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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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Design of Fir Digital Bandpass Filter with Hamming Window and Hanning Window Method for Fetal Doppler Hartono Siswono; Widyastuti Widyastuti; Yohanes Dovan; Dyah Nur’ainingsih
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26849

Abstract

DESIGN OF FIR DIGITAL BANDPASS FILTER WITH HAMMING WINDOW AND HANNING WINDOW METHOD FOR FETAL DOPPLER
Development of Customer Loyalty Measurement Application Using R Shiny with Structural Equation Model Partial Least Square Method, Customer Satisfaction Index, and Customer Loyalty Index Cintika Oktavia; Budi Warsito; Vincensius Gunawan Slamet Kadarrisman
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26649

Abstract

One of Indonesia's well-known e-commerce platforms, Shopee, relies on information technology to run its business. The information technology used by Shopee is considered unable to meet customer satisfaction. Customer reviews are dissatisfied with the facilities provided by Shopee, and some customers compare Shopee with other e-commerce sites. The research contribution is the understanding that the proper use of information technology can positively impact customer experience, improve operational efficiency, and support business growth in the e-commerce industry. Research with a quantitative approach will build a website-based application as a statistical tool for data processing using R shiny so that the application results have high interactivity, dynamic visualization, and better explanation. The research will collect 100 data provided to customers who have transacted at Shopee and distributed through the telegram application, which is distributed to particular groups and channels for Shopee users. Data processing for this study will use the  Structural Equation Model Partial Least Square, Customer Satisfaction Index, Net Promoter Score, and Customer Loyalty Index. The study results show that electronic service quality and security seals positively and significantly affect customer satisfaction. Electronic service quality has a moderate effect on customer satisfaction, while electronic security seals have a slightly lower effect on customer satisfaction (t=5.584, p<0.001). Additionally, a significant correlation between customer loyalty and satisfaction was discovered (t=14.764, p=0.001). Research proves the need to improve service quality and security aspects to increase customer satisfaction on e-commerce platforms and the importance of maintaining customer satisfaction as a strategy to increase customer loyalty.
New Generation Indonesian Endemic Cattle Classification: MobileNetV2 and ResNet50 Ahmad Fikri; Aniati Murni
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26659

Abstract

Cattle are an essential source of animal food globally, and each country possesses unique endemic cattle races. However, categorizing cattle, especially in countries like Indonesia with a large cattle population, presents challenges due to costs and subjectivity when using human experts. This research utilizes Computer Vision (CV) for image data classification to address this urgent need for automatic categorization. The main objective of this study is to develop a mobile-friendly model using CV techniques that can accurately detect and classify Indonesian endemic cattle races, such as Limosin, Madura, Pegon, and Simental. To achieve this, an object localization approach is employed to extract multiple features from distinct regions of each cattle image, including the head, ear, horn, and muzzle areas. The authors evaluate two CV algorithms, ResNet50 and MobileNetV2, to assess their performance in cattle race classification. The dataset used is facial photos of 147 cows. The results indicate that ResNet50 outperforms MobileNetV2, achieving a training data accuracy of 83.33% compared to MobileNetV2's 77.08%. Moreover, the validation accuracy of ResNet50 (76.92%) significantly surpasses MobileNetV2's (38.46%). The novel contribution of this research lies in developing a cost-effective and efficient solution for identifying endemic cattle breeds in Indonesia. The mobile-friendly model based on ResNet50 demonstrates superior accuracy, enabling cattle farmers and researchers to categorize cattle races with higher precision, reducing manual effort, and minimizing costs. In conclusion, this research provides a valuable advancement in automatic cattle categorization using CV techniques. By offering a practical and accurate model that considers Indonesia's specific cattle breeding conditions, this study contributes to the sustainable management and conservation of endemic cattle races while enhancing the efficiency of cattle farming practices.
Performance and Emissions of Nanoadditives in Diesel Engine: A review Nouby M. Ghazaly; Ahmed N. Abdulhameed
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27271

Abstract

Nowadays, the demand for energy and fossil fuels has widely increased as a result of the continuous growth of the population. However, the continued use of traditional fuels as the primary source of energy has resulted in various environmental challenges related to climate change and global warming. This has prompted researchers to look for more eco-friendly and sustainable fuel alternatives with a minimal amount of engine modification and emission treatment techniques. Amongst the suggested alternative fuels, biofuels, biofuel/diesel blends, and the incorporation of nanoparticles into fuels. The nanoparticle diesel additives played a vital role in increasing engine performance as well as retarding harmful emissions such as nitrogen oxides (NOx), carbon monoxide (CO), unburned hydrocarbon (UHC), and particulate matter (PM). Metal-oxides nanoadditive such as aluminum oxide (Al2O3), ceric oxide (CeO2), and titanium dioxide (TiO2) act as oxygen catalysts and promote proper mixing of fuel and air, resulting in more efficient combustion and decreased emissions. The incorporation of nanometal-based additives, including iron (Fe), copper (Cu), and aluminum (Al) accelerated the fuel evaporation rate and increased the probability of fuel ignition. Carbon-based nanoparticles such as carbon nanotubes (CNTs), graphene nanoplatelets (GNPs), and graphene oxide (GO) are promising fuel nanoadditives owing to their metal-free composition. In addition, carbon-based additives enhanced the thermal conductivity of fuel and increased active sites available for chemical reactions, which led to improved engine performance.
Fast Human Recognition System on Real-Time Camera Yuliza Yuliza; Rachmat Muwardi; Mustain Rhozaly; Lenni Lenni; Mirna Yunita; Galatia Erica Yehezkiel
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27009

Abstract

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can get many benefits by implementing this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is in the area of safety, namely in the field of searching for people, which is still done manually. Often search teams find it challenging to find people because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the You only look once (YOLO) method with the YoloV4-Tiny type, where this algorithm has high detection speed and accuracy. Using the YOLOV4-Tiny simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.
Validation of Varian Clinac iX Model on 6 MV Photon Beam Using Fast Monte Carlo Simulation Josua Timotius Manik; Anisza Okselia; Daniel Gibbor Gaspersz; Freddy Haryanto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27075

Abstract

Monte Carlo (MC) is widely recognized as the most accurate method for dosimetry analysis in radiotherapy due to its precision. However, successful MC dose calculation hinges upon the validation of the linac model employed in simulations. This study aims to verify the PRIMO model of the Varian Clinac iX and to determine the optimal initial electron energy. The comparison of one-dimensional dose distribution between simulations and measurements serves as the foundation for assessment. The Varian Clinac iX on 6 MV photon beam was meticulously modeled with the initial electron energies spanned from 5.2 to 5.8 MeV in increments of 0.2 MeV. The dose calculation were performed for a field size of 10 cm × 10 cm and a source-to-surface distance (SSD) of 100 cm. The Dose Planning Method (DPM) was adopted as the simulation engine for expedited MC simulation. A number of particle histories–approximately 4.0 × 108–were simulated, resulting in the generation of around 109 particles from the linac head. The investigation revealed that an initial electron energy of 5.8 MeV achieves good agreement with measurement by attaining the smallest difference in percentage depth dose (PDD) of about 0.98%. The lateral dose deviation of approximately 4.63% serves to validate the precision of the secondary collimator design. Additionally, a comparative analysis of DPM and PENELOPE for dose calculation was conducted. In contrast to the PENELOPE, the DPM speeds up simulation time by approximately 3.5 times, reduced statistical uncertainties to 0.59% and afford better accuracy in dose calculation. The result underscore the suitability of the PRIMO model for MC simulation for dose calculation, given its robust agreement with the measurements.
Multimedia Forensic Analysis of TikTok Application Using National Institute of Justice (NIJ) Method Rachmad Nur Fauzi; Nuril Anwar
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26924

Abstract

The advancement of technology, especially in mobile devices like smartphones, has had a significant impact on human life, particularly during the COVID-19 pandemic, leading to the growth of online activities, especially on social media platforms like TikTok. TikTok is a highly popular social media platform, primarily known for its focus on short videos and images often accompanied by music. However, this has also opened up opportunities for misuse, including the spread of false information and defamation. To address this issue, this research utilizes mobile forensic analysis with Error Level Analysis (ELA) to collect digital evidence related to crimes on TikTok. This research contributes by applying digital forensic techniques, specifically Error Level Analysis (ELA), to detect image manipulation on TikTok. By using forensic methods, this research helps uncover digital crimes occurring on TikTok and provides essential insights to combat misuse and criminal activities on this social media platform. The research aims to collect digital evidence from TikTok on mobile devices using MOBILedit Forensic Express Pro and authenticate it with ELA through tools like FotoForensics and Forensically, as well as manual examination. This research follows the National Institute of Justice (NIJ) methodology with ten stages of mobile forensic investigation, including scenario creation, identification, collection, investigation, and analysis. The research yields manipulated digital evidence from TikTok, primarily concerning upload times. Error Level Analysis (ELA) is used to assess the authenticity of images, revealing signs of manipulation in digital evidence. The research's contribution is to produce or collect manipulated digital evidence from TikTok, primarily concerning upload times, and to apply the Error Level Analysis (ELA) approach or technique to assess the authenticity of images, uncovering signs of manipulation in digital evidence.
Double Face Masks Detection Using Region-Based Convolutional Neural Network Sa&#039;aadah Sajjana Carita; Raden Budiarto Hadiprakoso
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.23902

Abstract

Because of the fast spread of coronavirus, the globe is facing a significant health disaster of COVID-19. The World Health Organization (WHO) released many suggestions to combat the spread of coronavirus. Wearing a face mask in public places and congested locations is one of the most effective preventive practices against COVID-19. However, according to recent research wearing double face masker even provide better protection than just one mask. Based on this finding, various public places require double masks to proceed more. It is pretty tricky to monitor individuals in crowded public places personally. Therefore, a deep learning model is suggested in this paper to automate recognizing persons who are not wearing double face masks. A faster region-based convolutional neural network model is developed using the picture augmentation approach and deep transfer learning to increase overall performance. We apply deep transfer learning by fine-tuning the low level pre-trained Visual Geometry Group (VGG) Face2 model. This [CR1] study used the publicly accessible VGGFace2 dataset and the self-processed dataset. The findings in this study show that deep transfer learning and image augmentation can increase detection accuracy by up to 11%. Consequently, the created model achieves 93.48% accuracy and 93.19% F1 score on the validation dataset, demonstrating its excellent performance. The test results show the proposed model for further research by adding the predicted dataset and class.
The Impact of Vegetation on the Performance of Polycrystalline and Monocrystalline Silicon Photovoltaic Modules Julius Tanesab; Monalisa Malelak; Marthen Beily; Irvan Helle
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26911

Abstract

The performance of a photovoltaic (PV) module decreases with increasing temperature. An emergent method developed to reduce temperature rise is vegetation that refers to cultivating crops under the shade of PV modules. This study aims to investigate the impact of caisim (brassica chinensis var. parachinensis), a popular tropical vegetable, on the performance of two polycrystalline silicon (pc-Si) and two monocrystalline silicon (mc-Si) PV modules. Initially, electrical parameters, solar irradiation, and temperature of the four PV modules were examined without vegetation. Furthermore, the same management was repeated with a treatment of two PV modules (pc-Si 1 and mc-Si 1) were vegetated and the other two modules (pc-Si 2 and mc-Si 2) were designated as reference modules, left without vegetation. Results of the experiments carried out in clear sunny days and analyzed with a least squares method revealed that, for the modules of the same technology, the efficiency of pc-Si 1 (vegetated) was higher than pc-Si 2 (reference), whereas mc-Si 2 (reference) outperformed mc-Si 1 (vegetated). Test results on mc-Si technology indicated that there was no contribution of vegetation to lowering temperature of the vegetated PV module, thereby failing to improve its efficiency. This might be related to the design and material of the mc-Si modules which support conductive heat losses. The conduction effect seemed to be more dominant than the evapotranspiration impact which may be low due to the wind and the greater distance between the vegetation and the modules. The results of this research imply that it is necessary to consider the application of vegetation for pc-Si technology for the design and optimization of the performance of solar power plants in Kupang, Indonesia. This research contributes to shining a light on the intricate relationship between PV module performance and vegetation. In a broader scope, this study provides a motivation for future investigations regarding efforts to overcome land competition to produce energy and food.
Modeling Human Mobility by Train on the Spread of COVID-19 in East Java Province Using Distance-Decay PageRank Algorithm Rizha Al-Fajri; Medria Kusuma Dewi Hardhienata; Yeni Herdiyeni
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27285

Abstract

Since early 2020, the world has been dealing with the COVID-19 outbreak. A person who has been infected with COVID-19 has the potential to transmit the virus to others. This study aims to model human mobility by train using the spatial network in East Java Province. This research examines the relationship between human mobility by train and the spread of COVID-19 in East Java Province. The spatial network is formed based on train stations and train trips, and the model was created using the Distance-decay PageRank algorithm. This research has modeled human mobility using the train in East Java Province. The result shows that human mobility by train is highly correlated with the spread of COVID-19 in East Java Province, with a correlation coefficient of 0.7 (r = 0.7).Since early 2020, the world has been dealing with the COVID-19 outbreak. A person who has been infected with COVID-19 has the potential to transmit the virus to others. This study aims to model human mobility by train using the spatial network in East Java Province. This research examines the relationship between human mobility by train and the spread of COVID-19 in East Java Province. The spatial network is formed based on train stations and train trips, and the model was created using the Distance-decay PageRank algorithm. This research has modeled human mobility using the train in East Java Province. The result shows that human mobility by train is highly correlated with the spread of COVID-19 in East Java Province, with a correlation coefficient of 0.7 ( = 0.7).

Page 31 of 51 | Total Record : 505

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