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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 30 Documents
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Search results for , issue "Vol 9, No 3 (2023): September" : 30 Documents clear
The Usage of Password Generators to Enhance Data Security in Most Used Applications Erwin Halim; Angelia Hartanto Teng; Marylise Hebrard; David Sundaram; Placide Poba-Nzaou
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.v9i3.26557

Abstract

One of the world's global issues is data breaches. These crimes can happen because most people use hackable passwords such as their birthdates and sequencing numbers or alphabets so they would not be easily forgotten. Setting weak passwords in almost or all accounts certainly raises issues and increases the possibility that classified information is hacked and leaked. When it happens, classified data can be misused and taken advantage. This research will help spread awareness to society on how important data security is and how helpful password generators can be to reduce and prevent the probability of data security crimes from happening. This quantitative research uses SMART-PLS as a statistical tool to process the data gathered and random sampling to determine its population. SMART-PLS is variance-based structural equation modeling that uses the partial squares path modeling method. Overall, researchers successfully gathered 114 datasets. Google Forms was used to gather the data. A potential limitation of the study is that all respondents are primarily based in Jakarta. Expanding the geographic focus for further study to gain more insights is highly recommended. 48% of the respondents came from the age group of under 20, occupation as students. Factors significantly affecting people's intention to comply with password generators are perceived password effectiveness, perceived ease of use, subjective norms, and attitude. Eventually, the intention to comply may arouse actual compliance. The result of the study can be used to raise educational campaigns on the usefulness of password generators to promote data security. Based on the result, 78.9% of the respondents are willing to increase their data security. This research contribution is to see how aware people are of data security, how well they know password generators as a technology to generate strong passwords, and how welcome they are with the idea of using password generators.
Topic Detection on Twitter Using Deep Learning Method with Feature Expansion GloVe Windy Ramadhanti; Erwin Budi Setiawan
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.v9i3.26736

Abstract

Twitter is a medium of communication, transmission of information, and exchange of opinions on a topic with an extensive reach. Twitter has a tweet with a text message of 280 characters. Because text messages can only be written briefly, tweets often use slang and may not follow structured grammar. The diverse vocabulary in tweets leads to word discrepancies, so tweets are difficult to understand. The problem often found in classifying topics in tweets is that they need higher accuracy due to these factors. Therefore, the authors used the GloVe feature expansion to reduce vocabulary discrepancies by building a corpus from Twitter and IndoNews. Research on the classification of topics in previous tweets has been done extensively with various Machine Learning or Deep Learning methods using feature expansion. However, To the best of our knowledge, Hybrid Deep Learning has not been previously used for topic classification on Twitter. Therefore, the study conducted experiments to analyze the impact of Hybrid Deep Learning and the expansion of GloVe features on classification topics. The total data used in this study was 55,411 datasets in Indonesian-language text. The methods used in this study are Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Hybrid CNN-RNN. The results show that the topic classification system with GloVe feature expansion using the CNN method achieved the highest accuracy of 92.80%, with an increase of 0.40% compared to the baseline. The RNN followed it with an accuracy of 93.72% and a 0.23% improvement. The CNN-RN Hybrid Deep Learning model achieved the highest accuracy of 94.56%, with a significant increase of 2.30%. The RNN-CNN model also achieved high accuracy, reaching 94.39% with a 0.95% increase. Based on the accuracy results, the Hybrid Deep Learning model, with the addition of feature expansion, significantly improved the system's performance, resulting in higher accuracy.
Optimization of Heavy Point Position Measurement on Vehicles Using Support Vector Machine Franky Melky; Siti Sendari; Ilham Ari Elbaith
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.v9i3.26261

Abstract

During this time, weight point testing is still done manually using a jack until now it has begun to be replaced with hydraulic equipment namely Lift Table Hydraulic (LTH) which is a portable table with a hydraulic system equipped with sensors (Loadcell and LVDT), powerpack control panel, powerpack, relay module and solenoid valve to adjust the table height. This portable table is one component of the heavy point measurement equipment system used for mining and plantation vehicles such as tractors, buses, trucks which are required to have a safe structure in heavy road conditions with rough or uneven surfaces with slopes up to an angle of 15 ° to 20 °. This emphasized research contributes to more accurate testing. Based on these problems, this research was conducted using Support Vector Machine (SVM) for the optimization of heavy point position measurement. The objects used are minibuses with 1 and 19 passengers and buses with 29 and 36 passengers on the proportion of datasets (training: testing) of 80% and 20% using linier kernel. From the experimental results, the accuracy in the condition of 1 passenger is 94.7%; minibus 19 passengers 98%; bus 29 passengers 98.1% and bus 36 passengers 97.4%. The highest accuracy obtains on 29 passengers minibus. 
Detection of COVID-19 Based on Synthetic Chest X-Ray (CXR) Images Using Deep Convolutional Generative Adversarial Networks (DCGAN) and Transfer Learning Anhar Anhar; Dandi Septiandi
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.v9i3.26685

Abstract

The global COVID-19 pandemic has significantly impacted the health and lives of people worldwide, with high numbers of cases and fatalities. Rapid and accurate diagnosis is crucially important. Radiographic imaging, particularly chest radiography (CXR), has been considered for diagnosing suspected COVID-19 patients. CXR images offers quick imaging, affordability, and wide accessibility, making it pivotal for screening. However, the scarcity of CXR images remains due to the pandemic's recent emergence. To address this scarcity, this study harnesses the capabilities of Deep Convolutional Generative Adversarial Networks (DCGAN). DCGAN is a convolution-based GAN approach, has the potential to alleviate the scarcity of CXR data by generating authentic-looking synthetic images. This study combines synthetic CXR images with real CXR images to bolster model performance, resulting in an Extended Dataset. Extended Dataset comprises 7,345 images, with 34.63% being original CXR images and 65.37% being synthetic images produced by DCGAN. Expanded Dataset then utilized to train three pre-trained models: ResNet50, EfficientNetV1, and EfficientNetV2. The outcomes are remarkable, showcasing considerable enhancement in detection accuracy. Especially for the EfficientNetV1 model, it takes the lead with an impressive accuracy of 99.21% after merely ten epochs, achieved within a brief training period of 6.18 minutes. This surpasses the prior accuracy of 98.43% observed when used the Original Dataset (without synthetic CXR images). Overall, this research offers a solution to mitigate the scarcity of synthetic CXR images for COVID-19 detection. For future endeavors, refining the quality of synthetic images stands as an area for exploration, enhancing the overall efficacy of this approach.
Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using Bert Language Models Lenggo Geni; Evi Yulianti; Dana Indra Sensuse
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.v9i3.26490

Abstract

General election is one of the crucial moments for a democratic country, e.g., Indonesia. Good election preparation can increase people's participation in the general election. In this study, we conduct a sentiment analysis of Indonesian public opinion on the upcoming 2024 election using Twitter data and IndoBERT model. This study is aimed at helping the government and related institutions to understand public perception. Therefore, they could obtain valuable insights to better prepare for elections, including evaluating the election policies, developing campaign strategies, increasing voter engagement, addressing issues and conflicts, and increasing transparency and public trust. The main contribution of this study is threefold: (i) the application of state-of-the-art transformer-based model IndoBERT for sentiment analysis on political domain; (ii) the empirical evaluation of IndoBERT model against machine learning and lexicon-based models; and (iii) the new dataset creation for sentiment analysis in political domain. Our Twitter data shows that Indonesian public mostly reacts neutrally (83.7%) towards the upcoming 2024 election. Then, the experimental results demonstrate that IndoBERT large-p1 is the best-performing model that achieves an accuracy of 83.5%. It improves our baseline systems by 48.5% and 46.49% for TextBlob, 2.5% and 14.49% for Multinomial Naïve Bayes, and 3.5% and 13.49% for Support Vector Machine in terms of accuracy and F-1 score, respectively.
Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory Kevin Yudhaprawira Halim; Dodon Turianto Nugrahadi; Mohammad Reza Faisal; Rudy Herteno; Irwan Budiman
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.
Object Classification Model Using Ensemble Learning with Gray-Level Co-Occurrence Matrix and Histogram Extraction Florentina Tatrin Kurniati; Daniel HF Manongga; Eko Sediyono; Sri Yulianto Joko Prasetyo; Roy Rudolf Huizen
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.v9i3.26683

Abstract

In the field of object classification, identification based on object variations is a challenge in itself. Variations include shape, size, color, and texture, these can cause problems in recognizing and distinguishing objects accurately. The purpose of this research is to develop a classification method so that objects can be accurately identified. The proposed classification model uses Voting and Combined Classifier, with Random Forest, K-NN, Decision Tree, SVM, and Naive Bayes classification methods. The test results show that the voting method and Combined Classifier obtain quite good results with each of them, ensemble voting with an accuracy value of 92.4%, 78.6% precision, 95.2% recall, and 86.1% F1-score. While the combined classifier with an accuracy value of 99.3%, a precision of 97.6%, a recall of 100%, and a 98.8% F1-score. Based on the test results, it can be concluded that the use of the Combined Classifier and voting methods is proven to increase the accuracy value. The contribution of this research increases the effectiveness of the Ensemble Learning method, especially the voting ensemble method and the Combined Classifier in increasing the accuracy of object classification in image processing.
Vessel Tracking System Based LoRa SX1278 Yosi Apriani; Wiwin A Oktaviani; Ian Mochamad Sofian
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.
Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms Shoffan Saifullah; Rafal Drezewski; Anton Yudhana; Andri Pranolo; Wilis Kaswijanti; Andiko Putro Suryotomo; Seno Aji Putra; Alin Khaliduzzaman; Anton Satria Prabuwono; Nathalie Japkowicz
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.v9i3.26722

Abstract

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification.
Implementation of Geographic Information System Based on Google Maps API to Map Waste Collection Point Using the Haversine Formula Method Ni Made Ary Esta Dewi Wirastuti; Lino Verlin; Is-Haka Mkwawa; Khalid G. Samarah
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.v9i3.26588

Abstract

Human life with all its activities cannot be separated from the existence of waste but the quality of post-consumption waste is generally still low. To convert waste into a more stable form and does not pollute the environment, it is required a precise waste collection system. Waste collection system is an important part of waste management. This study is proposed a geographic information system (GIS) application based on google maps Application Programming Interface (API) to map waste collection point using Haversine formula method. The application is designed to develop a waste collection system from households to Temporary Disposal Sites (TDS), quickly and accurately. The application can be used by households and waste taxi bike drivers to communicate when the households need the taxi bike drivers to pick the waste up to the temporary collection points. A geographic information system application based on google maps API used to display the location of the taxi bike driver, TDS and the location of the waste collection (household). Haversine formula is used to get the nearest waste taxi bike driver to the location of the request for waste transportation. The result of this research is an application that can monitor and track waste collection. Using black box testing, the system has run according to the functions and scenarios designed. Based on the testing the system usability scale results, the application obtains a score of 70.125 which indicates that the application is classified as good and acceptable to users.

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