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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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Eidos System Prediction of Myopia in Children in Early Education Stages Al-Ansi, Abdullah M.; Almadi, Mudar; Ichhpujani, Parul; Ryabtsev, Vladimir
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26292

Abstract

This study used a database containing factors that, when processed using the Eidos intellectual system, detect myopia in children of primary school age. The database includes parameters that take into account the properties of the visual system, as well as factors that determine the duration of the performance of the main functions of the cognitive and entertaining nature of the students. The results obtained allow us to determine those factors that are more conducive to the appearance of myopia. The negative impact of some factors that cause myopia can be removed, such as, limiting the screen time spent, increasing outdoor activities/sports. A retrospective training sample can be used for automated processing using the Eidos intellectual system of the results obtained during the preventive examination of schoolchildren by an ophthalmologist. Early intervention towards myopia management in students, improves the chances of maintaining vision and slows myopia progression. The contribution of this research includes factors of a social nature that could be influenced at school in the process of education, increasing the attention towards childent, awareness of maintaining vision and slows down the progression of myopia.
Design of a Laboratory Scale Archemedes Screw Turbine Model Hydroelectric Power Station (PLTA) Simulator Basri, Muhammad Hasan; Muhtadi, Ahmad; Hasan, Darul
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26309

Abstract

The purpose of this research is to design a new model simulator of the Archimedes Screw turbine on a laboratory scale which is simple, inexpensive, environmentally friendly and for practice at the Electrical Engineering Laboratory of Nurul Jadid University by studying the efficiency of the Archimedes turbine which utilizes kinetic energy. water flow energy from the difference in upstream-downstream water head. Methods used numerical simulations have been run to evaluate the performance coefficient of the turbine alone (without friction loss or blockage augmentation), and to extend the TSR range. Numerical simulations make it possible to generate efficiency curves of Archimedes Screw turbines in both parallel and inclined configurations. The result obtained is that the proposed geometry can be used in real-life applications, providing 0.5 kW at flow velocities between 1 and 2 m/s. Novelty of hydropower simulation studies of the Archimedes turbine screw model using numerical simulation methods.
Sentiment Analysis of Customers’ Review on Delivery Service Provider on Twitter Using Naive Bayes Classification Basuki, Ari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26327

Abstract

Customer evaluations on social media may help us remain competitive and comprehend our business's target market. By analysing consumer evaluations, a business owner can identify common themes, pain points, and desired features or enhancements.  By analysing customer feedback across multiple channels, such as social media, online reviews, and customer service interactions, businesses can rapidly identify any negative sentiment or potential brand damage. The contribution of our study is to evaluate the performance of the Naive Bayes method for classifying customer feedback on courier delivery services obtained via Twitter. The Naive Bayes algorithm is selected due to its simplicity, which facilitates efficient computation, suitability for large datasets, outstanding performance on text classification, and ability to manage high-dimensional data. In this investigation, the Naive Bayes classifier accuracy is 0.506, which is considered to be low.  According to our findings, the irrelevant feature classification resulting in an error throughout the categorization process. A large number of data appearance characteristics that do not correspond to the testing data category have been identified as a result of this occurrence.
Experimenting with the Hyperparameter of Six Models for Glaucoma Classification Ilham, Muhammad; Prihantoro, Angga; Perdana, Iqbal Kurniawan; Magdalena, Rita; Saidah, Sofia
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26331

Abstract

Glaucoma, being one of the leading causes of blindness worldwide, often presents without noticeable symptoms, making early detection crucial for effective treatment. Numerous studies have been conducted to develop glaucoma detection systems. In this particular study, a glaucoma detection system using the CNN method was developed. The models employed in this study include AlexNet, Custom Layer, MobileNetV2, EfficientNetV1, InceptionV3, and VGG19. For training, an augmented RIM-ONE DL dataset was utilized. Hyperparameter experiments were conducted to determine the most optimal parameters for each model, specifically testing batch size, learning rate, and optimizer. The hyperparameter optimization process yielded the optimal parameters for each model. However, it is important to note that the MobileNetV2, InceptionV1, and VGG19 models exhibited signs of overfitting in the training graph results. Among the models, the custom layer model achieved the highest accuracy of 93%, while InceptionV3 attained the lowest accuracy at 83.5%. Testing of the models was performed using data from Cicendo Eye Hospital and the RIM-ONE DL testing dataset. Based on the testing results, it was found that InceptionV3 outperformed the other models in predicting images accurately. Therefore, the study concluded that high accuracy in training does not necessarily indicate superior performance in testing, particularly when limited variation exists in the training dataset.
Strawberry Plant Diseases Classification Using CNN Based on MobileNetV3-Large and EfficientNet-B0 Architecture Pramudhita, Dyah Ajeng; Azzahra, Fatima; Arfat, Ikrar Khaera; Magdalena, Rita; Saidah, Sofia
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26341

Abstract

Strawberry is a plant that has many benefits and a high risk of being attacked by pests and diseases. Diseases in strawberry plants can cause a decrease in the quality of fruit production and can even cause crop failure. Therefore, a method is needed to assist farmers in identifying the types of diseases in strawberry plants. Currently, there are many methods to assist farmers in identifying types of disease in plants, including strawberry plants. In this study, a system is proposed to be able to detect strawberry plant diseases by classifying the disease based on healthy and diseased strawberry leaf images. The proposed system is the Convolutional Neural Network (CNN) algorithm using MobileNetV3-Large and EfficientNet-B0 models to train pre-processed datasets. The results of this study obtained the best accuracy reaching 92.14% using the MobileNetV3-Large architecture with the hyperparameter optimizer RMSProp, epochs 70, and learning rate 0.0001. The percentage of the evaluation model using MobileNetV3-Large for precision, recall, and F1-Score achieved 92.81%, 92.14%, and 92.25%.  Whereas in the EfficientNet-B0 architecture, the best accuracy results only reach 90.71% with the hyperparameter optimizer Adam, 70 epochs, and a learning rate of 0.003. Then, the precision, recall, and F1-scores for EfficientNet-B0 reached 92.65%, 90.00%, and 90.37%. Overall, it presents fairly good results in classifying strawberry leaf plant disease. Furthermore, in future work, it needs to obtain higher accuracy by generating more datasets, trying other augmentation techniques, and proposing a better model.
Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory Halim, Kevin Yudhaprawira; Nugrahadi, Dodon Turianto; Faisal, Mohammad Reza; Herteno, Rudy; Budiman, Irwan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26354

Abstract

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification. 
Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction Nikmah, Tiara Lailatul; Jumanto, Jumanto; Prasetiyo, Budi; Fitriani, Nina; Muslim, Much Aziz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26366

Abstract

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.
Aspect-Based Sentiment Analysis from User-Generated Content in Shopee Marketplace Platform Cahyani, Andharini Dwi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.26367

Abstract

A number of businesses, such as TripAdvisor, Open Table, and Yelp, have successfully utilized aspect-based sentiment analysis in order to gain insights from reviews provided by customers and enhance the quality of their goods or services.  Businesses are able to swiftly discover any unfavorable sentiment or possible harm to their brand when they analyze client input across numerous aspects from social media, online reviews, and conversations with customer care representatives. This study aims to explain how aspect-based semantic analysis of market-collected user-generated data through performance comparisons of Doc2vec and TF-IDF vectorization. Both Doc2Vec and TF-IDF have their own distinctive qualities, which might vary according on the nature of the job, the dataset, and the volume of the available training data. For the objectives of this research, the data was obtained from several of fashion merchants that run their companies by means of the Shopee platform, which is a well-known online marketplace platform in Indonesia.  In this research, the accuracy and F1 Score achieved by Doc2Vec vectorization was superior to those achieved by TF-IDF vectorization. Our findings shows that Doc2Vec vectorization is better for classifying customer ratings because it can pull out the semantic meaning of words in a document. The findings also shows that the score of c and gamma parameter have significant impact to the score of Accuracy and F1 Score of the classifier.By precisely categorizing client sentiment, this study enables businesses to improve their services, respond to customers' problems, and increase their customer satisfaction.
Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network Nafiz, Muhammad Fauzan; Kartini, Dwi; Faisal, Mohammad Reza; Indriani, Fatma; Saragih, Triando Hamonangan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26374

Abstract

COVID-19 disease is known as a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed as a means of detecting COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. The AlexNet model, utilizing an input size of 227x227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930303. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through the utilization of mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. The primary contribution of this research lies in identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19.
Vessel Tracking System Based LoRa SX1278 Apriani, Yosi; Oktaviani, Wiwin A; Sofian, Ian Mochamad
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26385

Abstract

This research presents a vessel tracking system that provides real-time coordinate and speed information. The idea behind the development of this system originated from Automatic Identification System (AIS) technology, which functions as a vessel monitoring system in maritime areas. The system aims to improve navigation safety, monitor vessel traffic, and maritime security. In Indonesia, AIS is regulated by the Ministry of Transportation. However, this technology has not yet been implemented in river waters. In addition, AIS is a complex and expensive system. In this research, geographic location detection information in the form of a vessel tracking system is obtained using the UBlox Neo-6M GPS module based on LoRa technology. The LoRa mechanism periodically sends location data and vessel speed from the node to the gateway. The data is then sent to the ThingSpeak server using the MQTT protocol. On the server, the data can be accessed for further analysis. The developed system shows that the research can be realized and the system functions properly through a series of experimental tests. While in the in situ test, the system displayed good performance on LoRa SF 7 configuration with a signal strength of -118 dBm within the communication range of 1000 meters. This result can be improved by considering the MAPL value of -138 dBm.

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