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Telcomatics
Published by Universitas Internasional Batam
ISSN : -     EISSN : 25415867     DOI : http://dx.doi.org/10.37253/telcomatics.v5i1.838
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Telcomatics is a peer reviewed Journal in English or Bahasa Indonesia published two issues per year (June and December). The aim of Telcomatics is to publish articles dedicated to all aspects of the latest outstanding developments in the field of Electrical Engineering and Information System. Telcomatics Journal welcomes full research articles in the following engineering subject areas: Telecommunication and Information Technology Applied Computing and Computer Instrumentation and Control Electronic Computer Security Computer Network Image Processing Mechatronic and Robotic Network Traffic Modeling Game Technology Intelligent System
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 10 No. 2 (2025)" : 5 Documents clear
Analisis Pengaruh Seo dan Iklan Online Terhadap Keputusan Pembelian Konsumen Kerry, Winson John; Elisa, Lilis; Hoverio, Auron Rafael; Dalon, Dalon; Jaya, Oki
Telcomatics Vol. 10 No. 2 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i2.11310

Abstract

The use of Search Engine Optimization (SEO) technology and online advertising as a marketing medium shows how marketing strategies have shifted in the digital era. Although both are widely used, the effectiveness of this strategy on purchasing decisions is not fully clear yet. This study tries to find out how much influence SEO and online advertising have on online purchasing decisions. The method used is a quantitative approach with a survey technique by distributing questionnaires to 400 students of Universitas Internasional Batam (UIB) class of 2023–2024. Data analysis was carried out using the Structural Equation Modeling (SEM) method using SmartPLS 3.2.9 software. The results of the study show that both SEO and online advertising have a real effect on online purchasing decisions. SEO contributes to increasing visibility and trust in a product, while online advertising helps catch the consumer attention and buying interest. Hopefully, this research can be a helpful guide for for business actors in developing effective digital marketing strategies, as well as increasing public understanding of the importance of the role of SEO and online advertising in influencing purchasing decisions in the digital era.
Product Quality Classification Based on Machine Learning in the Quality Control System of the Laser Metal Deposition Process Elok Fiola; Rahma Neliyana; Try Yani Rizki Nur Rohmah; M. Syamsuddin Wisnubroto; Fajri Farid
Telcomatics Vol. 10 No. 2 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i2.11432

Abstract

Industry 4.0 revolutionizes modern manufacturing by enabling the active integration of smart sensors and machine learning to optimize product quality control systems. This research focuses on classifying product quality in the Laser Metal Deposition (LMD) process by applying three machine learning algorithms, namely Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). The dataset consists of four numerical sensor variables, including Optical Sensor, Laser Power, Pressure, and Temperature, with Defect Label as the binary target variable. The Synthetic Minority Oversampling Technique (SMOTE) is used to balance the class distribution. Correlation analysis reveals weak linear relationships among all variables, suggesting the presence of complex non-linear interactions. The Random Forest model produces the best performance with accuracy of 0.88, recall of 0.79, and AUC of 0.80, outperforming Decision Tree and SVM. These findings indicate that ensemble-based methods effectively capture complex patterns within sensor data and offer reliable predictions for quality control in metal manufacturing industries, particularly within Laser Metal Deposition processes.
Perancangan dan Pengembangan Sistem Smart Parking Berbasis ESP32 dan Aplikasi Mobile Android Studio Erick; Yuvier, Maxi; Fernando, Nelson; Marcelleno, Ng; Yulianto, Andik
Telcomatics Vol. 10 No. 2 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i2.10867

Abstract

The rapid growth in the number of vehicles has intensified challenges in parking management, leading to traffic congestion and wasted time for drivers searching for available spots. Conventional parking systems, which often rely on manual processes, are inefficient and contribute to these problems. This research addresses these issues by designing and developing an Internet of Things (IoT) based smart parking system to provide real-time monitoring of parking availability. The system utilizes an ESP32 microcontroller, chosen for its integrated Wi-Fi and processing capabilities, as the core processing unit. For vehicle and user management, an RFID-RC522 sensor is used for vehicle entry authentication , while an HC-SR04 ultrasonic sensor detects vehicles at the exit. The backend architecture employs a multi-layered approach, using MQTT as the communication protocol with HiveMQ as the broker. System logic and data flows are managed using Node-RED, which communicates with a Flask server to interact with the database. The user interface is a mobile application developed using Android Studio. This application requires user authentication via a login page and provides a real-time dashboard that visually displays the status of parking slots—green for available and red for occupied. The successful implementation of this prototype demonstrates a functional end-to-end solution that effectively integrates hardware and software to create a more efficient and user-friendly parking management system.
Random Forest Classifier Approach for Accurate Malicious URL Identification Haeruddin, Haeruddin; Elvert; Yulianto, Andik; Sabariman, Sabariman
Telcomatics Vol. 10 No. 2 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i2.11173

Abstract

Internet users currently face significant risks from malicious URLs that facilitate phishing attacks, malware distribution, and data theft. Traditional blacklisting methods have become ineffective against evolving cyberattack techniques. This study proposes a Random Forest classification approach for more accurate malicious URL detection, focusing on critical URL features including URL length, presence of special keywords, subdomain structure, and special character usage. these features train the Random Forest model to distinguish between safe and malicious URLs. We evaluate model effectiveness using accuracy, precision, and recall metrics. This research aims to develop a Random Forest-based malicious URL detection system that is more accurate and adaptive than conventional methods. The study examines both the advantages and limitations of this approach, along with its potential as a reliable detection solution for dynamic digital environments. Evaluation results demonstrate an overall accuracy of 94%, weighted average F1-score of 0.94, and macro average F1-score of 0.94.
Analisis Sentimen Review Aplikasi Chat GPT dengan Memanfaatkan Algoritma Support Vector Machine Prabadaru, Alit Damar; Zahrawati, Ashifa; Jibran, Muhammad Agmal; Nurulita, Salsabila; Dewana, Nadya Cantika Apriani
Telcomatics Vol. 10 No. 2 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i2.11759

Abstract

This study analyses user sentiment toward the ChatGPT application based on reviews collected from the Google Play Store. The goal of this research is to classify user opinions into positive and negative categories using the Support Vector Machine (SVM) algorithm. The dataset was obtained through web scraping and processed using several text preprocessing steps, including case folding, tokenization, stopword removal, and stemming. The TF-IDF method was applied to convert the text into numerical feature vectors suitable for machine learning models. A linear SVM model was used to perform sentiment classification due to its effectiveness in handling high-dimensional text data. The results of the evaluation show that the linear SVM provides stable and accurate performance when identifying sentiment in user reviews. The findings also indicate that TF-IDF features contribute significantly to improving model accuracy. Overall, this research concludes that SVM is a suitable and reliable method for sentiment analysis of application reviews. The outcomes can help developers understand user perceptions and improve the quality of the ChatGPT application based on the insights obtained

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