora Jl. Ismail Marzuki-Cilinaya-Cakranegara-Mataram 83127 Indonesia
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
UPGRADE: Jurnal Pendidikan Teknologi Informasi
Published by Universitas Bumigora
ISSN : -     EISSN : 30217067     DOI : https://doi.org/10.30812/upgrade
Core Subject : Science, Education,
Computer Science & IT Education Other
Jurnal Upgrade Pendidikan Teknologi Informasi menerima artikel riset dan kajian ilmiah (review) dengan lingkup ilmu pendidikan teknologi beserta aplikasinya. Adapun fokus dan ruang lingkup topik yang diterbitkan pada jurnal ini adalah sebagai berikut. 1. Teknologi Pendidikan 2. Kecerdasan Buatan & Aplikasi 3. Jaringan & Keamanan Komputer 4. Pengambilan Multimedia Berbasis Komputer 5. Sistem Pendukung Keputusan 6. Gudang Data & Penambangan Data 7. Sistem-E, Logika Fuzzy 8. Sistem Informasi Geografis (SIG) 9. Interaksi Manusia & Komputer 10. Pemrosesan Citra, Sistem Informasi 11. Mobile Computing & Application 12. Multimedia System 13. Neural Network 14. Pattern Recognition 15. Inovatif pengembangan multimedia pendidikan dan e-learning
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 55 Documents
 2   3   4   5   6 
PLS-SEM Analysis of Students’ AI Use Examining the Impact of Computational Thinking and Deep Learning Skills Ria, Reny Refitaningsih Peby; Anggeraini, Nining; Yuda, Lalu Setia; Awaliyah, Mia
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 3 No 2 (2026): FEBRUARI
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v3i2.6159

Abstract

The massive integration of generative artificial intelligence (AI) in higher education, particularly in scientific writing, calls for a deeper examination of the cognitive foundations underlying students’ AI use. Although prior research has predominantly focused on AI adoption and attitudes, limited attention has been devoted to modeling the cognitive skills that meaningfully shape AI engagement. Addressing this gap, the present study develops and empirically tests a structural model of students’ AI use by investigating the roles of Computational Thinking (CT) and Deep Learning Skills (DLS).A quantitative correlational research design was employed, involving 273 undergraduate students from three teacher education programs. Data were collected using a structured self-administered questionnaire and analyzed through Partial Least Squares–Structural Equation Modeling (PLS-SEM) to evaluate both the measurement model (reliability and validity) and the structural relationships among constructs. The results indicate that both CT and DLS significantly predict students’ AI use in academic writing, with DLS demonstrating a stronger structural effect. The proposed model explains a moderate proportion of variance in AI utilization, suggesting that higher-order cognitive and learning competencies function as central determinants of effective and responsible AI engagement. These findings contribute theoretically by positioning AI use not merely as a technological adoption issue but as a cognitively grounded learning process. The study further implies that higher education curricula should systematically integrate CT and DLS development to ensure that AI serves as a cognitive augmentation tool that strengthens academic integrity and learning quality.
Machine Learning-Based Network Traffic Anomaly Detection Using the CIC-IDS2017 Dataset Rozam, Nadhir Fachrul; Sari, Tika Novita; Yudianto, Muhammad Resa Arif; Rahman, Dzul Fadli
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 3 No 2 (2026): FEBRUARI
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v3i2.6174

Abstract

The increasing volume and diversity of traffic in modern networks demand more adaptive intrusion detection approaches than traditional signature-based methods. This study aims to evaluate and compare the performance of several machine learning algorithms in detecting multi-class network traffic anomalies using the  CIC-IDS2017 dataset. The research process includes data cleaning and transformation,  class imbalance handling through random undersampling, and the implementation of five classification models: Logistic Regression, Gaussian NaïveBayes, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Model performance is assessed using accuracy, precision, recall, and F1-score, supported by confusion matrix analysis and feature contribution evaluation. The results indicate that Random Forest achieves the best performance with an accuracy of 99.44% and consistently high evaluation metrics, while Gaussian Naïve Bayes shows the lowest performance. Furthermore, flow-based features are found to play a dominant role in improving classification accuracy, while misclassifications mainly occur among classes with similar traffic patterns. The findings highlight that selecting appropriate algorithms and applying effective preprocessing strategies are critical for developing more accurate and adaptive intrusion detection systems capable of addressing evolving cyber threats.
Pengaruh Media Video Scribe Berbasis Model Pembelajaran Group Investigation Terhadap Peningkatan Hasil Belajar di Sekolah Dasar Lestari, Ika Dwi; Nugraha, Jaka; Maulana, Reza
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 3 No 2 (2026): FEBRUARI
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v3i2.6165

Abstract

Critical thinking is one of the essential competencies that should be fostered in 21st-century education. However, its implementation at the elementary school level has not yet achieved optimal outcomes, partly because teacher-centered learning approaches remain widely practiced. Therefore, this study aimed to investigate the effect of integrating VideoScribe media with the Group Investigation learning model on the development of fifth-grade elementary students’ critical thinking skills.This study employed a quantitative quasi-experimental design with a non-equivalent control group. The sample consisted of 54 students divided into two groups: 27 students in the experimental class and 27 students in the control class. Data were collected through pretests and posttests and analyzed using the Wilcoxon and Mann–Whitney nonparametric tests. In addition, N-Gain analysis was conducted to measure students’ improvement levels. The findings revealed that the experimental group showed a slight significant improvement in critical thinking skills. The results indicate that integrating VideoScribe media with the Group Investigation model promotes more interactive and collaborative learning, thereby effectively supporting the development of critical thinking skills in elementary school students.
Integrasi Teknologi Informasi dalam Pembelajaran Seni sebagai Strategi Pembelajaran Abad ke-21 di SD Nugraha, Jaka; Risnawati, Eris; Maulana, Reza; Lestari, Ika Dwi
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 3 No 2 (2026): FEBRUARI
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v3i2.6166

Abstract

The rapid development of information technology has encouraged changes in art learning in elementary schools to become more creative, interactive, and aligned with 21st-century skills. However, its implementation still faces several challenges, including limited teachers’ digital competence, unequal availabilityof digital facilities, and low utilization of technology in art learning. This study aims to analyze the integrationof information technology in art learning as a 21st-century learning strategy in elementary schools. The studyemployed aq ualitative approach with a descriptive design through observation, interviews, and documentation.Data were analyzed through data reduction, data display, and conclusion drawing. The findings revealedthat the use of digital media, design applications, learning videos, and interactive platforms was able toimprove students’ creativity, collaboration, communication, and engagement in learning activities. Otherfindingsindicatedthatteachers’digitalcompetenceandlimiteddigitalfacilitiesremainedthemainobstaclesinImplementing technology-based art learning. Theoretically,this study strengthens the concept of constructivistand technology-based learning in supporting the holistic developmentof 21st-century skills among elementaryschool students.
Augmentasi Data Berbasis GAN dan Ekstraksi Fitur EfficientNetB0 dengan XGBoost untuk Meningkatkan Klasifikasi Penyakit Daun Jagung Anugerah Ikasatya, Ririn; Ikasatya, Ririn Anugerah; Apriliani, Cahya; Firdaus, Fathir Jannatul; Pratama, Gede Yogi
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 3 No 2 (2026): FEBRUARI
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v3i2.6224

Abstract

Corn leaf diseases are one of the main factors contributing to decreased corn productivity. Manual identification of leaf diseases remains subjective, time-consuming, and highly dependent on individual experience. This study aims to improve the performance of image-based corn leaf disease classification through the integration of data balancing techniques, deep feature extraction, and machine learning-based classification methods. The dataset consists of four classes with an imbalanced distribution, namely  Blight with 802 images, Common Rust with 914 images, Gray Leaf Spot with 401 images, and Healthy with 813 images, where GrayLeaf Spot represents the minority class. Data balancing is performed by generating synthetic images using  a convolution-based generative model to increase the number of samples in the minority class. Furthermore, feature extraction is carried out using the EfficientNetB0 architecture, and classification is performed using a gradient boosting-based algorithm. There sults show that the proposed approach improves accuracy from 92.49 percent to 93.29 percent and enhances the model’s ability to recognize the minority class, as indicated by an increase in recall from 69 percent to 78 percent and an improvement in performance balance from 0.76to 0.84. These findings indicate that the proposed method is effective in improving classification performance, particularly for the minority class, without reducing performance on majority classes.

Page 6 of 6 | Total Record : 55

 2   3   4   5   6 

Filter by Year

2023 2026


Reset
Filter By Issues
All Issue Vol 3 No 2 (2026): FEBRUARI Vol 2 No 2 (2025): Vol. 2 No. 2 Februari 2025 Vol 3 No 1 (2025): Agustus Vol 2 No 1 (2024): Vol. 2 No. 1 Agustus 2024 Vol 1 No 2 (2024): Vol.1 No. 2 Februari 2024 Vol 1 No 2 (2024): Volume 1 No 2 Februari 2024 Vol 1 No 1 (2023): Vol. 1 No. 1 Agustus 2023 Vol 1 No 1 (2023)