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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Published by Universitas Ahmad Dahlan
ISSN : 23383070     EISSN : 23383062     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
JITEKI (Jurnal Ilmiah Teknik Elektro Komputer dan Informatika) is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD) in collaboration with Institute of Advanced Engineering and Science (IAES). The aim of this journal scope is 1) Control and Automation, 2) Electrical (power), 3) Signal Processing, 4) Computing and Informatics, generally or on specific issues, etc.
Arjuna Subject : -
Articles 505 Documents
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Photovoltaic Generator Approach Model for Characteristic Estimation I-V Suwarno Suwarno; Rini Sadiatmi; Aminah Asmara Dewi; Herman Birje
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26440

Abstract

Modeling of the photovoltaic (PV) approach is generally described by nonlinear equations with solving the equation using the steps iteratively.  However, this proposed research discusses the monocrystal type PV module approach model to estimate the characteristics of a photovoltaic generator, because it has the advantage that it is good enough to operate in Indonesia. This approach model takes into account the relationship between power, energy, and current to obtain the performance characteristics of the PV generator. This PV generator approach model is compared with PV generator manufacturer data and analyzed to validate the proposed approach model. Approach model with simulation hope this helps to find out IV characteristics according to the data recorded on the PV module and can save time or reduce the time to measurement results. The simulation results obtained the amount of power, energy, and current, 200.475W, 133.65 W/m2, and 7.9 Amperes, respectively with a simulation time of about 1.5 milliseconds. In addition to the above results, a comparison between the current and the power of the PV modules under study is also given and it gives the result that at a certain current the power level will no longer increase, but the power will decrease drastically due to heat from the PV panel module, because the module has the ability to receive heat from outside.
Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network Muhammad Fauzan Nafiz; Dwi Kartini; Mohammad Reza Faisal; Fatma Indriani; Triando Hamonangan Saragih
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26374

Abstract

COVID-19 disease is known as a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed as a means of detecting COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. The AlexNet model, utilizing an input size of 227x227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930303. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through the utilization of mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. The primary contribution of this research lies in identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19.
The Implementation of H5P in Interactive Games for Cyber Security Awareness Learning Facilities for Elementary and Junior High School Students Mazura Binti Mat Din; Shaifizat Mansor; Siti Rafidah Muhamat Dawam; Andria Andria; Ridam Dwi Laksono; Kelik Sussolaikah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26547

Abstract

The importance of digital security awareness is not only related to privacy protection but also includes responsible use, protection from cyberbullying, and understanding the consequences of online actions. In primary and secondary schools, understanding regarding cyber security awareness is still lacking; students must realize that what they do in the digital world can have long-term impacts on their real lives. An introduction and understanding of digital security awareness need to be given to elementary and junior high school students. This study intended to provide a vehicle (learning tool) and introduce cyber security awareness using interactive games. By using the Moodle platform and the H5P plugin to make games more interactive, interesting, effective, and efficient. This activity was carried out using the RnD method. With the RnD method, the development of awareness and the many ways to protect oneself on the internet can be developed significantly, according to the level of understanding of students Game platform development uses a Moodle-based learning management system. Then do the customization using H5P. Meanwhile, games are developed by taking into account cyber security awareness indicators. Game applications that are compiled can run well. The trial was conducted on 4 elementary schools and 4 junior high schools. During the trial, the platform's reliability can run smoothly when accessed by users. There were no obstacles in using games by the user during the trial. User features related to achievement badges, as an indication of the level of play each player can achieve responsively. Interactive games about cyber security awareness can be formed and run according to the design set. The contribution of this research is to increase students' understanding of internet safety awareness through interactive games and to increase students' knowledge about how to protect themselves while surfing the internet through interactive games.
Design and Implementation of IoT-Based Burglary Detection System Mokhalad Mahdi Jassem; Mohammed Al-Nouman
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26602

Abstract

This paper aims to design and implement an IoT-based anti-theft security system. The system uses fingerprint recognition technology to grant access to a building only if the fingerprint matches the stored data. In case of unauthorized access attempts, the system captures a photo of the person and sends an immediate alert to the homeowner via the Telegram API. The proposed paper methodology involves analyzing existing systems, identifying research gaps, and developing a generally implementable framework. The proposed system is implemented using Raspberry Pi as the main control unit and incorporates components like the R305 Fingerprint Recognition Sensor, Camera Pi for video surveillance, and additional hardware for door control and sensor integration. Through practical testing, the implemented system demonstrates reliable burglary detection and notification capabilities, enhancing home or building security. Finally, the obtained research results offer valuable insights for future developments in anti-theft security systems. 
Performance Evaluation of Sliding Mode Control (SMC) for DC Motor Speed Control Dimas Dwika Saputra; Alfian Ma'arif; Hari Maghfiroh; Muhammad Ahmad Baballe; Angelo Marcelo Tusset; Abdel-Nasser Sharkawy; Rania Majdoubi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26291

Abstract

DC motor is an industrial motor that is practical for many applications and implementations. However, the speed of a DC motor often decreases because of the given load, thus causing it to be unstable and inconstant. In addition, parameter uncertainty is another issue of DC motors. The performance of the system will be impacted by the uncertainty. Therefore, in this study, SMC is used as speed control of the DC motor since it can handle non-linear plants. The performance also compares with PID to know the effectiveness of the SMC method in DC motor speed control. This study proposes a hardware design and implementation of DC motor angular speed control on Arduino UNO as an embedded control system. The performance comparison analysis results proved that both controllers could perform well. However, both controllers need further fine-tuning. There are still overshoot and steady-state errors for PID and SMC, respectively. In future work, the optimization method can be used to find the optimal gain or by combining it with an adaptive algorithm.
Design of a Laboratory Scale Archemedes Screw Turbine Model Hydroelectric Power Station (PLTA) Simulator Muhammad Hasan Basri; Ahmad Muhtadi; Darul Hasan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26309

Abstract

The purpose of this research is to design a new model simulator of the Archimedes Screw turbine on a laboratory scale which is simple, inexpensive, environmentally friendly and for practice at the Electrical Engineering Laboratory of Nurul Jadid University by studying the efficiency of the Archimedes turbine which utilizes kinetic energy. water flow energy from the difference in upstream-downstream water head. Methods used numerical simulations have been run to evaluate the performance coefficient of the turbine alone (without friction loss or blockage augmentation), and to extend the TSR range. Numerical simulations make it possible to generate efficiency curves of Archimedes Screw turbines in both parallel and inclined configurations. The result obtained is that the proposed geometry can be used in real-life applications, providing 0.5 kW at flow velocities between 1 and 2 m/s. Novelty of hydropower simulation studies of the Archimedes turbine screw model using numerical simulation methods.
Circuit Modeling of Dual Band MIMO Diversity Antenna for LTE and X-Band Applications Aminu Gambo A.; S. F. Kolawale; Sani Saminu; Ali Danladi; Adamu Halilu Jabire
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26272

Abstract

This paper presents a study on developing a dual-band antenna equivalent circuit model for X-Band and LTE applications. MIMO antennas play a crucial role in modern wireless communication systems, and understanding their impedance behavior is essential. This work proposes a dual-band lumped equivalent circuit model, utilizing gradient optimization based on antenna-simulated S-parameters in Advanced Design System (ADS). The four radiating elements of the MIMO antenna are accurately modeled, considering their geometry and the defected ground structure (DGS) effect, which enhances the antenna's isolation and low correlation coefficient (ECC). The calculated lumped equivalent circuit model is validated through rigorous simulation and measurement data, demonstrating consistency with the expected results. The experimental measurements show measured isolation exceeding 20 dB while achieving a maximum realized gain of 5.9 dBi and an efficiency of 87%. The developed model holds promise for improving the design and performance of MIMO antennas for various applications.
Experimenting with the Hyperparameter of Six Models for Glaucoma Classification Muhammad Ilham; Angga Prihantoro; Iqbal Kurniawan Perdana; Rita Magdalena; Sofia Saidah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26331

Abstract

Glaucoma, being one of the leading causes of blindness worldwide, often presents without noticeable symptoms, making early detection crucial for effective treatment. Numerous studies have been conducted to develop glaucoma detection systems. In this particular study, a glaucoma detection system using the CNN method was developed. The models employed in this study include AlexNet, Custom Layer, MobileNetV2, EfficientNetV1, InceptionV3, and VGG19. For training, an augmented RIM-ONE DL dataset was utilized. Hyperparameter experiments were conducted to determine the most optimal parameters for each model, specifically testing batch size, learning rate, and optimizer. The hyperparameter optimization process yielded the optimal parameters for each model. However, it is important to note that the MobileNetV2, InceptionV1, and VGG19 models exhibited signs of overfitting in the training graph results. Among the models, the custom layer model achieved the highest accuracy of 93%, while InceptionV3 attained the lowest accuracy at 83.5%. Testing of the models was performed using data from Cicendo Eye Hospital and the RIM-ONE DL testing dataset. Based on the testing results, it was found that InceptionV3 outperformed the other models in predicting images accurately. Therefore, the study concluded that high accuracy in training does not necessarily indicate superior performance in testing, particularly when limited variation exists in the training dataset.
Optimization of Machine Learning Models with Segmentation to Determine the Pose of Cattle Amril Mutoi Siregar; Sony Hartono Wijaya; Ahmad Fauzi; Tjong Wan Sen; Sutan Faisal; Tukino Tukino; Yana Cahyana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26750

Abstract

Image pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20. The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color even though shape recognition.
Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective Naveen N; Nagaraj G Cholli
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.25148

Abstract

Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinicians

Page 28 of 51 | Total Record : 505

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