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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 29 Documents
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Search results for , issue "Vol. 9 No. 4 (2023): December" : 29 Documents clear
Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM) Saputra, Heru; Stiawan, Deris; Satria, Hadipurnawan
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.v9i4.27559

Abstract

Portable Document Format (PDF) files as well as files in several other formats such as (.docx, .hwp and .jpg) are often used to conduct cyber attacks. According to VirusTotal, PDF ranks fourth among document files that are frequently used to spread malware in 2020. Malware detection is challenging partly because of its ability to stay hidden and adapt its own code and thus requiring new smarter methods to detect. Therefore, outdated detection and classification methods become less effective. Nowadays, one of such methods that can be used to detect PDF files infected with malware is a machine learning approach. In this research, the Support Vector Machine (SVM) algorithm was used to detect PDF malware because of its ability to process non-linear data, and in some studies, SVM produces the best accuracy. In the process, the file was converted into byte format and then presented in Byte Frequency Distribution (BFD). To reduce the dimensions of the features, the Sequential Forward Selection (SFS) method was used. After the features are selected, the next stage is SVM to train the model. The performance obtained using the proposed method was quite good, as evidenced by the accuracy obtained in this study, which was 99.11% with an F1 score of 99.65%. The contributions of this research are new approaches to detect PDF malware which is using BFD and SVM algorithm, and using SFS to perform feature selection with the purpose of improving model performance. To this end, this proposed system can be an alternative to detect PDF malware.
Double Face Masks Detection Using Region-Based Convolutional Neural Network Carita, Sa'aadah Sajjana; Hadiprakoso, Raden Budiarto
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 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.
Development of Customer Loyalty Measurement Application Using R Shiny with Structural Equation Model Partial Least Square Method, Customer Satisfaction Index, and Customer Loyalty Index Oktavia, Cintika; Warsito, Budi; Kadarrisman, Vincensius Gunawan Slamet
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 Fikri, Ahmad; Murni, Aniati
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.
Design of Fir Digital Bandpass Filter with Hamming Window and Hanning Window Method for Fetal Doppler Siswono, Hartono; Widyastuti, Widyastuti; Dovan, Yohanes; Nur’ainingsih, Dyah
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

Fetal heart rate using Doppler Ultrasound is a standard method to assess fetal health. Examination of the fetal heart with a Doppler device is more convenient for women. Fetal Doppler can accidentally take the mother's heartbeat. A filter is needed to enhance the audibility of fetal heartbeats while suppressing unwanted frequencies and noise. The normal fetal heart rate ranges from 120 to 160 beats per minute, or 2 Hz - 3 Hz. This frequency can be filtered using a bandpass filter. the digital FIR bandpass filter were created using the Hamming and Hanning window methods. The results of the FIR filter with the Hamming and Hanning window, Orde 100 Hanning gave the best frequency bandwidth range which was 1.833 Hz to 3.167 Hz. Orde 20 Hamming and Hanning had the shortest delay +- 2 s and Orde 100 Hamming and Hanning had the longest delay +- 6s. For the noise at 1.6 Hz, Orde 100 Hamming and Orde 100 Hanning the signal level of the signal output is the same as the desired signal level. For the noise at 3.1 Hz, Orde 100 Hamming and Orde 100 Hanning had the signal level of the signal output is almost the same as the desired signal level. At the frequency point of 1.6 Hz, the noise signal at the input has a magnitude response 2533, it is a decrease after passing through the filter to = 0. At the frequency point of 3.1 Hz, the noise signal at the input has a magnitude response 2246, and there is a decrease after passing through the filter to 167.7. From this study, we can choose 100 orde Hanning because it gave the best frequency bandwidth range which was 1.833 Hz to 3.167 Hz, the delay of +- 6s.
Gaussian Based-SMOTE Method for Handling Imbalanced Small Datasets Misdram, Muhammad; Noersasongko, Edi; Purwanto, Purwanto; Muljono, Muljono; Pamuji, Fandi Yulian
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.v9i4.26881

Abstract

The problem of dataset imbalance needs special handling, because it often creates obstacles to the classification process. A very important problem in classification is to overcome a decrease in classification performance. There have been many published researches on the topic of overcoming dataset imbalances, but the results are still unsatisfactory. This is proven by the results of the average accuracy increase which is still not significant. There are several common methods that can be used to deal with dataset imbalances. For example, oversampling, undersampling, Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, Adasyn, Cluster-SMOTE methods. These methods in testing the results of the classification accuracy average are still relatively low. In this research the selected dataset is a medical dataset which is classified as a small dataset of less than 200 records. The proposed method is Gaussian Based-SMOTE which is expected to work in a normal distribution and can determine excess samples for minority classes. The Gaussian Based-SMOTE method is a contribution of this research and can produce better accuracy than the previous research. The way the Gaussian Based-SMOTE method works is to start by determining the random location of synthesis candidates, determining the Gaussian distribution. The results of these two methods are substituted to produce perfect synthetic values. Generated synthetic values are combined with SMOTE sampling of the majority data from the training data, produce balanced data. The result of the balanced data classification trial from the influence of the Gaussian Based SMOTE result in a significant increase in accuracy values of 3% on average.
The Impact of Vegetation on the Performance of Polycrystalline and Monocrystalline Silicon Photovoltaic Modules Tanesab, Julius; Malelak, Monalisa; Beily, Marthen; Helle, Irvan
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.
Multimedia Forensic Analysis of TikTok Application Using National Institute of Justice (NIJ) Method Fauzi, Rachmad Nur; Anwar, Nuril
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.
Intellectual System Diagnostics Glaucoma Ichhpujani, Parul; Ryabtsev, Vladimir; Utkina, Tetyana Yuriyivna
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.v9i4.26969

Abstract

Glaucoma is a chronic eye disease that can lead to permanent vision loss. However, glaucoma is a difficult disease to diagnose because there is no pattern in the distribution of nerve fibers in the ocular fundus. Spectral analysis of the ocular fundus images was performed using the Eidos intelligent system. From the ACRIMA eye image database, 90.7% of healthy eye images were recognized with an average similarity score of 0.588 and 74.42% of glaucoma eye images with an average similarity score of 0.558. The reliability of eye image recognition can be achieved by increasing the number of digitized parameters of eye images obtained, for example, by optical coherence tomography. The research contribution is the digital processing of fundus graphic images by the intelligent system “Eidos”. The scientific contribution lies in the automation of the glaucoma diagnosis process using digitized data. The results of the study can be used at medical faculties of universities to carry out automated diagnostics of glaucoma.
Fast Human Recognition System on Real-Time Camera Yuliza, Yuliza; Muwardi, Rachmat; Rhozaly, Mustain; Lenni, Lenni; Yunita, Mirna; Yehezkiel, Galatia Erica
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.

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