cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 20   21   22   23   24 
Detecting the Same Pattern in Choreography Balinese Dance Using Convolutional Neural Network and Analysis Suffix Tree I Komang Hendra Trinium Jaya; Made Windu Antara Kesiman; I Made Gede Sunarya
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.24461

Abstract

The Balinese dances that are popular today were created by maestros who have existed since time immemorial. To develop the dances made by the existing maestro, one must know the characteristics of each dance based on the motion used. The help of digital image processing and string algorithm analysis methods will help to determine the characteristics of a dance. The algorithm used for dance analysis is the Suffix Tree, where the suffix tree is one of the algorithms that can be used to find patterns from input strings. The string to be analyzed is a series of codes performed by the classifier. The classifier used is Convolutional Neural Network. This method uses an image as its input, which will later perform convolution operations and perform a full-connected layer. The results were obtained using the Convolutional Neural Network method with Alexnet architecture as the classification and confusion matrix to calculate the level of accuracy of the test set, the best accuracy for the head is by using parameter learning rate 0.001, epoch 150, and RGB color space obtained 95% accuracy, 88% precision, 78% recall, and 82% f1-score. For the full body, using a learning rate of 0.01, epoch 150, and RGB color space, the accuracy is 85%, precision is 79%, recall is 64%, and f1-score is 69%. For the legs, using a learning rate of 0.001, epoch 150, and RGB color space, the accuracy is 92%, precision is 84%, recall is 59%, and f1-score is 65%. The results of the suffix tree analysis between codes that use ground truth and classification results have similar values, although the results of the movement patterns obtained by the suffix tree algorithm have not varied, which is dominated by class A because class A is the dominant class in each dance.
Hybrid Approach-RSMOTE for Handling Class Imbalance with Label Noise Hartono Hartono; Erianto Ongko
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.23684

Abstract

The class imbalance problem is the main problem in classification. This issue arises because real-world datasets frequently exhibit an imbalance as a result of a class with more instances than other classes. In handling class imbalance, a Hybrid Approach that blends data-level and algorithm-level approaches produce good results. However, apart from the class imbalance, which reduces classification accuracy, the complexity of the data also has an effect. The complexity of this data causes a minority noise sample which lies between the minority and the majority. In order to determine how close minority samples are to their homogeneous and heterogeneous nearest neighbors, it is necessary to calculate the relative density. The greater the proximity to the homogeneous nearest neighbors, the greater the relative density, which causes the minority samples to be in a safe state but otherwise be categorized as noisy samples. This research will combine the application of the Hybrid Approach with A self-adaptive Robust SMOTE (RSMOTE), which is an adaptive method from SMOTE that applies the concept of relative density in the over-sampling process on minority samples. The research contribution is to implement the Hybrid Approach-RSMOTE in handling class imbalance with noise by using relative density in over-sampling and also to improve classification performance. The results showed that the Hybrid Approach-RSMOTE and Hybrid Approach-SMOTE had given good results in handling class imbalance. However, the Hybrid Approach-RSMOTE gave better results in the Precision, Recall, F1-Measure, and G-Mean and showed significant differences. Based on the results of the study, it can be stated that the performance of the Hybrid Approach in handling class imbalance is influenced by the selection of the over-sampling method. The results show that RSMOTE can be considered an over-sampling method in the Hybrid Approach.
Monitoring and Control Design of Automatic Transfer Switch-Automatic Main Failure with Human Machine Interface (HMI) Khafidzati 'Ulya; Yahya Chusna Arif; Lucky Pradigta Setiya Raharja
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.23750

Abstract

Automatic transfer switch-Automatic main failure is a commonly used technology for shifting supply from PLN to a generator in the case of a PLN blackout. The ATS-AMF module system frequently employs a PLC, which comes at a high cost, and the system alarm information only is seen by the user close to the system. The purpose of this research is to design an automatic transfer switch-automatic main failure system that used SCADA to improve the reliability of electricity supply by providing notification alarm information and buzzers. This research contribution is a development of an automatic transfer switch-automatic main failure, which can be used as a simulator for studies on measuring voltage, current, power, and frequency of main power supply in real cases. Furthermore, this instrument is used fuel level and temperature measures for its backup power (Genset), the result of measures will be monitored SCADA system with the available failure data, alarm logs, and status logs recorded in historical data, which is designed at a low cost and is easy to use. This information result of the measured sensor will be transferred in real-time to the SCADA system, so can be directly obtained for analysis. The main components for this system are microcontroller STM32 Nucleo, PZEM 004T sensor, ultrasonic sensor, DS18B20 sensor, ethernet, and VTSCADA. The result of this system is the temperature detects 89oC, and alarm information has been sounded with the statement “Genset Temperature Warning HIGH” thus instructing the generator to turn off the system. Meanwhile, based on the results of the fuel adjustment test, SCADA gives the information “Genset Fuel Level Warning LOW” when setting the fuel at 36%. The data historical viewer that stores up to 6 months and alarm information for the warning system on the SCADA has been successfully designed.
Towards Service Level Agreement Quantification on Service-Based Computing Irving Vitra Paputungan; Akmal Kurniadi Denna; Devi Rachmawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23629

Abstract

A service Level Agreement is an agreement between service providers and consumers that contains the rights and obligations of both parties, particularly in terms of the delivery of services provided during the subscription period on service-based computing. Once approved, normally, the Service Level Agreement will not change until the end of the subscription period. SLA violations are often positioned between yes and no. As a result, service providers must deal with severe penalties or compensation. In this paper, the use of weightage for each SLA parameter is introduced in this paper. Such quantification using weightage is the main contribution. SLA violation detection cases in service-based computing are used to demonstrate how SLA quantification works. In the simulation scenario of SLA quantification, the presence of weightage and its aggregates along with the upper and lower bound is able to help the SLA violation detection process more appropriate. Violations are no longer seen between Yes and No, but the severity of the violation can also be determined. The number of violated parameters is not very influential in determining the level because the main determinant is the weightage. At the same time, the upper and lower limits are also very helpful in determining the level of violation. It is believed that SLA quantification is the way forward for better SLA management.
Control Improvement of Low-Cost Cast Aluminium Robotic Arm Using Arduino Based Computed Torque Control Petrus Sutyasadi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.24646

Abstract

Gravity causes non-linearity in position control of an articulated industrial robotic arm. Especially for a joint position control of a robot’s shoulder and elbow that works parallel with the gravity direction. To overcome the problem, Computed Torque Control algorithm was implemented. This algorithm linearized the feedback, so a regular linear Proportional Derivative controller can be implemented. The contribution of this research is to find an effective controller to control a heavy weight low-cost robotic arm link/body using low-cost controller such as Arduino. A Computed Torque Control was implemented to control the shoulder joint of an articulated robotic arm. This joint is the most affected joint by the gravity. It works along the vertical plane, and loaded by the rest of the arm and the robot’s load. The proposed controller was compared to a Proportional Integral Derivative (PID) Controller and a Cascade PID Controller. The experiment showed that the Computed Torque Controller can control the position of the arm properly both in the direction along or against the gravity. A linear PID controller could not bring the arm to the set point when it moves against the gravity, but it works well when the arm moves in the opposite direction. A Cascade PID controller has an overshot when the arm moves along the gravity. But it works properly when it moves up against the gravity. A Computed Torque Control works well in both directions even in the presence of gravity force because it includes the gravity on its algorithm.
Raspberry Based Hand Gesture Recognition Using Haar Cascade and Local Binary Pattern Histogram Helfy Susilawati; Fitri Nuraeni
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.24643

Abstract

Many companies and even public institutions for civil servants currently use photo-taking for the attendance. However, this strategy is still considered ineffective since the employees still can hack the attendance by making their own photos and put them in their desks. Therefore, an alternative that can complement the current face detection method is highly needed so that the employee’s attendance can be directly monitored. One of the methods that can be used to detect the attendance is hand gesture detection. This research aims to detect hand gestures made by the employees to ensure whether they really come to work or not. This research make  the chance for manipulation using photo or fake GPS is quite small. For the purpose of hand gesture recognition, this study utilized Local Binary Pattern Histogram algorithm. The hand gesture image was first taken using a raspberry pi camera and then processed by the device to examine whether it matches the registered ID or not. The results showed that ID recognition by using hand gestures is detectable. The number recognition in hand gestures includes numbers 1 to 10. The test results showed that for 5 trials, the average time required for reading hand gestures using a laptop was 9.2 seconds, while that of using raspberry was 14.2 seconds. The results of this research show that the system has not been able to distinguish which hand is read first, so numbers that have the same number are considered the same, such as 81 and 18. So, the motion reading using a raspberry takes longer than that of using a laptop because the laptop's performance is higher than that of a raspberry and system cannot distinguish between numbers consisting of the same number.
Compensation to Fulfill Voltage Drop Security in Medium Voltage Feeders Hermagsantos Zein; Sri Utami; Siti Saodah; Conny Kurniawan Wachjoe
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.24743

Abstract

Increasing the load or expanding the feeder can increase the voltage drop. In addition, the greater the flow of reactive power on the line, the worse the voltage drop. On the other hand, the voltage must meet certain safety limits, and one way to correct the voltage drop is to apply for compensation. However, calculating the voltage drop through the power flow and the measurement methods is difficult to apply because it is necessary to determine the value of the amount of load on each node. This paper will propose a simple method that is convenient to apply to determine the compensation needed to maintain voltage quality in medium voltage feeders. The methodology used is a current source approach that functions as a variable. Then the current flow along the channel is assumed as a linear function so that the load center point is obtained according to the feeder configuration and load capacity. The simulation results on the 21-node feeder assuming a power factor of 0.8, show that the voltage drop improvement is quite effective with compensation. For example, at a 150 A current source, with 30 A compensation, the voltage drop can be increased from 4.45% to 3.98%. Furthermore, by applying for compensation, it is possible to expand the load to a source current of 165 A with a compensating current of 80 A.
Classification of Leukocytes Using Meta-Learning and Color Constancy Methods Eduardo Rivas-Posada; Mario I. Chacon-Murguia; Juan Alberto Ramirez-Quintana; Carlos Arzate-Quintana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25192

Abstract

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, reported models do not leverage the knowledge produced by the classification of leukocytes to solve similar tasks. For example, the knowledge can be reused to classify images collected with different types of microscopes and image-processing techniques. Therefore, we propose a meta-learning methodology for the classification of leukocyte images using different color constancy methods involving previous knowledge. Our methodology is trained with a specific task at the meta-level, and the knowledge produced is used to solve a different task at the base-level. For the meta-level, we implemented meta-models based on Xception, and for the base-level, we used support vector machine classifiers. Besides, we analyzed the Shades of Gray color constancy method commonly used in skin lesion diagnosis and now implemented for leukocyte images. Our methodology, at the meta-level, achieved 89.28% for precision, 95.65% for sensitivity, 91.78% for F1-score, and 94.40% for accuracy. These scores are competitive regarding the reported state-of-the-art models, especially the sensitivity which is very important for imbalanced datasets, and our meta-model outperforms previous works by +2.25%. Additionally, for the basophil images that were acquired from a chronic myeloid leukemia-positive sample, our meta-model obtained 100% for sensitivity. Moreover, we present an algorithm that generates a new conditioned output at the base-level obtaining highly competitive scores of 91.56% for sensitivity and F1 scores, 95.61% for precision, and 96.47% for accuracy. The findings indicate that our proposed meta-learning methodology can be applied to other medical image classification tasks and achieve high performances by reusing knowledge and reducing the training time for new similar tasks.
Terahertz Imaging Simulation Using Silicon-based Microstrip Antenna and Horn Antenna for Breast Cancer Detection Herry Tony Andhyka; Catur Apriono; Fitri Yuli Zulkifli
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.24809

Abstract

Breast cancer is one of the most common cancers in the world that cause a lot of mortality. Early cancer detection is crucial to decrease morbidity and mortality rates worldwide. Effective treatment or early intervention is crucial before the disease becomes more incurable. This research contributes to proposing a THz imaging system for early cancer detection, especially breast cancer, by using the benefit of THz radiation. Some approaches are made differently from the previous research, such as the imaging method, the antenna type, and the material for the antenna with the expectation of producing an efficient system and better imaging results. The system consists of one microstrip antenna as a transmitter, 25 horn antenna as a receiver and a breast tissue model. All antenna is designed to meet the requirement specification. The receiver antenna will receive power from the transmitter which will vary due to the absorption of the breast model. The received power will be visualized into a 2D color image. The simulation was able to visualize an image of the breast tissue model. Received power varies from -16.280 dB to -55.241 dB which leads to different color levels to represent the model. Antenna radiation patterns also take a role to cause the phenomenon occurred that leads to differentiation of the breast tissue type. Based on the results, this research has able to simulate a THz imaging system for breast cancer. Further modification to the system can be done to improve the imaging results.
Deep Learning-Based SOLO Architecture for Re-Identification of Single Persons by Locations Rotimi-Williams Bello; Chinedu Uchechukwu Oluigbo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i4.25059

Abstract

Analyzing and judging of captured and retrieved images of the targets from the surveillance video cameras for person re-identification have been a herculean task for computer vision that is worth further research. Hence, re-identification of single persons by locations based on single objects by locations (SOLO) model is proposed in this paper. To achieve the re-identification goal, we based the training of the re-identification model on synchronized stochastic gradient descent (SGD). SOLO is capable of exploiting the contextual cues and segmenting individual persons by their motions. The proposed approach consists of the following steps: (1) reformulating the person instance segmentation as: (a) prediction of category and (b) mask generation tasks for each person instance, (2) dividing the input person image into a uniform grids, i.e., G×G grid cells in such a way that a grid cell can predict the category of the semantic and masks of the person instances provided the center of the person falls into the grid cell and (3) conducting person segmentation. Discriminating features of individual persons are obtained by extraction using convolution neural networks. On person re-identification Market-1501 dataset, SOLO model achieved mAP of 84.1% and 93.8% rank-1 identification rate, higher than what is achieved by other comparative algorithms such as PL-Net, SegHAN, Siamese, GoogLeNet, and M3L (IBN-Net50). On person re-identification CUHK03 dataset, SOLO model achieved mAP of 82.1 % and 90.1% rank-1 identification rate, higher than what is achieved by other comparative algorithms such as PL-Net, SegHAN, Siamese, GoogLeNet, and M3L (IBN-Net50). These results show that SOLO model achieves best results for person re-identification, indicating high effectiveness of the model. The research contributions are: (1) Application of synchronized stochastic gradient descent (SGD) to SOLO training for person re-identification and (2) Single objects by locations using semantic category branch and instance mask branch instead of detect-then-segment method, thereby converting person instance segmentation into a solvable problem of single-shot classification.

Page 22 of 51 | Total Record : 505

 20   21   22   23   24 

Filter by Year

2015 2025


Reset
Filter By Issues
All Issue Vol. 11 No. 4 (2025): December Vol. 11 No. 3 (2025): September Vol. 11 No. 2 (2025): June Vol. 11 No. 1 (2025): March Vol. 10 No. 4 (2024): December Vol. 10 No. 3 (2024): September Vol. 10 No. 2 (2024): June Vol. 10 No. 1 (2024): March Vol 9, No 4 (2023): December Vol. 9 No. 4 (2023): December Vol 9, No 3 (2023): September Vol. 9 No. 3 (2023): September Vol. 9 No. 2 (2023): June Vol 9, No 2 (2023): June Vol 9, No 1 (2023): March Vol. 9 No. 1 (2023): March Vol 8, No 4 (2022): Desember Vol 8, No 3 (2022): September Vol 8, No 2 (2022): June Vol 8, No 1 (2022): March Vol 7, No 3 (2021): December Vol 7, No 2 (2021): August Vol 7, No 1 (2021): April Vol. 7 No. 1 (2021): April Vol 6, No 2 (2020): December Vol. 6 No. 1 (2020): June Vol 6, No 1 (2020): June Vol 5, No 2 (2019): December Vol 5, No 1 (2019): June Vol 4, No 2 (2018) Vol 4, No 2 (2018) Vol 4, No 1 (2018) Vol 4, No 1 (2018) Vol 3, No 2 (2017) Vol 3, No 2 (2017) Vol 3, No 1 (2017) Vol 3, No 1 (2017) Vol 2, No 2 (2016) Vol 2, No 2 (2016) Vol 2, No 1 (2016) Vol 2, No 1 (2016) Vol 1, No 1 (2015) Vol 1, No 1 (2015) More Issue