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Buletin Ilmiah Sarjana Teknik Elektro
Published by Universitas Ahmad Dahlan
ISSN : 26857936     EISSN : 26859572     DOI : 10.12928
Core Subject : Engineering,
Electrical & Electronics Engineering
Buletin Ilmiah Sarjana Teknik Elektro (BISTE) adalah jurnal terbuka dan merupakan jurnal nasional yang dikelola oleh Program Studi Teknik Elektro, Fakultas Teknologi Industri, Universitas Ahmad Dahlan. BISTE merupakan Jurnal yang diperuntukkan untuk mahasiswa sarjana Teknik Elektro. Ruang lingkup yang diterima adalah bidang teknik elektro dengan konsentrasi Otomasi Industri meliputi Internet of Things (IoT), PLC, Scada, DCS, Sistem Kendali, Robotika, Kecerdasan Buatan, Pengolahan Sinyal, Pengolahan Citra, Mikrokontroller, Sistem Embedded, Sistem Tenaga Listrik, dan Power Elektronik. Jurnal ini bertujuan untuk menerbitkan penelitian mahasiswa dan berkontribusi dalam pengembangan ilmu pengetahuan dan teknologi.
Arjuna Subject : -
Articles 295 Documents
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A Sentiment Analysis Using Fuzzy Support Vector Machine Algorithm Larasati, Aisyah; Susanto, Yohana Ruth Wulan Natalia; Mohamad, Effendi; Purnama, Agus Rachmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9363

Abstract

The Ministry of Communication and Information and the Ministry of BUMN of The Republic of Indonesia designed a mobile app “Peduli Lindungi” to be used to help the public and related government agencies in carrying out screening and tracing people's movement to stop the spread of Corona Virus Disease (Covid-19).The existence of a mobile app, “Peduli Lindungi” triggers abundant different sentiments from the Indonesian community, either positive or negative sentiments. Based on the positive sentiment, the government of the Republic of Indonesia may have some feedback about the aspects of the app that should be maintained. In contrast, negative sentiments can be used as initial points of the potential improvement of the mobile app. This study applies a Fuzzy Support Vector Machine (FSVM) model to classify the user's reviews on Peduli Lindungi Application. FSVM can classify customers’ reviews into two or more classes and relatively results in higher accuracy than other classification approaches. The results of this study indicate that the classification of reviews with FSVM produces quite good accuracy  with a value of 77%. A total correct prediction is 2192 reviews out of 2813 reviews.
Fuzzy A* for optimum Path Planning in a Large Maze Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9394

Abstract

 Traditional A* path planning, while guaranteeing the shortest path with an admissible heuristic, often employs conservative heuristic functions that neglect potential obstacles and map inaccuracies. This can lead to inefficient searches and increased memory usage in complex environments. To address this, machine learning methods have been explored to predict cost functions, reducing memory load while maintaining optimal solutions. However, these require extensive data collection and struggle in novel, intricate environments. We propose the Fuzzy A* algorithm, an enhancement of the classic A* method, incorporating a new determinant variable to adjust heuristic cost calculations. This adjustment modulates the scope of scanned vertices during searches, optimizing memory usage and computational efficiency. In our approach, unlike traditional A* heuristics that overlook environmental complexities, the Fuzzy A* employs a dynamic heuristic function. This function, leveraging fuzzy logic principles, adapts to varying levels of environmental complexity, allowing a more nuanced estimation of the path cost that considers potential obstructions and route feasibility. This adaptability contrasts with standard machine learning-based solutions, which, while effective in known environments, often falter in unfamiliar or highly complex settings due to their reliance on pre-existing datasets. Our experimental framework involved 100 maze-solving trials in diverse maze configurations, ranging from simple to highly intricate layouts, to evaluate the effectiveness of Fuzzy A*. We employed specific metrics such as path length, computational time, and memory usage for a comprehensive assessment. The results showcased that Fuzzy A* consistently found the shortest paths (99.96% success rate) and significantly reduced memory usage by 67% and 59% compared to Breadth-First-Search (BFS) and traditional A*, respectively. These findings underline the effectiveness of our modified heuristic approach in diverse and challenging environments, highlighting its potential for real-world pathfinding applications.
Advancing UAV Path Planning System: A Software Pattern Language for Dynamic Environments Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9407

Abstract

In the rapidly advancing domain of Unmanned Aerial Vehicle (UAV) technologies, the capability to navigate dynamic and unpredictable environments is paramount. To this end, we present a novel design pattern framework for real-time UAV path planning, derived from the established Pattern Language of Program Community (PLOP). This framework integrates a suite of software patterns, each selected for its role in enhancing UAV operational adaptability, environmental awareness, and resource management. Our proposed framework capitalizes on a blend of behavioral, structural, and creational patterns, which work in concert to refine the UAV's decision-making processes in response to changing environmental conditions. For instance, the Observer pattern is employed to maintain real-time environmental awareness, while the Strategy pattern allows for dynamic adaptability in the UAV's path planning algorithm. Theoretical analysis and conceptual evaluations form the backbone of this research, eschewing empirical experiments for a detailed exploration of the design's potential. By offering a systematic and standardized approach, this research contributes to the UAV field by providing a robust theoretical foundation for future empirical studies and practical implementations, aiming to elevate the efficiency and safety of UAV operations in dynamic environments.
Human Activity Recognition System Using WiFi Sensing and Deep Learning Kurniawati, Nazmia; Nurjihan, Shita Fitria
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9408

Abstract

Human activity recognition systems can be used for various purposes such as monitoring, authentication, and telemedicine. In this research, a non-invasive, high privacy, easy to implement, and affordable human activity recognition system based on WiFi and deep learning is developed. Sixteen activities; including upper body, lower body, and whole body movement; were recognized by utilizing Channel State Information (CSI) contained in the WiFi signal. Measurements were carried out in an empty room with dimensions of 6*8 m with the distance between the transmitter and receiver being 1, 3 and 6 meters from the subject. Google Teachable Machine is used to recognize activities carried out. From the measurement result, the accuracy shows more than 97%. It is also evident that the further the measurement distance, the worse the recognition results. This is due to the increasing amount of noise in the radio channel as the distance increases.
Adaptive Cyber-Defense for Unmanned Aerial Vehicles: A Modular Simulation Model with Dynamic Performance Management Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9415

Abstract

In light of escalating cyber threats, this study tackles the cybersecurity challenges in UAV systems, underscoring the limitations of static defense mechanisms. Traditional security approaches fall short against the sophisticated and evolving nature of cyber-attacks, particularly for UAVs that depend on real-time autonomy. Addressing this deficiency, we introduce an adaptive modular security system tailored for UAVs, enhancing resilience through real-time defensive adaptability. This system integrates scalable, modular components and employs machine learning techniques—specifically, neural networks and anomaly detection algorithm to improve threat prediction and response. Our approach marks a significant leap in UAV cybersecurity, departing from static defenses to a dynamic, context-aware strategy. By employing this system, UAV stakeholders gain the flexibility needed to counteract multifaceted cyber risks in diverse operational scenarios. The paper delves into the system's design and operational efficacy, juxtaposing it with conventional strategies. Experimental evaluations, using varied UAV scenarios, measure defense success rates, computational efficiency, and resource utilization. Findings reveal that our system surpasses traditional models in defense success and computational speed, albeit with a slight increase in resource usage a consideration for deployment in resource-constrained contexts. In closing, this research underscores the imperative for dynamic, adaptable cybersecurity solutions in UAV operations, presenting an innovative and proactive defense framework. It not only illustrates the immediate benefits of such adaptive systems but also paves the way for ongoing enhancements in UAV cyber defense mechanisms.
Development of the Design and Control of a Hexapod Robot for Uneven Terrain Putra, Prasetya Murdaka; Widodo, Nuryono Satya
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9426

Abstract

In the Indonesian Search and Rescue Robot Contest in 2021 and 2022, the robot still cannot work well when passing through uneven obstacles. The change in uneven terrain from the previous year was a difficulty for the robot in passing it. This research was conducted to develop mechanical design and movement control design for the robot, so that the robot can be more optimal when moving through uneven terrain. The design of the hexapod robot is done by reducing the dimensions of the existing robot and determining the Center of Gravity point. The movement of the robot is also designed by determining the angular position of the AX-18A servo with respect to the terrain the robot travels through. The movement algorithm applied to the robot is the tripod gait algorithm. The robot control on the debris field and irregular floor is done by applying a proximity sensor to minimise the robot from hitting the wall on the field. The robot also has an IMU sensor that will work in measuring the slope on the up-and-down floor terrain (slope), so that the movement of the robot can be adjusted when passing through the terrain with the slope read by the sensor. The results of the research conducted show that the robot can be redesigned through 3D design through solidworks by determining the Center of Gravity (CoG) point. The robot has been able to pass through 3 objects tested, namely debris terrain, irregular floor terrain, and up and down floor terrain. The success rate of the robot when passing through debris terrain and irregular floor terrain is 100% with an average time of 9.7 seconds and 10.1 seconds. The success rate of the robot when passing through the up-and-down floor terrain is 80% with an average time of 22.9 seconds.
The Use of Clustering Methods in Memory-Based Collaborative Filtering for Ranking-Based Recommendation Systems Wiyono, Slamet; Rais, Rais
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9435

Abstract

This research explores the application of clustering techniques and frequency normalization in collaborative filtering to enhance the performance of ranking-based recommendation systems. Collaborative filtering is a popular approach in recommendation systems that relies on user-item interaction data. In ranking-based recommendation systems, the goal is to provide users with a personalized list of items, sorted by their predicted relevance. In this study, we propose a novel approach that combines clustering and frequency normalization techniques. Clustering, in the context of data analysis, is a technique used to organize and group together users or items that share similar characteristics or features. This method proves beneficial in enhancing recommendation accuracy by uncovering hidden patterns within the data. Additionally, frequency normalization is utilized to mitigate potential biases in user-item interaction data, ensuring fair and unbiased recommendations. The research methodology involves data preprocessing, clustering algorithm selection, frequency normalization techniques, and evaluation metrics. Experimental results demonstrate that the proposed method outperforms traditional collaborative filtering approaches in terms of ranking accuracy and recommendation quality. This approach has the potential to enhance recommendation systems across various domains, including e-commerce, content recommendation, and personalized advertising.
Body Posture Position Alarm Prototype Based on NodeMCU ESP8266 Setyawan, Candra Dwi; Wisaksono, Arief
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9543

Abstract

Lack of physical activity has a negative impact, namely reduced motor coordination abilities and changes in posture or shape of the spine. Sitting positions that are more static and less ergonomic, such as sitting in a hunched position, can trigger significant muscle activation. Therefore, in an effort to prevent bone abnormalities, research was carried out regarding a prototype body posture alarm based on the NodeMCU ESP8266. This prototype uses a flexible sensor to read spinal curvature integrated into the NodeMCU ESP8266 and a buzzer as the output. This prototype will be attached to the back support shoulder, so this prototype design can also help repair bones that have been damaged due to bad sitting habits. In general, this prototype reminds users to always be in a normal body position by making a sound when the body position is not normal. From the test results, the prototype works well. NodeMCu's speed in capturing WiFi signals is fast enough so that the prototype works quickly, flexible sensor readings are accurate without using an amplifier. The back support shoulder design is very efficient in helping users to maintain a normal body position.
Comparative Analysis of MLP, CNN, and RNN Models in Automatic Speech Recognition: Dissecting Performance Metric Lenson, Abraham K. S.; Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9668

Abstract

This study conducts a comparative analysis of three prominent machine learning models: Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) in the field of automatic speech recognition (ASR). This research is distinct in its use of the LibriSpeech 'test-clean' dataset, selected for its diversity in speaker accents and varied recording conditions, establishing it as a robust benchmark for ASR performance evaluation. Our approach involved preprocessing the audio data to ensure consistency and extracting Mel-Frequency Cepstral Coefficients (MFCCs) as the primary features, crucial for capturing the nuances of human speech. The models were meticulously configured with specific architectural details and hyperparameters. The MLP and CNN models were designed to maximize their pattern recognition capabilities, while the RNN (LSTM) was optimized for processing temporal data. To assess their performance, we employed metrics such as precision, recall, and F1-score. The MLP and CNN models demonstrated exceptional accuracy, with scores of 0.98 across these metrics, indicating their effectiveness in feature extraction and pattern recognition. In contrast, the LSTM variant of RNN showed lower efficacy, with scores below 0.60, highlighting the challenges in handling sequential speech data. The results of this study shed light on the differing capabilities of these models in ASR. While the high accuracy of MLP and CNN suggests potential overfitting, the underperformance of LSTM underscores the necessity for further refinement in sequential data processing. This research contributes to the understanding of various machine learning approaches in ASR and paves the way for future investigations. We propose exploring hybrid model architectures and enhancing feature extraction methods to develop more sophisticated, real-world ASR systems. Additionally, our findings underscore the importance of considering model-specific strengths and limitations in ASR applications, guiding the direction of future research in this rapidly evolving field.
Optimizing UAV Navigation: A Particle Swarm Optimization Approach for Path Planning in 3D Environments Airlangga, Gregorius
Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v5i4.9696

Abstract

This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges.

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