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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 601 Documents
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Development of Modified CNN Algorithm for Agriculture Product: A Research Review Deepika Sharma; Navneet Agrawal
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 1 (2022): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i1.23722

Abstract

Now a day, with the increase in world population, the demand for agricultural products is also increased. Modern days electronic technologies combined with machine vision techniques have become a good resource for precise weed and crop detection in the field. It is becoming prominent in precision agriculture and also supporting site-specific weed management. By reviewing as there are so many different kinds of weed detection algorithms that were already used in the weed removal process or in agriculture. By the comparative study of research papers on weed detection. In this paper, we have suggested advanced and improved algorithms which take care of most of the limitations of previous work. The main goal of this review is to study the different types of algorithms used to detect weeds present in crops for automated systems in agriculture. This paper used a method that is based on a convolutional neural network model, VGG16, to identify images of weeds. As the basic network, VGG16 has very good classification performance, and it is relatively easy to modify. Download the weed dataset. This image dataset has 15336 segments, being 3249 of soil, 7376 soybeans, 3520 grass, and 1191 broadleaf weeds. Our model fixes the first 16 layers of  VGG16 parameters for layer-by-layer automatic extraction of features, adding an average pooling layer, convolution layer, Dropout layer, fully connected layer, and softmax for classifiers. The results show that the final model performs well in the classification effect of 4 classes. The accuracy is 97.76 %. We will compare our result with the CNN model. It provides an accurate and reliable judgment basis for quantitative chemical pesticide spraying. The results of this study can provide an overview of the use of CNN-based techniques for weed detection.
K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg Shoffan Saifullah; Rafal Drezewski; Alin Khaliduzzaman; Lean Karlo Tolentino; Rabbimov Ilyos
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.v8i2.23724

Abstract

The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a non-destructive quantitative study on embryos and their morphology in a precision poultry production system in the future.
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.
UDP Pervasive Protocol Design and Implementation on Multi Devices using MyRIO Mochammad Hannats Hanafi Ichsan; Rizal Maulana; Octavian Metta Wisnu Wardhana
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.v8i2.23835

Abstract

Pervasive Computing is one of the practical computing applications to facilitate computer operations by minimizing human interaction with computers. Pervasive Computing has been developed using UDP protocol to recognize the other devices without manual configuration. NI MyRIO device is one of the most reliable devices for the prototyping process. However, there are still not many implementations of data transmission using specific protocols. And the direction of use for smart homes or smart environments is still not widely done. This research contribution implemented Pervasive UDP protocols on PC devices and two NI MyRIO using LabVIEW programming language. UDP protocols are used because they do not require a handshake to recognize another device to reduce delays and have smaller data sizes due to the absence of recognition fields and sequence fields. Each device uses a dual-state machine system design that has a function to detect other devices automatically and act as an application to use the address of another device. PC represents the host, and MyRIO represents the client. Using the same state machine to detect all devices can recognize more than one device on the same network. The obtained test results show that all functional testing scenarios succeeded 100%. The discovery time is averaged at 0.202754 seconds for First MyRIO as First Client and 0.303201 seconds for Second MyRIO as Second Client. The delay in sending data from the host to the client is no more than 2 seconds. Based on this research, MyRIO has the ability to pervasive Computing with other devices. And can be used for prototyping models with good capabilities.
A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification Muhammad Dzulfikar Fauzi; Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan; Nia Annisa Ferani Tanjung
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.v8i2.23886

Abstract

Potato plant growth needs to be protected from weeds that grow around it. Currently, the manual spraying of pesticides by farmers is not only precise on weeds but also on cultivated plants. Therefore, we need an intelligent system that can appropriately classify potato plants and weeds. The research contribution combines feature extraction and appropriate classification methods to obtain optimal accuracy. In addition, the small amount of data also contributes to this research. In this research, it is proposed to use a combination of feature extraction using deep learning techniques and classification using machine learning. We use the feature extraction method with the DenseNet201 model because this study's data is not too much. Complex vectors from DenseNet201 were reduced using Principal Component Analysis (PCA). Then we classified it with the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classification methods. The experimental results show that the PCA method can reduce the complexity of high-dimensional features into 2 and 3 dimensions. The average of the best classification results using SVM was obtained with a 3-dimensional PCA configuration, but on the contrary, using KNN obtained the best results in a 2-dimensional PCA configuration. The results showed 100% accuracy on the DenseNet201-SVM hybrid. The SVM kernel configuration used is a linear kernel. The results of this study can be an insight into an accurate classification method for separating weeds and potatoes so that agricultural technology can apply this method for classification.
Tuberculosis Detection in X-Ray Image Using Deep Learning Approach with VGG-16 Architecture Suci Aulia; Sugondo Hadiyoso
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.v8i2.23994

Abstract

Tuberculosis (TB) is a chronic disease still the main problem in Indonesia. However, this disease can be cured with drugs at a particular time after the patient is detected as having TB. TB diagnosis or screening can be made through x-ray imaging of the chest cavity by a radiology specialist. The Mantoux test can then be used to confirm the diagnosis.  X-ray images often have varying contrasts that lead to true negatives or false negatives. Whereas generally, a chest x-ray is the initial examination of TB. Error detection will have a fatal impact on treatment therapy. Therefore, this study proposed a system for TB detection based on x-ray images using deep learning. The system developed uses a Convolutional Neural Network (CNN) with the VGG-16 architecture. In the performance test stage, 700 normal and 140 TB chest x-ray images were used. The simulation results show that the proposed system can classify normal and TB lungs with an accuracy of 99.76%. The highest accuracy is achieved using batch size=50. This system is expected to assist radiology in detecting tuberculosis on X-Ray images of the lungs. The contribution of this study is to build a machine learning model for TB detection and optimization of model parameters to get the best accuracy.
Lung Sounds Classification Based on Time Domain Features Achmad Rizal; Istiqomah Istiqomah
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.v8i2.24007

Abstract

Signal complexity in lung sounds is assumed to be able to differentiate and classify characteristic lung sound between normal and abnormal in most cases. Previous research has employed a variety of modification approaches to obtain lung sound features. In contrast to earlier research, time-domain features were used to extract features in lung sound classification. Electromyogram (EMG) signal analysis frequently employs this time-domain characteristic. Time-domain features are MAV, SSI, Var, RMS, LOG, WL, AAC, DASDV, and AFB. The benefit of this method is that it allows for direct feature extraction without the requirement for transformation. Several classifiers were used to examine five different types of lung sound data. The highest accuracy was 93.9 percent, obtained Using the decision tree with 9 types of time-domain features. The proposed method could extract features from lung sounds as an alternative.
Anti-Forensics with Steganographic File Embedding in Digital Image Using Genetic Algorithm Amadeus Pondera Purnacandra; Subektiningsih Subektiningsih
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.v8i2.24208

Abstract

In this study, a steganography method on digital images as anti-forensics by utilizing genetic algorithms was proposed. Genetic Algorithms are artificial intelligence whose functions are optimization and search. The purpose of this research is to optimize steganography as anti-forensic by applying a Genetic Algorithm and combined with the Hilbert curve, lempel Ziv Markov chain, and least significant bit. The result provides a new steganography method by combining various existing methods. The proposed method will be tested for image quality using PSNR, SSIM, Chi-Squared steganalysis and RS-Analysis, and extraction test. The novelty obtained from the developed method is that the steganography method is as optimal as anti-forensic in keeping confidential data, has a large embedding capacity, and is able to be undetected using forensic methods. The results can maintain data confidentiality, have a large embedding capacity, and are able to be undetected using forensic methods. The proposed method got better performance rather than the previous method because PSNR and SSIM values are high, secret data can be received back as long as the pixel value doesn't change, and the size of the embedding capacity. The proposed method has more ability to embed various types of payload/ secret data because of the way it works, which splits byte files into binary. The proposed method also has the ability not to be detected when forensic image testing is carried out.
Shibboleth IdP for Single Sign-On with Kubernetes and Persistent Volume Longhorn Ikhwan Alfath Nurul Fathony; Mukhammad Andri Setiawan
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.v8i4.24272

Abstract

Many organizations do not use centralized user authorization with Single Sign-On (SSO) Management to seamlessly move from one system to another. The same thing also occurred at Universitas Islam Indonesia (UII). Students were having trouble login in from one web service to another. The Board of Information Systems of UII, or Badan Sistem Informasi (BSI), implements SSO to avoid this problem. However, after BSI implemented SSO on the virtual machine, it turned out that the server load became too high. A spiking number of user logins happened in a short period. The centralized system could not handle this. The research's solution is to use a clustered service using Shibboleth IdP. The Shibboleth IdP customization can be carried out to be deployed into the Kubernetes cluster infrastructure ecosystem to meet the needs of authentication login on the business processes at UII. The Shibboleth IdP itself will be equipped with a persistent storage longhorn to support and maintain the service and avoid a single point of failure. The Kubernetes and Persistent Volume Longhorn provide a redundancy function in an application and a more flexible replication process. Inside Kubernetes, there is containerization technology. It was used to optimize the server's resources instead of replicating the application using virtual machines. With the use of centralized login by Shibboleth IdP and persistent storage longhorn, the error because of server load could be minimized. The downtime of the downed services can also be reduced. The research also proves that using Kubernetes and Persistent Volume Longhorn could help the system by preventing a Single Point of Failure using its redundancy function.
Blockchain Technology Purwono Purwono; Alfian Ma'arif; Wahyu Rahmaniar; Qazi Mazhar ul Haq; Dimas Herjuno; Muchammad Naseer
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.v8i2.24327

Abstract

Blockchain came because of the occurrence of incredulity to single authorities by introducing the concept of network decentralization and data distribution saved in a ledger. Decentralization is used to validate discrepancies in the majority of data. The consensus mechanism collectively maintains the consistency of the ledger. A blockchain is a set of blocks containing transaction data interconnected to each other using the concept of cryptography. A mining process is an effort to add new blocks to the blockchain. The mining computer carries out the process after passing several complex mathematical problems. The fastest miner is rewarded with crypto coins. Some consensus mechanisms commonly used in blockchain are proof of work, proof of stake, practical byzantine fault tolerance, and proof of elapsed time. Blockchain network is designed and implemented in such a way that it can guarantee the security of its data, is easy to be audited, is robust to denial of service and majority attacks, and is private and confidential. The application of blockchain is not limited to finance systems; it can also be applied in health, education, supply chain, and state democracy systems.

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