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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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Saving Product Using Blockchain for E-BMT Platform Taufiq Gilang Adhitama; Anggunmeka Luhur Prasasti; Ali Fahmi Perwira Negara
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.v8i4.24753

Abstract

Baitul Maal Wa At Tamwil (BMT) is a sharia financial institution that provides savings and loan services in accordance with the social, cultural, and economic needs of rural communities, especially in agricultural and plantation communities. The current data management is still using manual recording and a centralized server which can cause fraudulent financial reports and creates a lack of credibility between BMT and its customers. The research method is to decentralize the application data system by using blockchain technology, then replacing the conventional database to blockchain system. The simulation shows that the e-BMT application are connected to blockchain network as intended, users can use metamask to interact with the Ethereum network, the blockchain implementation on e-BMT application has run according to expectations with a 100% success rate with the average transfer time on two devices of 9.47 seconds and 12.13 seconds. While the results of data entry time on two devices obtained an average of 9.96 seconds and 37.09 seconds. While the blockchain implementation on e-BMT could provide access to every user so that each entity could confirm the validity of the transactions, the size of the transactions, and other data recorded on the blockchain without having to develop an integrated database system. The research contributes in two aspects, first, we develop the distributed blockchain system using public Ethereum  blockchain network integrated with with popular e-wallet such as metamask, provides easy access for both customers and BMT parties who are connected to the network so that the recorded data can be accessed by anyone, and second, the application of blockchain technology to BMT is capable to interact with users as it is built on a website platform with RESTful API.
MongoDB Based Real-Time Monitoring Heart Rate Using Websocket For Remote Healthcare Emin Guney; Gamze Agirtas; Cuneyt Bayilmis
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.v8i4.25052

Abstract

With the gradual development of Industry 4.0, the internet of things (IoT) concept has become an even more current and fundamental study topic. Consisting of devices and objects with communication capability, IoT is a network that uses internet infrastructure, especially for data collection, display, decision-making, control, and optimization of processes. Recently, patient tracking systems have become even more critical with Covid19 and have diversified in health for IoT topics such as biomedical device tracking and disease diagnosis. Within the scope of this study, a prototype of a patient tracking system was developed over the sensor in order to contribute to the biomedical field. We aimed to observe real-time heart rates using WebSockets to demonstrate its use in the medical field via the web application. Monitoring the heart rate using a WebSocket can help doctors make faster and better diagnoses. The current technology study instantly collected the patient's heart rhythm with the pulse sensor. The pulse data collected in real time was then transferred to a web platform with the NodeMCU ESP 8266 board. With this platform, the patient was monitoring in real-time. With the opportunities provided by the study, the doctor implemented an application monitors the instantaneous pulse of the patients.
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.
Optimized PID-Like Neural Network Controller for Single-Objective Systems Gunawan Dewantoro; Johanes Nico Sukamto; Fransiscus Dalu Setiaji
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.v8i4.25237

Abstract

The utilization of intelligent controllers becomes more prevalent as the hype of Industry 4.0 arises. Artificial neural network (ANN) exhibits the mapping ability and can estimate the output by means of either interpolation or extrapolation. These properties are sought to supersede the classical controllers. In this study, the ANN establishment was initiated by collecting dataset from the input and output of a well-known PID controller. The dataset was trained using a set of control factor combinations, including the number of neurons, the number of hidden layers, activation functions, and learning rates. Two kinds of ANN controllers were investigated, including one-input and three-input ANN. The testing was conducted under normal and uncertain conditions. These uncertainties include external disturbances, plant variations, and setpoint variations. The integral absolute error (IAE) was selected as the single objective to assess. The simulation results show that the response of three-input ANN controllers could yield smaller IAE at their best combinations under most kinds of conditions. Besides, the three-input ANN outperforms the one-input ANN both qualitatively and quantitatively. These facts might lead to a broader utilization of ANN as controllers.
Low-Cost Active Monitoring of Attendance using Passive RFID Technology Wael A Farag; Mohamed Abouelela
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.v8i4.25168

Abstract

In this paper, a smart attendance system for students attending schools is proposed. The proposed attendance system is based on Radio Frequency Identification (RFID) technology to facilitate automation and convenience. The proposed RFID Attendance System (RFID-AS) should be used by school administration to ensure safety for students as well as using it for grading and evaluation purposes. After careful study, passive RFID technology is selected to be used by the proposed system for its reasonable cost. The main components of the system are an RFID tag, an RFID reader, Visual Studio (XAF Tool), and SQL Server to compare the data from the RFID tag with the students’ database to record attendance automatically. A Graphical User Interface (GUI) is developed using Visual Studio (XAF Tool) to allow parents and school faculty to log in and browse the students’ records. Students will pass the classroom door, which will have an integrated RFID reader device to read their RFID. The paper discusses the design of the solution as well as the testing scenarios.
Cheating Prevention in E-proctoring Systems Using Secure Exam Browsers: A Case Study Hussein Mahmood Mohammed; Qutaiba Ibrahem Ali
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.v8i4.25094

Abstract

In this research, a case study has been conducted to analyze the possibility of preventing cheating or reducing it by using one of the lockdown browsers during the exam. An e-exam has been created using Moodle platform, and the exam has been conducted with the Safe Exam Browser (SEB) as a restriction program at one time and without it at another time, and an analysis has been made of the extent of the possibility of cheating during the exam for both cases. Wireshark and Registry Changes View programs have been used to observe the possibility of opening programs and applications or the ability of the examinee to use Windows tools during the exam. The use of Wireshark and Registry Changes View software showed high effectiveness in analyzing the examinee's device data and identifying the examinee's activity during the electronic exam, to give a clear perception of the possibility of preventing access to resources and applications on the examinee's device. The researchers concluded that the use of lockdown browsers is very necessary to prevent the examinee from accessing the resources on his device, which leads to a significant reduction in cheating during the electronic exam. The research contributions are two, the first one is the use of analyzing programs to observe the examinee`s activity during the exam, and the second one is presenting the lockdown browsers` features.
A Deep Neural Network Model for Realtime Semantic-Segmentation Video Processing supported to Autonomous Vehicles Trung-Nguyen Bui; Hanh Phan-Xuan; Thuong Le-Tien
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.v8i4.25120

Abstract

Traffic congestion has been a huge problem, especially in urban area during peak hours, which causes a major problem for any unmanned/autonomous vehicles and also accumulate environmental pollution. The solutions for managing and monitoring the traffic flow is challenging that not only asks for performing accurately and flexibly on routes but also requires the lowest installation costs. In this paper, we propose a synthetic method that uses deep learning-based video processing to derive density of traffic object over infrastructure which can support usefull information for autonomous vehicles in a smart control system. The idea is using the semantic segmentation, which is the process of linking each pixel in an image to a class label to produce masked map that support collecting class distribution among each frame. Moreover, an aerial dataset named Saigon Aerial with more than 110 samples is also created in this paper to support unique observation in a biggest city in Vietnam, HoChiMinh city. To present our idea, we evaluated different semantic segmentation models on 2 datasets: Saigon Aerial and UAVid. Also to track our model’s performance, F1 and Mean Intersection over Union metrics are also taken into account. The code and dataset are uploaded to Github and Kaggle repository respectively as follow: Saigon Aerial Code, Saigon Aerial dataset.
Fiber Optic Attenuation Analysis Based on Mamdani Fuzzy Logic in Gambir Area, Central Jakarta Yuliza Yuliza; Ninda Sari; Rachmat Muwardi; Lenni Lenni; Yosy Rahmawati
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.v8i4.24549

Abstract

In this study, the authors conducted an analysis of the quality of fiber optic network maintenance based on attenuation value and maintenance time using fuzzy Mamdani logic and simulated using Matlab software, to improve accuracy in drawing conclusions on maintaining quality. This study uses a quantitative method, in which the author obtains a summary of customer data from PT. Telkom Indonesia in a period of 4 months of observation from August to November 2021. In August there were 776 customers, in September there were 362 customers, in October there were 359 customers, and in November 445 customers who underwent Indihome fiber optic cable maintenance. The test results with the centroid method with an input Handling Time of 1.5 hours and an Attenuation of 15 dB, then the output Repair Quality is 5.5 or categorized as Good. The greater the attenuation value generated, the more time it takes to maintain the IndiHome internet network disturbance. This is due to the many technical maintenance of fiber optic cables carried out by technicians to adjust for damage/trouble in the field. It is expected that maintenance can be carried out routinely in order to avoid fatal internet disturbances on the customer's side, and maximize maintenance time according to the dosage determined by the company, which is less than 3 hours, taking into account the work performance of technicians and also the quality of maintenance.
Export Commodity Price Forecasting in Indonesia Using Decision Tree, Random Forest, and Long Short-Term Memory Shadifa Auliatama Harjanto; Siti Sa'adah; Gia Septiana Wulandari
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.v8i4.25242

Abstract

Gross Domestic Product (GDP) is an indicator that becomes a benchmark for a country's economic performance. One of the factors that significantly affect GDP is export activity. However, the problem that occurs is that the export value is relatively fluctuating, this is because commodity prices are always changing every time. Therefore, we need a system that can predict commodity prices accurately. It is hoped that this system can help the government to make appropriate export policies based on predictions of commodity prices in the future. The contribution of this study is to compare Decision Tree, Random Forest, and Long Short-Term Memory (LSTM) performance in forecasting several export commodities in Indonesia. In this study, the commodities forecasted are the main commodities from each sector that dominates exports in Indonesia, namely palm oil from the manufacturing sector, coffee from the agricultural sector, and coal from the mining sector. The experiments in this study were conducted by testing several hyperparameters of each method to determine the best model. The performance of models is measured using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that LSTM has the lowest error among Decision Tree and Random Forest with MAPE of 0.121, 0.494, and 0.282 in forecasting coal, coffee, and palm oil price respectively. Therefore, LSTM has proven to be the best method among Random Forest and Decision Tree in forecasting export commodity prices in Indonesia.
Recognition of Balinese Traditional Ornament Carving Images with Convolutional Neural Network and Discrete Wavelet Transform Ni Luh Putu Kurniawati; Made Windu Antara Kesiman; I Made Gede Sunarya
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.v8i3.24360

Abstract

Balinese carvings are less known to the public due to the lack of information about Balinese carvings. Minimum information about Balinese carvings can be overcome by utilizing advances in information technology in the field of image processing, namely the introduction of Balinese carving patterns. In the pattern recognition model of an image, there are several things that can be analyzed, such as the recognition method used, feature extraction, including the model in preprocessing to reduce noise in a Balinese carving image. In this study, the Convolutional Neural Network (CNN) was used to classify Balinese carving images combined with Discrete Wavelet Transform (DWT) in extracting image features. The introduction was made to 25 categories of Balinese carving ornaments. Tests are generated based on the level of accuracy generated in the testing process. Analysis of the results was carried out on the resulting model, namely the analysis of the combination of CNN with DWT and without DWT. Testing the data set with 212 training data and 129 testing data using all DWT channels. Based on the results of the tests that have been carried out, it is found that using the DWT extraction feature produces a higher testing accuracy value, namely 35.66% for 25 classes and 74, 42% for 3 carving classes. Meanwhile, without using DWT, it produces an accuracy value of 32.56% for 25 classes and 66.67% for 3 carving classes. In future research, it is hoped that there will be an improvement in the data set and good shooting with a balanced and adequate number for the 25 carving classes that have been obtained.

Page 23 of 51 | Total Record : 505

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