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International Journal of Electronics and Communications Systems
Published by Universitas Islam Negeri Raden Intan Lampung
ISSN : -     EISSN : 27982610     DOI : 10.24042
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electronics and Communications System (IJECS) [e-ISSN: 2798-2610] is a medium communication for researchers, academicians, and practitioners from all over the world that covers issues such as the improvement about design and implementation of electronics devices, circuits, and communication systems including but not limited to: circuit theory, integrated circuits, analog circuits, digital circuits, mixed-signal circuits, electronic components, filters, oscillators, biomedical circuits, neuromorphic circuits, RF circuits, optical communication systems, microwave systems, antenna systems, communications circuits for optical communication, development of physics evaluation instruments, development of physics instructional media, digital signal processing, communication theory and techniques, modulation, source and channel coding, microwave theory and techniques, wave propagation and more.
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 9 Documents
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Search results for , issue "Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System" : 9 Documents clear
Exploration of Data Handling Techniques to Improve PM2.5 Prediction Using Machine Learning Unik, Mitra; Sitanggang, Imas Sukaesih; Syaufina, Lailan; Jaya, I Nengah Surati
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.25687

Abstract

Particulate matter (PM₂.₅) is one of the most dangerous air pollutants because it can penetrate the respiratory system and cause serious health problems. Amidst the limitations of a real-time and comprehensive air quality monitoring system, a data-driven predictive approach is needed that can accurately project PM₂.₅ concentrations. This study aims to develop a PM₂ concentration prediction model using the Random Forest Regressor (RFR) algorithm optimised through a series of data pre-processing techniques. The pre-processing techniques include outlier detection with four methods (Isolation Forest, Autoencoder ANN, OCSVM, IQR) and missing value handling using three approaches (Spline Cubic Interpolation, Nearest Point Interpolation, Data Removal). The daily data used covered 12 environmental variables (including rainfall, temperature, relative humidity, AOD, and NDVI) from the period of March 2022 to March 2023, with PM₂.₅ as the target. The RFR model was built with 100 decision trees and 10-fold cross-validation to improve accuracy. Results showed the combination of IQR (outlier detection) and data deletion (missing values) produced the best performance with RMSE 0.082, MAE 0.027, and R² 0.886. The most influential variables were temperature (TEMP), relative humidity (RHU), and evapotranspiration (ET). This research contributes to the development of an accurate air quality prediction model, supporting the mitigation of PM₂.₅ pollution impacts on public health
Optimization of Stock Price Prediction Using Long Short-Term Memory (LSTM) Algorithm and Cross-Industry Standard Process Approach for Data Mining (CRISP-DM) Saepulrohman, Asep; Chairunnas, Andi; Denih, Asep; Safitri Yasibang, Nurdiana Dini
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.26727

Abstract

Predicting stock prices accurately is an integral part of investment analysis as it permits forecasting movements in the financial markets and tailoring strategies accordingly. In this study, the LSTM (Long Short-Term Memory) algorithm is used with the aim of improving predictive accuracy, particularly the forecasting of stock price movements. This research follows the CRISP-DM framework or Cross-Industry Standard Process for Data Mining, which incorporates six defined steps including: understanding the business context, data understanding, data preparation, model building, evaluation, and implementation. Stock price data for the ticker symbol “ANTM.JK” was sourced from Yahoo Finance for the date range of October 29, 2005 to July 11, 2024. Along with the consistency, several model accuracy enhancing preprocessing steps such as data cleaning, feature selection, and normalization with Python were performed before modeling. Hyperparameter tuning to reduce the error margins on predictions was conducted after training the LSTM model. Testing the hypotheses showed that the LSTM model demonstrated a low Root Mean Square Error (RMSE) on the test dataset indicating outstanding forecasting accuracy. The ability of the model to outperform conventional time series forecasting techniques is attributed to its ability to effectively retain nonlinear time-series relationships and long-term dependencies. These findings suggest that the LSTM algorithm can serve as a reliable tool for stock price forecasting in emerging markets. This study provides practical insights for investors and lays the groundwork for future research on hybrid or ensemble models to further improve prediction robustness and accuracy
Text Mining Customer Feedback: An Agglomerative Clustering Approach to Service Optimization Muthoharoh, Luluk
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.27188

Abstract

The increasing volume of customer support tickets in the e-commerce industry creates significant challenges in terms of efficiently managing unstructured text data. Traditional manual categorization methods are no longer efficient or scalable in managing well with growing data. This study proposes a text mining framework that integrates Natural Language Processing (NLP) techniques with Agglomerative Hierarchical Clustering (AHC) to automatically group customer support tickets based on their textual content similarity. The framework includes preprocessing (cleaning, tokenization, stopword removal, and lemmatization), followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and dimensionality reduction using Principal Component Analysis (PCA). The clustered data is then visualized through dendrograms and evaluated using silhouette scores to determine the optimal number of clusters. Using a real-world dataset of 8.469 support tickets, the framework identified an optimal two-cluster configuration, distinguishing between general inquiries and specific error-related complaints. Unlike previous studies by using K-Means or DBSCAN, this framework leverage the hierarchical structure to capture nuanced textual similarities without requiring cluster number in the beginning. It also introduces integrated for evaluation and visualization pipeline tailored for operational customer use. However, because AHC has high computational complexity, this approach is more suitable for daily clustering batches than for real-time processing. Alternatives such as Mini-Batch K-Means also need to be considered for more efficient implementation. This study contributes to the development of an automated triage system and strategies for improving customer experience in digital platforms
Augmented Reality for 3D Geometric Shapes and Nets Combination for Android Wulansari, Ossy Dwi Endah; Zaini, Teuku Muhammad; Bachry, Bobby; Nursiyanto, Nursiyanto
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.27574

Abstract

Nets of 3D geometric shapes are flat patterns that can be folded into 3D objects when their edges are joined. Comprehending these nets is essential, but it can be challenging because they are generally presented only as 2D images in textbooks, making them difficult to visualize without physical models. Augmented reality (AR) provides an interactive approach to visualizing 3D shapes more effectively. This study aims to develop an Android app named AR 3DNETS, which utilizes a paper cone as a 3D object marker, allowing students to interact with 3D models using their smartphones. The research follows the Multimedia Development Life Cycle (MDLC) approach, encompassing concept development, design, material collection, assembly, testing, and distribution stages. Testing results indicate that AR 3DNETS functions properly, with all buttons working correctly and compatibility across Android versions 9.0 to 13.0. The results of the data obtained from the survey indicate that the AR 3DNETS application got 17 markers with a really great category, and 3 pointers with a great category. The results of the survey suggest that learning with the AR 3DNETS application is doable to be utilized as a learning media for nets of 3D shapes. Separated from that, the evaluation criteria which talk about fun, curiosity, and simple to utilize focuses, received a rate of 82.58%, 84.52%, and 85.81%, these rates are included within the "Exceptionally Great" category. Feedback from users indicates that AR 3DNETS is highly interactive, engaging, and helps students better visualize 3D shapes. This study contributes to providing learning media for the geometry of 3D shapes.
Performance Evaluation of Electronic Control System in Series-Parallel Hybrid Vehicle: A Simulation Study Permatasari, Jelita; Santoso, Dian Budhi; Sunardi, Egi; Laili, Maria Bestarina
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.27629

Abstract

The increasing contribution of the transportation sector to global emissions has driven the development of hybrid electric vehicles (HEVs) as a practical solution to reduce environmental impact. The effectiveness of HEVs is highly dependent on electronic control systems that regulate power distribution between the internal combustion engine (ICE), electric motor, generator, and battery in real time under various operating conditions. This study aims to evaluate the performance of the electronic control system implemented using Stateflow in a simulated series-parallel hybrid electric vehicle. The research methodology involves simulating the vehicle model in MATLAB/Simulink, which integrates Stateflow to design and manage the logic and operational mode transitions. A continuous closed-loop feedback structure is used to facilitate real-time control decisions, guided by input variables such as throttle position, vehicle speed, and battery State of Charge (SoC). Various driving scenarios are simulated, including acceleration, steady cruising, deceleration, and energy recovery during braking. Simulation results show that the designed electronic control system can maintain operational stability with engine efficiency reaching 92%, battery power utilization up to 65%, and electronic transitions between modes (EV, HEV, regenerative) in less than 0.2 seconds, demonstrating a 40% improvement in response compared to conventional electronic control models. These findings confirm the potential of Stateflow-based electronic control approaches in creating more responsive and efficient hybrid vehicle propulsion systems, while supporting the development of low-emission transportation technology
Smart Poultry Farming: A Mobile-Based IoT System for Real-Time Broiler Monitoring and Management Nalendra, Adimas Ketut; Waspada, Heri Priya
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.27622

Abstract

The integration of digital technologies in broiler chicken farming offers practical solutions to persistent challenges such as environmental monitoring, feed management, and livestock health. This study presents the development of a mobile-based broiler farming information system integrated with Internet of Things (IoT) devices to enhance operational efficiency, particularly for small- and medium-scale farmers. The system provides real-time monitoring and control via mobile devices, featuring environmental condition tracking, feed and water level notifications, inventory management, and performance analytics. There was a use of waterfall software development methodology with phases of requirements analysis, system design, implementation, testing, deployment, and maintenance. The system makes use of an ESP32 microcontroller, DHT22 temperature and humidity sensors, MQ-135 ammonia gas sensors, and ultrasonic sensors for feed and water level monitoring. The data from the sensors is transferred to a cloud server and is fetched through a user-friendly mobile application. Tests indicate that the system supports effective real-time monitoring of coop conditions, feed and water control, and prompt response to environmental variations by means of automated alerts. It also supports decision-making through access to past data and performance reports. Despite these advantages, there are still limitations pertaining to internet connectivity, sensor stability, and farmer computer literacy. The novelty of this research is its comprehensive, mobile-accessible IoT solution that combines environmental monitoring with the large-scale farm management operations, completing shortcomings of earlier systems with specialized, isolated monitoring.
Multi-Feature Hybrid LSTM-CNN Framework for Phishing Email Detection Rokunojjaman, MD; Malik, Anup; Sajeeb, MD Musfik Jahan; Yahiduzzaman, MD; Islam, MD Sajedul
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.26764

Abstract

Phishing has become one of the most universal cyber-attacks, leveraging users' trust to hijack sensitive information such as login credentials, financial data, and personal information. With the increasing sophistication of phishing techniques, traditional rule-based methods and signature-based detection approaches have become inadequate. This study proposes an advanced phishing email detection system on multiple datasets using a hybrid deep learning method that incorporates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). The research methodology consists of the following steps: Dataset Collection and Pre-processing, Feature Extraction, and Hybrid LSTM-CNN Model Architecture. A feature extraction phase enhances detection by incorporating email metadata, URL patterns, and embedded links. The model hybrid LSTM-CNN model achived the highest accuracy in three different datset like Enron 98.2%, SpamAssassin 97.5% and Kaggle phishing email dataset 96.8% than LSTM, CNN, SVM, Random Forest and BERT. Apart from accuracy, this model also gained the highest score in precision 97.9%, recall 98.5% and F1-score 98.2% in critical evaluation metrics. This approach demonstrates the efficiency of deep learning methods for phishing detection attempts and enhancing email security. Furthermore, the proposed system can be incorporated into electronic communication devices such as secure email servers, smart gateways, and IoT-based communication devices. With its integration of detection technology into electronics hardware, firmware, and network protocol design, the system allows for real-time threat prevention, reduces network vulnerabilities, and maximizes the reliability of modern communication infrastructures
Dynamic Virtual Environment Synthesis: Leveraging Machine Learning for Real-Time 3D Object Integration in VR Spaces Pathak, Anshuman; Singh, Anmol Deep; Saregar, Antomi; Dixit, Aparna; Dewalkar, S. V.; Panse, V. R.
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.28137

Abstract

Existing VR environments relies on static asset libraries and predesigned scenarios, which limits personalization and fails to account for diverse user needs. This paper aims Dynamic Virtual Environment Synthesis (DVES), a new framework based on machine learning to generate and control a large library of 3D objects for real-time creation and context-aware adaptation. The research method categorizes the system design into five main components: data collection, preprocessing and annotation, machine learning model training, VR environment integration, and user interaction. DVES allows users to customize VR spaces through natural language, gestures, or biometric feedback, harnessing generative models for creating objects, reinforcement learning for adaptive environments, and neural rendering for adding realism, building foundation for the next-gen entertainment ecosystem. DVES improves gaming, training, therapy, and education by mediating static design and real-time systems. Unlike the existing conventional VR systems which depends on the static and prebuilt scenes, DVES continuously learns from user interactions, enabling the system to evolve dynamically. This novel study investigates scalability, real-time performance, and natural interfaces and provides insights into future applications, giving a custom VR experience to the users. In long term, DVES could serve as a foundation for fully autonomous VR ecosystems, creating a personalized and immersive digital experience. The study ensures transitioning VR from static, predesigned systems to self-sustaining, user-driven digital worlds.
Optimization of KNN Classification for ECG Data Analysis: Comparative Study of Model Performance Using the Hyperparameter Tuning and Cross-Validation Jannah, Miftahul; Nababan, Adli Abdillah
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.28467

Abstract

Abnormal heart rhythms such as arrhythmias can lead to severe complications, including stroke and cardiac arrest. Electrocardiography (ECG) is commonly used to monitor heart activity due to its non-invasive, affordable, and efficient nature. However, manual ECG interpretation can be time-consuming and error-prone, especially in high-demand clinical settings. This study aims to improve the performance of the K-Nearest Neighbors (KNN) algorithm for ECG signal classification by applying hyperparameter tuning and validating the results through cross-validation. ECG data were collected from participants under three physical activity conditions: sitting, walking, and running. The methodology included signal preprocessing, model development, hyperparameter tuning via Grid Search, and performance validation using K-fold cross-validation. The baseline KNN model achieved an accuracy of 78%. After optimization—by setting the number of neighbors to 16, using the Manhattan distance metric, and applying distance-based weighting—accuracy improved to 82%. Precision increased from 0.79 to 0.82, and the F1-score rose from 0.76 to 0.79. These results demonstrate the impact of systematic tuning on classification performance. An optimized KNN model offers a practical diagnostic aid for arrhythmia detection, particularly in settings with limited access to expert analysis. Its simplicity and low computational cost make it suitable for integration into portable diagnostic devices

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