cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 23   24   25   26   27 
Social Media Sentiment Analysis Using Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) Ahmad Zahri Ruhban Adam; Erwin Budi Setiawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25813

Abstract

The advancing technologies are aimed to maximize human performance. One of the great developments in technology is social media. The social media used in this study is Twitter because most people in Indonesia give their opinions to the public through tweets. The opinions given are very diverse, where they write positive, negative, and neutral opinions. The purpose of this study is to analyze the sentiments of the opinions given by the public in Bahasa Indonesia. To conduct sentiment analysis, tweets are collected by crawling the data. Tweets are then labeled positive, negative, and neutral and then represented as 1, -1, and 0. The method used to classify tweet sentiment is the Convolutional Neural Network (CNN) and Gated Recurrent Unit method (GRU). Research stages including feature selection, feature expansion, preprocessing and balancing with SMOTE. The highest accuracy value obtained on the CNN-GRU model with an accuracy value of 97.58% value. Based on these tests, it can be concluded that sentiment analysis research on Twitter social media using the Convolutional Neural Network and Gated Recurrent Unit methods can produce fairly high accuracy, and feature expansion testing of the deep learning model can provide a significant increase in accuracy values.
Implementation Of Fuzzy Logic Control Method On Chilli Cultivation Technology Based Smart Drip Irrigation System Faikul Umam; Ach. Dafid; Andharini Dwi Cahyani
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25878

Abstract

Herbal chili plants are very beneficial from a health and economic perspective. In the process of cultivating herbal chili plants, there are still many problems that need to be faced, including unfavorable climatic conditions and less intensive cultivation processes. Based on this description, to overcome these problems, technological innovation is needed that can be implemented directly in the cultivation of herbal chili plants. This situation can be achieved by applying a drip irrigation system. This system makes it possible to control the water supply requirements of chili herbs efficiently. System stability can run optimally when combined with a method that can make a decision quickly. Fuzzy logic is used in research because it is able to provide appropriate decisions on temperature and soil moisture data in chili herbs. This research is expected to overcome the problem of water shortages in barren areas. And increase people's interest in the cultivation of herbal chili plants. This research is also an overview and framework for developing the agricultural sector in Madura in the technology field. The results of this study indicate that technology can be designed and integrated with the fuzzy logic control method, then the results of testing the tool also show a 99,98% success rate. This is shown by the results of testing in the morning, afternoon, and evening. The contribution of this study is the control of temperature and humidity which in other studies only focused on the soil, not on the temperature and humidity of the air around the herbal chili plants with a system that has been controlled using the fuzzy method.
Measuring and Mitigating Bias in Bank Customers Data with XGBoost, LightGBM, and Random Forest Algorithm Berliana Shafa Wardani; Siti Sa'adah; Dade Nurjanah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25768

Abstract

To retain its clients, the Portuguese banking institution conducts direct marketing in the form of phone calls to conduct marketing so that clients subscribe to the bank's term deposit. The data used is named bank customers data. Important client features are considered in the acquisition process. This research was conducted with bank customers data from Portuguese banking institution which implements agent acquisition. With a large number of data on bank customers, it can lead to a diversity of data which allows the results of agent acquisition to be unfair. With this, a bias detection and mitigation algorithm are needed to achieve fairness. AI fairness 360 (AIF 360) is a toolkit that provided a bias detection and mitigation algorithm. The bias mitigation algorithm in AIF 360 is divided into three processes, namely reweighing and learning fair representation at the pre-processing stage, prejudice remover and adversarial debasing at the in-processing stage, and equalized odds and reject option classification at the post-processing stage. The output of this study is a comparison of the calculation of bias detection with disparate impact (DI) and statistical parity differences (SPD) before and after mitigation. The adversarial debiasing algorithm performed best than others with 0.943 of DI, -0.004 of SPD, and also increased the 0.015% of the AUC score. Conducting this research can help the prediction of client’s term deposits in Portuguese banking institution more fairly.
Comparison of Support Vector Machine (SVM) and Random Forest Algorithm for Detection of Negative Content on Websites Hermawan Syahputra; Aldiva Wibowo
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25861

Abstract

The amount of negative content circulating on the internet can damage people's morale so that social conflicts arise in society that threaten national sovereignty. Detecting negative content can help identify and prevent harmful events before they occur. This can lead to a safer and more positive online environment. Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm for Detection of Negative Content on Websites. The research contributions are 1) detect negative content on the internet with random forest and SVM, 2) comparing SVM and RF algorithms for detecting negative content on websites, 3) detection of negative content based on text focusing on the categories of fraud, gambling, pornography and Whitelist. The stages of this research are preparing a text content dataset on a website that has been labeled, preprocessing (duplicated data, text cleansing, case folding, stopward, tokenize, label encoding, data splitting, and determine the TF-IDF), finally performing the classification process with SVM and Random Forest. The dataset used in this study is a structured dataset in the form of text obtained from emails that have been registered on the TrustPositive website as negative content.  Negative content includes fraud, pornography and gambling. The results show the accuracy of the SVM is 97%, Precision 90% and Recall 91%, while for Accuracy in Random Forest is 92%, Precision 71%, and Recall 86%. The value obtained is the result of testing using 526 website URLs. The test results show that the Support Vector Machine is better than the Random Forest in this study.
Palm Print Recognition Using Intelligent Techniques: A review Sara A. Mohammed Al-Taie; Baydaa I. Khaleel
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25777

Abstract

Hand or Palm print recognition systems are one of the efficient people recognition and authentication systems that provide high-security levels by approving the entering and exiting of people such as employees in the work field or companies. The basis for using palmprints lies in the fact that no two individuals have exactly the same palmprint pattern, moreover palmprints remain more or less stablethroughout the lifetime and are easily obtainable using standard imaging techniques. Palm print recognition systems process picture data from a photograph of a person's palm and compare it to a record for that person using a scanning device or camera-based application. There are numerous ways to obtain a palmprint image, including digital scanners. Researchers have taken palmprint photographs using video cameras, CCD-based scanners, and tripods. A CCD-based scanner may be used to take a high resolution image of a palmprint. A palm image can also be perfectly aligned with the user's hand thanks to the pegs on the CCD-based scanner The palmprint has a variety of natural ompositions that are rich in identifying characteristics like wrinkles, ridges, major lines, single, and minute points. Because of these, a palmprint is a distinctive biometric that is trustworthy for identifying humans As Artificial Intelligence (AI) methods and applications improved, the improvement of computer techniques and the usage of  techniques increased in all fields including people recognition field. Many intelligent techniques are used to recognize people such as neural networks, the Genetic Algorithm, Particle Swarm Algorithm, and Deep Learning all these techniques are used and have almost the same recognition accuracy
Gazebo Semar: An Android-based Farmer Education Platform for Agricultural Waste Management Mahdaviqia Dharmawan; Lusia Dara Sari; Jericho Pandita Gunawan; Ernoiz Antriyandarti; An Duong
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25757

Abstract

Agricultural waste and lack of knowledge about agricultural waste management is an environmental problem in Karanganyar Regency. Farmers in Karanganyar only handle agricultural waste, such as rice straws, husks, and corn stalks by burning them. Therefore, this study attempts to create innovation by educating farmers on properly treating agricultural waste. This study implemented mix method, namely application development and application evaluation by conducting survey. The Gazebo Semar application is built using a Kodular service begins with concept planning, interface design, features, and coding. Application evaluation were collected from a survey using a questionnaire of 120 farmers in Karanganyar Regency to evaluate the usability testing using USE (Usefulness, Satisfaction, and Ease to use) questionnaires and use the Likert scale for measurement. Gazebo Semar is a solution to provide information on agricultural waste types, waste management, and marketing of processed agricultural waste products. This application allows the users to easily access information about agricultural waste and Zero Waste without visiting multiple websites or blogs. The Gazebo Semar App has nine main features: Home Screen, Zero Waste, SDGs, Waste Source, Waste Classification, Waste Management, Gazebo Semar Store, Quiz, and About Us. Gazebo Semar provides a number of novelties in terms of substance and features, so that it is expected to have an impact on local farmers especially in the field of agricultural waste processing. The results show that the score of Usefulness, Satisfaction, and Ease of Use is above 71%, which means Gazebo Semar has provided the application that fits with the needs of farmers. The research contribution is to improve the mindset of farmers in processing agricultural waste into other forms that are more economically valuable. The significance of this study is to increase public knowledge about agricultural waste, zero waste, and waste management. 
Optimization of Wind Farm Yaw Offset Angle using Online Genetic Algorithm with a Modified Elitism Strategy to Maximize Power Production Kurniawan Kurniawan; Aris Triwiyatno; Iwan Setiawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25747

Abstract

The wake interaction in a wind farm occurs when the front turbines block the flow of wind to the turbines behind them, causing a total power loss of approximately 10–25%. Wake interactions can be redirected to reduce bad impacts by optimizing the yaw offset angles. Optimization of the yaw offset angle can increase the total power of the wind farm by approximately 6–9%. However, the fluctuating wind flow angle in the environment causes the behavior of the wake interaction to change, making it difficult to optimize the yaw offset angles. Therefore, this study proposes an online genetic algorithm with a modified elitism strategy to overcome this problem. The contribution of this study is to improve the performance of the genetic algorithm by modifying the elitism strategy in order to optimize the yaw offset angle for each turbine adaptively to a wind farm operating in a dynamic environment. The optimal yaw offset angles are stored in the elite population for various wind flow angles and then reinserted into the search population in each generation according to the actual wind flow angles. A Gaussian-based analytical wake interaction model under a yawed condition developed by Shapiro is employed in this study to evaluate the total power of a wind farm. This study resulted in a convergence speed that was 3.8 times faster than the classical elitism strategy. At several wind flow angles of 270°, 315°, and 360°, an average power increase of 10.52% was obtained. This study shows that the modification of the elitism strategy can increase the convergence speed to adaptively track the optimal yaw offset angle at various wind flow angles, so that the average increase in wind farm power is 1.94% higher than in previous studies.
Water Quality Monitoring with Regression Based PPM Sensor for Controlling Hydroponic Dissolved Nutrient Dimas Adiputra; Titus Kristanto; Abduh Sayid Albana; Gilbert Wednestwo Samuel; Syakira Andriyani; Christian Jose Anto Kurniawan; Nursyahjaya Ramadaniputra; Era Anzha Naelil Munna
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.v9i2.25915

Abstract

Hydroponic cultivation requires rigorous monitoring and control of several parameters, such as turbidity, electric conductivity, acidity (pH), dissolved oxygen and nutrient, which usually be measured once a day manually. Therefore, automation in hydroponic cultivation requires those water quality information as the controlled variable. The dissolved nutrient is especially important because it significantly affects the hydroponic plant growth. Acquiring the dissolved nutrient can be done by using a PPM (parts per million) sensor, but most of the time the sensor needs further processing to obtain the desired measurement. This study presents a reading correction of a PPM sensor based on a regression method so the desired measurement can be done. Sample water with different PPM, such 309 PPM, 290 PPM, 762 PPM, 1910 PPM and 2420 PPM are measured first using a standard PPM meter. Then, the sample PPM is measured by using the PPM sensor. The study also investigates the best regression method to map the PPM sensor measurement to the standard PPM meter measurement by comparing several line equations, such as linear, exponential, polynomial and logarithmic. The function coefficient and bias is chosen by using least square methods. After comparing, the result shows that the polynomial function provides the best reading correction with average error of 76 PPM. The error is especially few when measuring the higher PPM (more than 500 PPM), which is suitable with hydroponic cultivation. Therefore, the PPM sensor with the polynomial function shown in this study can be used to measure the dissolve nutrient accurately in the automation of hydroponic activity compare to other line equations. This study is limited to small sample sizes to prove the concept. The generalization can also be considered in the future study.
Prediction of Post-Operative Survival Expectancy in Thoracic Lung Cancer Surgery Using Extreme Learning Machine and SMOTE Ajwa Helisa; Triando Hamonangan Saragih; Irwan Budiman; Fatma Indriani; Dwi Kartini
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.v9i2.25973

Abstract

Lung cancer is the most common cause of cancer death globally. Thoracic surgery is a common treatment for patients with lung cancer. However, there are many risks and postoperative complications leading to death. In this study, we will predict life expectancy for lung cancer patients one year after thoracic surgery The data used is secondary data for lung cancer patients in 2007-2011. There are 470 data consisting of 70 death class data and 400 survival class data for one year after surgery. The algorithm used is Extreme learning machine (ELM) for classification, which tends to be fast in the learning process and has good generalization performance. Synthetic Minority Over-sampling (SMOTE) is used to solve the problem of imbalanced data. The proposed solution combines the benefits of using SMOTE for imbalanced data along with ELM. The results show ELM and SMOTE outperform other algorithms such as Naïve Bayes, Decision stump, J48, and Random Forest. The best results on ELM were obtained at 50 neurons with 89.1% accuracy, F-Measure 0.86, and ROC 0.794. In the combination of ELM and SMOTE, the accuracy is 85.22%, F-measure 0.864, and ROC 0.855 on neuron 45 using a data division proportion of 90:10. The test results show that the proposed method can significantly improve the performance of the ELM algorithm in overcoming class imbalance. The contribution of this study is to build a machine learning model with good performance so that it can be a support system for medical informatics experts and doctors in early detection to predict the life expectancy of lung cancer patients.
Implementation of Open Web Application Security Project for Penetration Testing on Educational Institution Websites Nani Sulisnawati; Subektiningsih Subektiningsih
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.v9i2.25987

Abstract

The development of information technology cannot be separated from the development of website applications, as well as the threat of security attacks that will attack website applications. Educational Institution X uses a website application as an important medium in learning activities. Therefore, penetration testing is needed to find security holes in website applications. In this study, penetration testing will be carried out with the target website for student access at Educational Institution X based on the reason that there is sensitive student data that needs to be secure. The method used in this study is an experimental method with the OWASP TOP 10 2021 standard (Open Web Application Security Project). The penetration test results obtained on the website application at Educational Institution X found 11 vulnerabilities that could be tested. Of the 11 vulnerabilities, there is one vulnerability at the medium risk level, 7 at the low risk level, and 3 at the information risk level. The vulnerabilities found relate to token authentication, policy delivery, cookie attribute, cross-site script inclusion, authorization, clickjacking, and weak transport layer security. Based on the penetration testing activities obtained, it can be concluded that the vulnerability gaps found need to be further repaired by the website application system developer, in this case, the Educational Institution X. Therefore, the final result of this study is in the form of a report document containing a list of vulnerabilities, recommendations for vulnerability repairs, and vulnerability mitigation strategies as solutions for handling security systems on website applications to make them even better.

Page 25 of 51 | Total Record : 505

 23   24   25   26   27 

Filter by Year

2015 2025


Reset
Filter By Issues
All Issue Vol. 11 No. 4 (2025): December Vol. 11 No. 3 (2025): September Vol. 11 No. 2 (2025): June Vol. 11 No. 1 (2025): March Vol. 10 No. 4 (2024): December Vol. 10 No. 3 (2024): September Vol. 10 No. 2 (2024): June Vol. 10 No. 1 (2024): March Vol. 9 No. 4 (2023): December Vol 9, No 4 (2023): December Vol 9, No 3 (2023): September Vol. 9 No. 3 (2023): September Vol 9, No 2 (2023): June Vol. 9 No. 2 (2023): June Vol. 9 No. 1 (2023): March Vol 9, No 1 (2023): March Vol 8, No 4 (2022): Desember Vol 8, No 3 (2022): September Vol 8, No 2 (2022): June Vol 8, No 1 (2022): March Vol 7, No 3 (2021): December Vol 7, No 2 (2021): August Vol. 7 No. 1 (2021): April Vol 7, No 1 (2021): April Vol 6, No 2 (2020): December Vol 6, No 1 (2020): June Vol. 6 No. 1 (2020): June Vol 5, No 2 (2019): December Vol 5, No 1 (2019): June Vol 4, No 2 (2018) Vol 4, No 2 (2018) Vol 4, No 1 (2018) Vol 4, No 1 (2018) Vol 3, No 2 (2017) Vol 3, No 2 (2017) Vol 3, No 1 (2017) Vol 3, No 1 (2017) Vol 2, No 2 (2016) Vol 2, No 2 (2016) Vol 2, No 1 (2016) Vol 2, No 1 (2016) Vol 1, No 1 (2015) Vol 1, No 1 (2015) More Issue