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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 5 Documents
 1 
Search results for , issue "Vol. 14 No. 2 (2025): June: Computer Science" : 5 Documents clear
Development of distance measurement accuracy technology in physical activity tracking applications with a reward point system Setiono, Oki; Nurjanah, Nurjanah; Mubarokah, Kismi; Haikal, Haikal; Iqbal, Muhammad
Vertex Vol. 14 No. 2 (2025): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/85rbrg31

Abstract

In the digital era, physical activity tracking applications have become increasingly popular as tools to monitor body health and encourage healthy habits. However, the accuracy of distance measurements used by many of these applications still faces challenges, especially in environments with GPS signal interference. This research aims to develop a system that integrates GPS technology and accelerometer sensors to improve the accuracy of distance measurements in physical activity tracking applications. The developed system was tested through a prototype to evaluate the effectiveness of combining these two technologies in improving measurement results. Additionally, this research also designs a database system for efficient physical activity data management, enabling real-time monitoring. To enhance user motivation, a reward point system was applied as a gamification element to encourage further engagement in physical activities. The results of this research show that the combined use of GPS and accelerometers was able to improve measurement accuracy, with errors ranging from 2.4% to 4.2%, depending on the type of activity performed. Walking activities demonstrated higher accuracy compared to running. The reward point system was also proven to be effective in motivating users to be more active. This research provides an important contribution to the development of more accurate, efficient health applications that can improve both physical and mental well-being
Implementation of the Topsis Method in Determining Online Shopping Options in the Marketplace Tuslaela, Tuslaela; Alawiah, Enok Tuti; Apriyani, Helina
Vertex Vol. 14 No. 2 (2025): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ckpd8y89

Abstract

The development of information technology changes the way customers view shopping. Currently, customers are more likely to make online shopping transactions through the marketplace. Significantly increased internet penetration, ease of transactions, seller reputation, speed of service, ease of access are factors that support customers in making shopping transactions in the marketplace. However, customers need to decide wisely before making a shopping transaction so that the products obtained are in accordance with expectations. This study uses the TOPSIS method, a method in the decision-making process to choose an ideal solution based on the criteria offered. The results of the study obtained a result of 0.85 for product reviews as an alternative preference in shopping online through the marketplace.
Implementation of UI/UX Design in the Development of Profile Website Using the User Centered Design Method Harianty, Savanda; Wahyuni, Sri; Arista, Ruly Dwi
Vertex Vol. 14 No. 2 (2025): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/14gh9964

Abstract

The evolution of UI/UX design on the profile webpage is covered in this study. A community called Omah Cikal Kreatif aims to empower housewives by fostering their creativity via handicrafts made from secondhand items.  The primary obstacle is the limitations of digital platforms' user interface (UI) design and user experience (UX), which affect user engagement and low exposure. This research aims to design and develop an attractive, informative and easy-to-use website by applying the UCD approach. The development process includes user needs analysis, prototype design, and design evaluation using the System Usability Scale (SUS) method. The results of this research provide strategic insights in improving the quality of website design and strengthening Omah Cikal Kreatif's identity in the digital realm
Anomaly Detection of Parasitic Plankton in Brebes Eco-Waters Using Vision-Based Autoencoder AI Gunawan, Gunawan; Andriani, Wresti; Maryanto, Sesilia Putri; Mustaqiim, Restu Abi
Vertex Vol. 14 No. 2 (2025): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/75mwxm55

Abstract

The escalating impact of environmental stress on coastal ecosystems necessitates reliable, scalable tools for monitoring marine biodiversity. This study proposes an unsupervised anomaly detection framework to identify parasitic and morphologically abnormal plankton in the waters of Brebes, Indonesia. The primary aim is to develop an interpretable, vision-based system capable of detecting visual anomalies without relying on labeled anomaly data. The research integrates convolutional autoencoders for reconstructing normal plankton images, Principal Component Analysis (PCA) for feature extraction, and One-Class Support Vector Machines (OC-SVM) for classification. Monthly microscopic images were obtained from selected mangrove and aquaculture pond sites in Brebes, Central Java, using portable digital microscopy under standardized field conditions. Images that exceeded a dynamic reconstruction threshold were flagged as anomalous and validated by marine biology experts. The system achieved an F1-score of 86.1%, a precision of 85.3%, and an AUC of 0.94, demonstrating high effectiveness in distinguishing between normal and anomalous plankton. With an average inference time of 0.37 seconds per image, the system supports near real-time monitoring. These results confirm the potential of the proposed method as a low-latency, field-deployable solution for aquatic ecosystem surveillance. By integrating AI-based detection with ecological expert validation, this research offers a scalable approach for marine biodiversity assessment and establishes a foundation for future adaptive environmental monitoring systems.
Obesity risk estimation using ensemble learning and synthetic data augmentation techniques Ujianto, Nur Tulus; Gunawan, Gunawan; Andriani, Wresti; Ramadhani, Ivan Rizky; Nasichatun, Nasichatun
Vertex Vol. 14 No. 2 (2025): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/1bg4ws75

Abstract

Obesity has become a primary global health concern due to its strong association with various chronic diseases such as diabetes, cardiovascular disorders, and certain types of cancer. Accurate and early risk prediction of obesity is essential for effective prevention and intervention strategies. However, predictive modeling in this domain often encounters two critical challenges: the presence of imbalanced datasets and the complex, nonlinear nature of behavioral and anthropometric features. This study aims to address these challenges by developing a robust classification model that integrates ensemble learning with synthetic data augmentation techniques. The research utilizes the Obesity Dataset from Kaggle, which comprises 2,111 records labeled into seven obesity levels, reflecting a realistic class distribution imbalance. Preprocessing steps included data cleaning, encoding, and stratified splitting. To enhance class representation, two augmentation methods were applied: SMOTE for synthetic oversampling and Generative Adversarial Networks (GANs) for generating realistic minority samples. A stacking ensemble model was constructed using Random Forest and XGBoost as base learners, with Logistic Regression serving as the meta-learner. Hyperparameter optimization was conducted using both grid and randomized search methods. Evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess performance. The proposed model achieved a 91% accuracy and an F1-score of 0.89, significantly outperforming models from previous studies. These findings suggest that combining ensemble learning with hybrid augmentation strategies effectively addresses class imbalance and improves predictive reliability in obesity risk estimation. The developed model holds practical value as a decision-support tool for early screening and targeted intervention in obesity prevention programs.

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