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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 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.
A Hybrid Genetic Algorithm-Random Forest Regression Method for Optimum Driver Selection in Online Food Delivery Putrada, Aji Gautama; Alamsyah, Nur; Oktaviani, Ikke Dian; Fauzan, Mohamad Nurkamal
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.v9i4.27014

Abstract

The online food delivery trend has become rapid due to the COVID-19 incident, which limited mobility, while the broader challenge in the online food delivery system is maximizing quality of service (QoS). However, studies show that driver selection and delivery time are important in customer satisfaction. The solution is our research aim, which is the selection of optimal drivers for online food delivery using random forest regression and the genetic algorithm (GA) method. Our research contribution is a novel approach to minimizing delivery time in online food delivery by combining a random forest regression model and genetic algorithms. We compare random forest regression with three other state-of-the-art regression models: linear regression, k-nearest neighbor (KNN), and adaptive boosting (AdaBoost) regression. We compare the four models with metrics including , mean squared error (MSE), root mean squared error (RMSE), mean total error (MAE), and mean absolute percentage error (MAPE). We use the optimum model as the fitness function in GA. The test results show that random forest performs better than linear, KNN, and AdaBoost regression, with an , RMSE, and MAE value of 0.98, 54.3, and 11, respectively. We leverage the optimum random forest regression model as the GA fitness function. The best efficiency is reducing the delivery time from 54 to 15 minutes, achieved through rigorous testing on various cases. In addition, by completing this research, we also achieve some practical implications, such as an increase in customer satisfaction, a reduction in cost, and a paramount finding in the field of data-driven decision-making. The first key finding is an optimum driver selection model in random forest regression, while the second is an optimum driver selection model in GA.
Validation of Varian Clinac iX Model on 6 MV Photon Beam Using Fast Monte Carlo Simulation Manik, Josua Timotius; Okselia, Anisza; Gaspersz, Daniel Gibbor; Haryanto, Freddy
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.
Development of SLOC, CC, SQL Complexity Methods to Measure the Level of Similarity Complexity of Software Modules Subali, Made Agus Putra; Sugiartha, I Gusti Rai Agung; Putra, I Putu Aditya
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.v9i4.27150

Abstract

Software metrics are often used to reflect vulnerabilities in program code to measure the complexity of each software module. Knowing the complexity of each software module is an important thing to do because the project manager can analyze defects that may occur, costs spent, work schedules, and the resources needed. In this research, we aim to apply the SLOC, CC, SQL Complexity method in measuring the level of similarity of complexity between software modules by paying attention to the level of similarity of the syntactic structure of program logic and SQL commands, by knowing the similarity between software modules the project manager can predict the effort required. Based on the results of the level of equality for the eight modules, an average of 90% was obtained. The high results are due to the third feature used having a high level of similarity. In further research, other features will be added and weighting will be given to each feature.
Development of Simple Control System for a Negative Pressure Wound Therapy Device Davida, Angga; Basari, Basari
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.v9i4.27152

Abstract

Diabetic ulcers are wounds found on the legs of diabetic patients. Improper treatment opens the risk of complications like sepsis and osteomyelitis. A notable method of treatment is through a Negative Pressure Wound Therapy (NPWT) device. This device helps ulcer recovery by removing exudate, increasing blood flow, and promoting cellular proliferation via negative pressure. The objective of this study is to increase the local content of an affordable and effective method of diabetic ulcer therapy by developing a simple, low-cost NPWT prototype. This was achieved by using an Arduino UNO microcontroller, which included PID controls, an MPXV4115VC6U sensor reading function, an in-built timer, two modes, and an alarm system. The resulting prototype was calibrated before testing to reduce error rates. Testing was conducted using a Gas Flow Analyzer and an ulcer wound phantom. Negative pressure settings of 75, 85, and 125 mmHg were used for testing and were conducted on both modes for 30 minutes each. From these tests, it was found that the prototype could reach the negative pressure thresholds with minimal average error of at most -1.81%. With a wound phantom, the average error was -0.56% and -0.20% for the continuous and intermittent modes respectively. This small variance is negligible because NPWT therapy has a wide range of acceptable negative pressure, namely 60-80 mmHg and 80-125 mmHg, depending on wound type. In conclusion, a simple Arduino UNO-based system can function as an NPWT therapy device to aid diabetic ulcer recovery with minimal error.

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