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Vertex
Published by Institute of Computer Science
ISSN : 2089385X     EISSN : 28296761     DOI : https://doi.org/10.35335/Vertex
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Articles published in Vertex include original scientific research results (top priority), new scientific review articles (non-priority), or comments or criticisms on scientific papers published by Vertex. The journal accepts manuscripts or articles in the field of engineering from various academics and researchers both nationally and internationally. The journal is published every June and December (2 times a year). Articles published in Vertex are those that have been reviewed by Peer-Reviewers. The decision to accept a scientific article in this journal is the right of the Board of Editors based on recommendations from the Peer-Reviewers. Since 2011, Vertex only accepts articles derived from original research (top priority), and new scientific review articles (non-priority).
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol. 15 No. 1 (2025): December: Computer Science" : 2 Documents clear
Analysis of the Technology Acceptance Model (TAM) in the Acceptance of Smartboards in Elementary Schools Aria, Ririn Restu; Apriyani, Helina; Alawiah, Enok Tuti
Vertex Vol. 15 No. 1 (2025): December: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/f8fpy424

Abstract

The use of Smartboards in primary school learning has become an essential part of digital transformation in Indonesian education. However, the success of this implementation strongly depends on user acceptance. This study aims to analyze the factors influencing Smartboard acceptance using the Technology Acceptance Model (TAM), which includes Perceived Usefulness (PU), Attitude Toward Using (ATU), and Intention to Use (IU). A quantitative explanatory correlational design was employed, involving primary school teachers in West Jakarta who have used Smartboards in classroom instruction. Data were analyzed using Structural Equation Modeling (SEM). The findings indicate that all indicators of PU, ATU, and IU demonstrate valid and reliable loading factor values. Structurally, PU has a positive and significant effect on IU, and ATU also shows a significant positive influence on IU. These results highlight that perceived usefulness and positive attitudes are the primary determinants shaping teachers’ intention to use Smartboard technology. The study implies that enhancing digital competencies, providing structured Smartboard training, and ensuring technical support are crucial to optimizing the implementation of Smartboard-based learning
Enhancing Rice Disease Identification using Hybrid GLCM-XGBoost with SMOTE Imbalance Handling Sadad, Anwar
Vertex Vol. 15 No. 1 (2025): December: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/z9q64978

Abstract

Rice (Oryza sativa ) is a major food staple, which is prone to multiple diseases that will dramatically decrease the harvest yield. Disease identification is time consuming and is usually subject to subjective errors in a manual approach. The following research will seek to increase the level of precision of automatic rice plant disease detection, namely the Brown Spot, Hispa, and Leaf Blast classes. The suggested method combines both the Gray Level Co-occurrence Matrix (GLCM) to extract texture features and the Extreme Gradient Boosting (XGBoost) classification algorithm. Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to address class imbalance within the dataset of 5,548 images. Preprocessing steps include resizing, grayscale conversion, and Min-Max normalization. Experimental results demonstrate that the model trained on SMOTE-balanced data with optimized XGBoost parameters achieved a superior accuracy of 98%, outperforming the imbalanced scenario (97%) and previous studies. This research confirms that the combination of GLCM, SMOTE, and XGBoost constitutes a robust and high-precision method for rice disease identification

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