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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 4 Documents
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Search results for , issue "vol. 15 no. 2 (2026): june: computer science" : 4 Documents clear
IMDb Movie Rating Prediction Using a Random Forest Classification Approach Rifqy Rosyidah Ilmi
Vertex Vol. 15 No. 2 (2026): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/25rz6180

Abstract

Accurate movie rating prediction is essential for supporting audience preferences and analytical decision-making in the digital film industry. The availability of large-scale metadata from IMDb provides valuable opportunities for applying machine learning techniques to analyze rating patterns. This study investigates the effectiveness of a Random Forest classification model for predicting IMDb movie rating categories based on structured attributes, including genre, movie duration, content rating, actor popularity, and user review statistics. Data preprocessing involved handling missing values, removing duplicates, encoding categorical variables, normalizing numerical features, and partitioning the dataset into training and testing subsets. To mitigate class imbalance among rating categories, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. Experimental evaluation demonstrates that the proposed model achieves an overall accuracy of 0.78, accompanied by balanced precision, recall, and F1-score values across all classes. Confusion matrix analysis shows that classification errors predominantly occur between neighboring rating categories, reflecting the inherent subjectivity of movie ratings. Furthermore, feature importance analysis highlights genre, duration, content rating, and user engagement indicators as the most influential predictors. These results indicate that Random Forest offers a robust and interpretable baseline model for IMDb rating prediction and provides meaningful insights for future movie analytics and recommendation research.  
Analyzing Consumer Purchasing Behavior in Electrical Supply Stores Using Association Rules Yunas Akbar; Tiwuk Wahyuli Prihandayani; Novianti Madhona Faizah; Luky Fabrianto; Ryan Rakryan
Vertex Vol. 15 No. 2 (2026): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/8ed7jg14

Abstract

This study aims to improve inventory management and to analyze consumer purchasing behavior through data exploration and the application of association rule mining. The dataset used in this research consists of sales transaction records of electrical products collected over a one-year period. Due to the wide variety of items sold, product categorization is conducted to support more effective analysis and interpretation of purchasing patterns. The method applied in this study is association rule mining using the Apriori algorithm. This method is employed to discover relationships and co-occurrence patterns among items in transaction data. The minimum thresholds used in this study are support ≥ 10% and confidence ≥ 30%, ensuring that only significant and reliable association rules are generated. The results of the analysis reveal several important patterns, with the strongest rule identified as: “Lakban, Switch, and Socket → Cable,” which has a confidence value of 46%. This indicates that customers who purchase Lakban, switches, and sockets have a 46% likelihood of also purchasing cables. The findings provide insights into customer purchasing behavior that can be utilized to optimize inventory control, improve product arrangement, and develop effective cross-selling strategies. Furthermore, this study demonstrates that the application of association rule mining can support data-driven decision-making, enhance operational efficiency, and contribute to increased sales performance and customer satisfaction
Simulation Of Rectangular Patch Microstrip Antenna For Wimax At 3.5 GHz Frequency Muhammad Iqbal; Aprima A Matondang; Stephanie Pardede; The Fitri Astarani; Morlan Pardede
Vertex Vol. 15 No. 2 (2026): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/vmprwd02

Abstract

Microstrip antenna is one of the compact antenna types that can be implemented using PCB material and consists of three main parts, namely the radiating element, substrate, and ground plane. This antenna is widely used in wireless communication systems because it has a simple structure, small size, and is easy to integrate with electronic devices. One of its applications is in WiMAX systems operating at 3.5 GHz to support more efficient data transmission. In this study, a rectangular patch microstrip antenna was designed as an alternative antenna for WiMAX systems. The design process was carried out using AWR software to obtain antenna characteristics that meet the required specifications. The evaluated performance parameters included return loss, VSWR, and bandwidth. Based on the simulation results, the designed antenna achieved a return loss of -14.21 dB, a VSWR of 1.484, and a bandwidth of 152 MHz. After optimization, the final design achieved an improved return loss of -26.93 dB and a VSWR of 1.09. These results indicate that the proposed antenna has good performance and is suitable for supporting WiMAX communication systems
Unsupervised Machine Learning Based DSS for Land Profiling and Disease Risk Mitigation in Smart Farming Embun Fajar Wati; Elvi Sunita; Andi Diah Kuswanto
Vertex Vol. 15 No. 2 (2026): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/na9y0b02

Abstract

Decision Support Systems (DSS) in smart farming require methodologies capable of representing the holistic complexity of agricultural ecosystems. This study proposes a DSS framework based on unsupervised machine learning, specifically K-Means clustering, to automatically segment land profiles using IoT sensor records. The dataset consists of 500 global sensor data points covering seven essential environmental variables: soil moisture, pH, temperature, rainfall, humidity, sunlight duration, and the NDVI index. Through Principal Component Analysis (PCA) for dimensionality reduction and Silhouette Score evaluation, the system successfully identified and mapped seven land profiles with distinct microclimatic characteristics. Cross-tabulation analysis further demonstrates the principal novelty of this DSS, namely its ability to classify land into "Safe Zones" (Clusters 0, 3, and 4), which are characterized by Mild disease status and are suitable for Soybean, Cotton, and Maize, as well as "High-Risk Zones" (Clusters 1, 2, 5, and 6), which consistently correspond to Severe disease status. These findings indicate that a DSS based on environmental clustering is substantially more effective for crop selection recommendations and disease prevention than conventional predictive approaches. Ultimately, this framework provides farmers with actionable insights to optimize productivity and minimize agricultural risk

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