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Freddy Kurniawan
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Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC)
Published by Institut Teknologi Dirgantara Adisutjipto
ISSN : 26852381     EISSN : 27152626     DOI : 10.28989/avitec
Core Subject : Science, Engineering,
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering
This journal is the scientific publications journal published by Department of Electrical Engineering, Sekolah Tinggi Teknologi Adisutjipto. It aims to promote and disseminate the research finding in the development of management theories and practices. It will provide a platform for academicians, researchers, and practitioners to share their experience and solution to problems in different areas of journal scopes. Every submitted paper will be blind-reviewed by peer-reviewers. Reviewing process will consider novelty, objectivity, method, scientific impact, conclusion, and references.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 142 Documents
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Intelligent HEED Algorithm for Energy Optimization in Heterogeneous Wireless Sensor Network Adedokun, Ayobami O.; Dahunsi, Folasade M.
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 1 (2025): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i1.2565

Abstract

Wireless Sensor Networks (WSNs) are deployed in various applications, from agricultural automation to environmental monitoring, where sensor nodes transmit data to a central base station. However, nodes further from the base station face accelerated energy depletion, primarily due to higher communication demands. Energy conservation is critical in these resource-constrained networks to prolong network longevity. This study introduces I-HEED (Intelligent Hybrid Energy-Efficient Distributed) clustering, a novel energy optimization algorithm that merges the energy-efficient HEED (Hybrid Energy-Efficient Distributed) protocol with the Monkey Search Algorithm. I-HEED balances energy distribution by optimizing cluster head selection, enabling efficient data aggregation and transmission to the base station. Through optimized cluster head selection, I-HEED effectively reduces energy consumption and enhances data transmission efficiency compared to LEACH (Low Energy Adaptive Clustering Hierarchy), DEEC (Distributed Energy Efficient Clustering), and HEED. The performance evaluation shows that I-HEED significantly outperforms existing protocols, with improvements of 3,700 more packets transmitted than DEEC, 2,800 more than HEED, and 500 more than LEACH. I-HEED also achieved higher node survivability and fewer dead nodes, making it ideal for resource-constrained WSNs. These findings validate I-HEED’s effectiveness as a robust, energy-efficient solution, offering extended operational life across diverse WSN applications in resource-limited environments.
Anomalous Investigation of ILS Glide Path Signals on Runway 24 at the Jakarta Air Traffic Service Center Wildan, Muh; Wibowo, Priyo; Wicaksono, Mochamad Sugeng; Sulaiman, Muhammad Arif; Rahmadani, Eldo Tri; Prasojo, Muhamad Adimukti
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 1 (2025): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i1.2847

Abstract

Precision landing guidance is critical for aircraft safety, yet recurring signal anomalies in the ILS Glide Path on runway 24 at the Jakarta Air Traffic Service Center raise concerns about approach and landing reliability. Despite compliance with standard regulations, persistent signal anomalies affecting glide slope accuracy raise concerns about safe aircraft approach. This study investigates the sources of distortion, quantifies their impact on ILS Glide Path performance, and proposes mitigation strategies. Spectrum analysis, environmental assessments, and signal evaluation through oscilloscopes and navigation analyzers were employs to identify and quantify sources of distortion. Results indicate that while composite audio signals comply with standards, navigation analyzer readings reveal persistent deviations in Difference in Depth of Modulation (DDM) due to harmonic distortions at 30 Hz and its multiples up to 450 Hz. These distortions could interfere with DDM values received by calibration aircraft, making conventional technical adjustments, such as power level settings and antenna reconfigurations, insufficient for complete resolution. Instead, alternative mitigation approaches including reducing environmental reflections, optimizing siting criteria, and refining regulatory compliance measures are recommended. These findings provide valuable insights for enhancing ILS Glide Path reliability, refining signal mitigation strategies, and ensuring regulatory compliance for safer aviation navigation systems.
Optimizing Connectivity and Network Management with SDN Technology on VANET Using the SSF Method Dinata, Hane Yorda; Suranegara, Galura Muhammad
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 1 (2025): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i1.2867

Abstract

Vehicular Ad-Hoc Networks (VANET) represent a crucial innovation in transportation technology, enabling vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, VANET faces challenges such as signal fluctuations, data security issues, and high mobility, which affect network reliability. This study aims to optimize connectivity and network management in VANET using the Strongest-Signal-First (SSF) method supported by Software-Defined Networking (SDN). The research was conducted through simulations using Mininet-WiFi. The system was designed with two vehicles and four access points to evaluate the performance of the SSF method, focusing on quality of service (QoS) parameters such as data transfer, jitter, packet loss, and bandwidth. Data were collected over a 30-second simulation under varying bandwidth conditions. The results demonstrate that the SSF method effectively maintains communication reliability, achieving a maximum packet loss of only 0.05% and an average data transfer rate of 285 – 324 kB. However, the effects of fading and network dynamics caused fluctuations in minimum transfer rates (102 – 114 kB) and jitter (0.1 – 1.0 ms), particularly at lower bandwidths. The SSF method has proven to enhance communication stability in VANET. Nevertheless, challenges such as fading and high mobility require additional mechanisms to further improve network performance in dynamic environments.
The Role of VADER and SentiWordNet Labeling in Naïve Bayes Accuracy for Sentiment Analysis of Rice Price Increases Furqon, Ihtiar Nur; Soyusiawaty, Dewi
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 1 (2025): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i1.2806

Abstract

The surge in rice prices in Indonesia in 2024 is a critical issue affecting social welfare and national food security, particularly amid rising rice imports. This study evaluates public sentiment on Twitter using the Naïve Bayes method and compares the effectiveness of two automated labeling methods, VADER and SentiWordNet, in improving sentiment analysis accuracy. The research is significant due to the limited literature on automated labeling comparisons, especially in food price crises. The methodology includes data collection, preprocessing, translation, sentiment labeling using VADER and SentiWordNet, TF-IDF feature extraction, Naïve Bayes classification, and performance evaluation across different data split ratios: 60% training and 40% testing, 70% training and 30% testing, 80% training and 20% testing, and 90% training and 10% testing. Results show that VADER excels in detecting positive sentiments, achieving 74.42% accuracy at a 90:10 split but struggles with negative sentiment identification, with a highest F1-score of 56.58%. SentiWordNet performs better for positive sentiment detection, reaching 77.86% accuracy and 96.22% recall at an 80:20 split but yields a low F1-score of 32.15% for negative sentiments. In conclusion, VADER is suitable for balanced sentiment detection, while SentiWordNet is more effective for identifying positive sentiments.
Network Signal Coverage Expansion Planning WLAN Outdoor with 4-C Scenario Approach at Telkom University Kaffa, Rayhan Sidiq; Usman, Uke Kurniawan; Purnomo, Zhikya Sekar Lutfi; Akbar, Rangga Fadhillah; Wisetyo, Sakti Putro
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 1 (2025): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i1.2711

Abstract

Enhancing internet accessibility on campus is vital for both academic purposes and general internet usage. This research aims to expand the outdoor Wi-Fi coverage at Telkom University, taking into account wireless signal propagation, infrastructure, and network structure. The planning process includes conducting a walk test to evaluate signal coverage, simulating signal and interference, calculating the link budget (including pathloss, RSSI, and EIRP), and determining the Bill of Quantities (BoQ). The analysis of these simulations and calculations leads to informed planning recommendations. By applying the 4-C scenario approach, the study demonstrates that this method effectively covers the entire target area with a minimum RSSI of -75 dBm. There is no interference in the 2.4 GHz band, although co-channel interference occurs in the 5 GHz band. The RSSI consistently stays above -75 dBm, with the lowest measurement being -74 dBm over 200 meters in the 2.4 GHz band. EIRP values are within Indonesia's standard limit of 36 dBm, with a peak value of 33 dBm in the 5 GHz band. The total length of transmission cables used is 1628.3 meters, and the total BoQ amounts to Rp. 384,964,540.
Development of Coordinated Control of Vehicle Traffic Flow at Adjacent Intersection Kurniawan, Freddy; Jusoh, Muzammil; Muminov, Bahodir; Alam, Hermansyah; Dermawan, Denny; Purnomo, Muhamad Jalu
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 1 (2025): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i1.2823

Abstract

Traffic congestion in areas with two closely situated traffic lights is a complex issue that is often difficult to resolve. To address this issue, a coordination of timing between the two traffic controllers is proposed. This research conducts an experiment with two traffic controllers at two nearby intersections. The vehicle flow at each intersection is managed by the Agent acting as the traffic controller. The agent where more vehicles arrive is designated as the master agent, while the other agent is designated as the slave agent. A coordination algorithm is developed to synchronize the timing of the traffic controller so that the timing at the slave Agent was adjusted according to vehicle platoon arrivals from the master Agent. By this method, the green phase of the slave agent can be synchronized with the master agent, allowing vehicle platoons arriving from the master agent to immediately receive a green phase at the slave agent. This coordinated traffic control can be implemented with a microcontroller-based system, and vehicle movement can be simulated using Matlab's SimEvents. From the experiment conducted for two intersections located 500 meters apart, this scheme can reduce the average vehicle wait time from 40 seconds to just 9.4 seconds.
Aspect-Based Sentiment Analysis on User Perceptions of OVO using Latent Dirichlet Allocation and Support Vector Machine Aprilia, Eka Fahira; Arifiyanti, Amalia Anjani; Sembilu, Nambi
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 2 (2025): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i2.3035

Abstract

The rapid development of digital technology and the Internet has significantly influenced financial services in Indonesia, leading to the widespread use of digital wallets. One of the most prominent digital wallet platforms is OVO, which has received millions of user reviews across application stores. This study applies aspect-based sentiment analysis to better understand user perceptions from reviews of the OVO application (versions 3.115 to 3.119). A total of 17.086 reviews were collected through web scraping and refined to 4.996 relevant entries. Topic modeling using Latent Dirichlet Allocation (LDA) identified four main aspects frequently discussed by users: Transaction Efficiency, User Experience, Account Access and Registration, and Balance and Charges. However, automatic aspect labeling using LDA keywords achieved only 11.46% agreement with manual annotations, increasing to 40.60% after keyword refinement. Therefore, manual aspect annotation was adopted as the basis for sentiment labeling. Sentiment labeling was conducted by three annotators based on structured guidelines, achieving a Fleiss’ Kappa score of 0.9915. A classification model was then developed using the Support Vector Machine (SVM) algorithm across six testing scenarios. The best-performing model, using a Linear kernel without ML-SMOTE, achieved a macro-average precision of 0.843, recall of 0.786, and F1-Score of 0.804. These results demonstrate the model’s effectiveness in handling multi-label classification under imbalanced data conditions, particularly for well-distributed aspects such as Transaction Efficiency and User Experience, while highlighting challenges in minority-class detection for aspects such as Account Access and Registration and Balance and Charges.
Enhancing Aspect-Based Sentiment Analysis in Imbalanced Multilabel Datasets using Resampling and Classifiers for Digital Signature Applications Narendra, Efriza Cahya; Arifiyanti, Amalia Anjani; Sugata, Tri Luhur Indayanti
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 2 (2025): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i2.3023

Abstract

Amid the growing demand for digital identity solutions, applications like Privy, VIDA, and Xignature offer integrated digital signature and e-stamp services, generating extensive user feedback on platforms like Google Play Store and App Store. Extracting meaningful insights from thousands of reviews is challenging, necessitating effective sentiment analysis. Aspect-Based Sentiment Analysis (ABSA) enables detailed sentiment evaluation by linking user feedback to specific aspects and sentiments. However, ABSA faces challenges with imbalanced datasets where label distributions are uneven. This study explores the application of three resampling techniques, including MLROS, MLSMOTE, and REMEDIAL, to address this issue in multilabel classification. Using multilabel classifiers, including Binary Relevance, Label Powerset, and Classifier Chains, the study systematically evaluates their performance. Results reveal that resampling significantly enhances outcomes, with MLROS and Classifier Chains under a 70:30 split achieving the best performance, reducing Hamming Loss to 0.0401 or 95% accuracy. This marks a 34.2% improvement over baseline models without resampling or classifiers. The model generalizes well to unseen data with minimal overfitting, as indicated by validation results. These results underscore the importance of imbalanced data resampling and multilabel classification techniques in advancing ABSA, offering valuable insights for improving sentiment analysis in real-world applications.
A Combined Approach to Safety and Security of IoT by Applying Fault Tree Analysis and Attack Trees with Minimal Cut Sets Karim, Mohammad Rezaul; Kabir, Sohag; Lei, Ci; Lefticaru, Raluca; Abdul Baset, Mohammad
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 2 (2025): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i2.2918

Abstract

The rapid proliferation of Internet of Things (IoT) systems introduces complex safety and security challenges, as traditional frameworks often treat these aspects separately, overlooking critical interdependencies. This study proposes a unified methodology integrating Fault Tree Analysis (FTA) and Attack Trees (AT) with Minimal Cut Sets (MCS) to holistically assess IoT safety and security. FTA systematically identifies root causes of system failures, while AT models attack vectors and their probabilities. By deriving MCS—minimal combinations of safety faults and security breaches—the approach reveals critical scenarios where failures and attacks interact, enabling prioritized mitigation. Applied to a real-world IoT dataset (BoTNeTIoT-L01), the framework identified key vulnerabilities, such as Data_Corruption (safety probability: 0.005) linked to Mirai attacks (security probability: 0.01), demonstrating how integrated MCS enhance risk visibility. Quantitative analysis (mean safety: 0.005, variance: 0.000006) confirmed the methodology’s effectiveness in capturing interdependencies across IoT layers (Perception, Network, Data Processing, Application). Results emphasize that combined safety-security analysis prevents isolated risk assessments, offering actionable insights for resilient IoT design. The study concludes that integrating FTA-AT-MCS bridges existing gaps in IoT dependability, enabling targeted resource allocation and adaptive strategies against evolving threats. This approach advances IoT ecosystems’ safety, security, and trustworthiness in interconnected environments.
A Multilingual Approach to Aspect-Based Sentiment Analysis on Gobis Suroboyo Application Reviews using LDA and SVM Puspitasari, Dianita; Wahyuni, Eka Dyar; Permatasari, Reisa
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 2 (2025): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i2.3033

Abstract

The GOBIS application, developed by the Surabaya City Transportation Department, is a digital service designed to provide public transportation information and reduce traffic congestion. Despite having exceeded 100,000 downloads, the application has received numerous complaints from users, as reflected in the multilingual reviews on its platform. To ensure analytical consistency, this research focuses solely on reviews in Indonesian and English. Using Aspect-Based Sentiment Analysis (ABSA), this study employs Latent Dirichlet Allocation (LDA) for aspect identification and Support Vector Machine (SVM) for sentiment classification. The aim of this research is to determine the dominant aspects in user feedback and evaluate the effectiveness of the Support Vector Machine (SVM) model in classifying multilingual reviews. The research results show six main aspects that frequently appear in reviews, namely Application Features and Development, User Suggestions and Service Innovation, Error and Location Accuracy, Delay and Application Usability, Comfort and Service Quality, as well as Route Tracking and Vehicle Information. The Support Vector Machine (SVM) model, tested with 10-fold cross-validation, demonstrates consistent performance, achieving balanced metrics accuracy (74.16%), precision (73.76%), recall (73.54%), and F1-score (73.63%). This highlights its capability in handling multilingual sentiment analysis for application improvement.

Page 12 of 15 | Total Record : 142

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