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
 32   33   34   35   36 
Effect of SMOTE Variants on Software Defect Prediction Classification Based on Boosting Algorithm Aflaha, Rahmina Ulfah; Herteno, Rudy; Faisal, Mohammad Reza; Abadi, Friska; Saputro, Setyo Wahyu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28521

Abstract

Detecting software defects early on is critical for avoiding significant financial losses. However, building accurate software defect prediction models can be challenging due to class imbalance, where the data for defective modules is much less than for standard modules. This research addresses this issue using the imbalanced dataset NASA MDP. To address this issue, researchers have proposed new methods that combine data level balancing approaches with 14 variations of the SMOTE algorithm to increase the amount of defective module data. An algorithm-level approach with three boosting algorithms, Catboost, LightGBM, and Gradient Boosting, is applied to classify modules as defective or non-defective. These methods aim to improve the accuracy of software defect prediction. The results show that this new method can produce a more accurate classification than previous studies. The DSMOTE and Gradient Boosting pair with 0.9161 has the highest average accuracy (0.9161). The DSMOTE and Catboost model achieved the highest average AUC value (0.9637). The ADASYN kernel and Catboost showed the best ability to perform the average G-mean value (0.9154). The research contribution to software defect prediction involves developing new techniques and evaluating their effectiveness in addressing class imbalance.
An Innovative Artificial Intelligence-Based Extreme Learning Machine Based on Random Forest Classifier for Diagnosed Diabetes Mellitus Saputra, Dimas Chaerul Ekty; Muryadi, Elvaro Islami; Phann, Raksmey; Futri, Irianna; Lismawati, Lismawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28690

Abstract

Since 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In recent years, the fatality rate attributable to diabetes has increased by 13% in low- and middle-income countries. Statistics collected by the World Health Organization indicate that the number of persons diagnosed with diabetes has increased from 108 million in 1980 to 422 million in 2014. The objective of this study is to construct a model capable of diagnosing persons with diabetes reliably, correctly, and consistently. This research used secondary data offered by Kaggle. The original data came from the National Institute of Diabetes and Digestive and Kidney Diseases. Each of the up to 768 data points consists of nine characteristics and two outputs, such as diabetes and non-diabetes in the provided example. In this study, a single algorithm is constructed by integrating two separate algorithms. Random forest algorithms, which are based on machine learning, and extreme learning machines, which are based on deep learning, have generated extraordinarily accurate results. When the confusion matrix is used, 98.05% accuracy is attained. Therefore, it is feasible to conclude that the suggested method was successful in completing an adequate analysis and classifying the data.
Analysis Kernel and Feature: Impact on Classification Performance on Speech Emotion Using Machine Learning Gondohanindijo, Jutono; Noersasongko, Edi; Pujiono, Pujiono; Muljono, Muljono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29022

Abstract

The main objective of this study is to test the machine learning kernel's selection against the characteristics of the data set used, resulting in good classification performance. The goal of speech emotion recognition is to improve computers' ability to detect and process human emotions in order to improve their ability to respond to interactions between people and computers. It can be applied to feedback on talks, including sentimental or emotional content, as well as the detection of human mental health. One field of data mining work is Speech Emotion Recognition. One of the important things in data mining research is to determine the selection of the kernel Classifier, know the characteristics of datasets, perform Engineering Features and combine features and Corpus Datasets to obtain high accuracy. The research uses analysis and comparison methods using private and public datasets to detect speech emotions. Experimental analysis was done on the characteristics of datasets, selection of kernel classifiers, pre-processing, feature and corpus datasets fusion. Understanding the selection of a classifier kernel that matches the characteristics of the dataset, engineering features and the merger of features and datasets are the contributions of this investigation to improving the accuracy of the classification of speech emotion data. For models with the selection of kernels that match the characteristics of their datasets, this study gave an increase in accuracy of 12.30% for the private dataset and 14.80% for the public dataset, with accuracies of 100.00% and 74.80% respectively. Combining features and public datasets provides an increase in accuracy of 33.62% with an accuracy of 73.95%.
Implementation of Fisherface Algorithm for Eye and Mouth Recognition in Face-Tracking Mobile Robot Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Duri, Ades Harafi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29266

Abstract

Facial recognition is an artificial intelligence algorithm that distinguishes one face from another by capturing facial patterns visually. This recognition specifically detects and identifies individuals based on facial features by scanning the entire face. Several methods are used for facial detection, including facial landmarks points, Local Binary Patterns Histograms (LBPH), and Fisherface. In the context of this research, Fisherface is used to reduce the dimensionality of facial space in order to obtain image features. The method is insensitive to changes in expression and lighting, leading to better pattern classification and making it suitable for implementation on mobile devices such as robot vision. Therefore, this research aimed to measure the response time speed and accuracy level of pattern recognition when implemented on mobile robot devices. The results obtained from the accuracy testing showed that the highest accuracy for face detection process was 90%, while the lowest was 78.3%. In addition, the average execution time (AET) for the fastest process was 1.63 seconds and the slowest was 1.72 seconds. For pattern recognition, the statistics showed 90% accuracy, 100% precision, 81.81% recall, and F-1 score of 89.5%. Meanwhile, the longest execution time was 0.084 seconds and the fastest was 0.064 seconds. In face tracking process, the mobile robot movement was based on real-time pixel sizes, determining x and y values to produce the center of face region.
iGWO-RF: an Improved Grey Wolfed Optimization for Random Forest Hyperparameter Optimization to Identification Breast Cancer Muryadi, Elvaro Islami; Futri, Irianna; Saputra, Dimas Chaerul Ekty
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.29300

Abstract

The study focuses on improving the accuracy of breast cancer diagnosis by enhancing the predictive capabilities of a Random Forest model. This is achieved by utilizing an improved Grey Wolf Optimization algorithm for hyperparameter optimization. The main objectives are to enhance early detection, increase diagnostic precision, and reduce computational demands in clinical workflows. The work utilizes the Improved Grey Wolf Optimization (iGWO) algorithm to tune the hyperparameters of a Random Forest (RF) model, thereby improving its accuracy in diagnosing breast cancer. The methodology encompasses several steps, including data preparation, model training using iGWO-enhanced RF, performance evaluation compared to traditional methods, and validation using clinical datasets to confirm the reliability and effectiveness of the approach. The iGWO-RF model demonstrated exceptional performance in diagnosing breast cancer, achieving an accuracy of 96.4%, precision of 96.4%, recall of 98.0%, F1-score of 97.2%, and ROC-AUC of 0.988. The findings of iGWO-RF outperform those of SVM, original RF, Naive Bayes, and KNN models, indicating that iGWO-RF is effective in optimizing hyperparameters to improve prediction accuracy. The iGWO-RF model greatly enhances the accuracy and efficiency of breast cancer diagnosis, surpassing conventional models. Integrating iGWO-RF into clinical workflows is advised to improve early identification and patient outcomes. Additional investigation should focus on the utilization of this technology in various medical datasets and circumstances, highlighting its potential in a wide range of healthcare environments.
GAMA CUTE: Development of a Web-based for Gadjah Mada Caring University for Thalassemia Exit Prediction Tool by Applying Machine Learning Saputra, Dimas Chaerul Ekty; Afiahayati, Afiahayati; Ratnaningsih, Tri
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29301

Abstract

Blood disorders occur in one or several parts of the blood that affect the nature and function, and blood disorders can be acute or chronic. Blood disease consists of several types, such as anemia. Anemia is the most common hematologic disorder associated with a decrease in the number of red blood cells or hemoglobin, causing a decrease in the ability of the blood to carry oxygen throughout the body. Patients with anemia in Indonesia have increased for the age of 15-24 years. This study aimed to conduct a screening test for anemia using machine learning. It is expected to know the process of knowing the type of anemia suffered. The machine learning technique used to identify the cause of anemia is divided into four classes, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination (Beta Thalassemia Trait and Iron Deficiency Anemia or Hemoglobin E and Iron Deficiency Anemia). This study would apply the K-Nearest Neighbor (KNN) and Random Forest (RF) methods to build a model on the data collected. The evaluation results using a confusion matrix in the form of accuracy, precision, recall, and f1-score against the KNN and RF methods are 79.36%, 59.40%, 62.80%, and 62.80%. In comparison, the RF is 87.30%, 90.89%, 78.40%, and 81.00%. From the results of comparing the two methods, the Graphic User Interface (GUI) implementation using python applies the RF method. The classifier that gets the highest value among all these parameters is called the best machine learning algorithm to perform screening tests for anemia.
Control and Navigation of Differential Drive Mobile Robot with PID and Hector SLAM: Simulation and Implementation Fahmizal, Fahmizal; Pratikno, Matthew Sebastian; Isnianto, Hidayat Nur; Mayub, Afrizal; Maghfiroh, Hari; Anugrah, Pinto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29428

Abstract

Navigation technology is essential in fields like transportation and logistics, where precise mapping and localization are critical. Simultaneous Localization and Mapping (SLAM) technologies, such as Hector SLAM, enable robots to map environments by detecting and predicting object locations using sensors like LiDAR. Unlike other SLAM methods, Hector SLAM operates without odometry, relying solely on LiDAR data to produce accurate maps. This study investigates the application of Hector SLAM in a differential drive mobile robot controlled via the Robot Operating System (ROS), with PID control managing the motor speeds. The research contribution is the integration of Hector SLAM with PID control to enhance mapping accuracy in environments without odometry data. The method involves testing the robot's mapping performance in an indoor environment, focusing on the impact of varying linear and angular velocities on the quality of the generated maps. The PID control was tuned to ensure stable speed values for the robot's differential drive motors. Results show that Hector SLAM, when combined with well-tuned PID control, generates highly accurate maps that closely match the actual environment dimensions, with minimal errors. Specifically, the mapping error was found to be within 0.10 meters, validating the effectiveness of this approach in non-odometric systems. In conclusion, the study demonstrates that Hector SLAM, supported by PID-controlled motor stability, is an effective solution for mapping in differential drive mobile robots, particularly in scenarios where odometry is unavailable.
From Text to Truth: Leveraging IndoBERT and Machine Learning Models for Hoax Detection in Indonesian News Ridho, Muhammad Yusuf; Yulianti, Evi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29450

Abstract

In the era of technology and information exchange online content being deceitful poses a serious threat to public trust and social harmony on a global scale. Detective mechanisms to identify content are essential for safeguard the populace effectively. This study is dedicated to creating a machine learning system that can automatically spot deceptive content in Indonesian language by utilizing IndoBERT. A model specifically tailored for the intricacies of the Indonesian language. IndoBERT was selected due to its capacity to grasp the linguistic nuances present, in Indonesian text which are often challenging for other models built upon the BERT framework. The key focus of this study lies in conducting an assessment of the IndoBERT model in relation to other approaches used in past research for identifying fake news like CNN LSTM and various classification models such as Logistic Regression and Naïve Bayes among others. To address the issue of imbalanced data between valid labels in fake news detection tasks we employed the SMOTE oversampling technique, for data augmentation and balancing purposes. The dataset employed consists of Indonesian language news articles publicly available and categorized as either hoax or valid following assessment by three judges voting system. IndoBERT Large demonstrated performance by achieving an accuracy rate of 98% outperform the original datasets 92% when tested on the oversampled dataset. Utilizing the SMOTE oversampling technique aided in data balance and enhancing the models performance. These outcomes highlight IndoBERTs capabilities in detecting fake news and pave the way for its potential integration, into real world scenarios.
The Effectiveness of Data Imputations on Myocardial Infarction Complication Classification Using Machine Learning Approach with Hyperparameter Tuning Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Budiman, Irwan; Farmadi, Andi; Tajali, Ahmad
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29479

Abstract

Complications from Myocardial Infarction (MI) represent a critical medical emergency caused by the blockage of blood flow to the heart muscle, primarily due to a blood clot in a coronary artery narrowed by atherosclerotic plaque. Diagnosing MI involves physical examination, electrocardiogram (ECG) evaluation, blood sample analysis for specific heart enzyme levels, and imaging techniques such as coronary angiography. Proactively predicting acute myocardial complications can mitigate adverse outcomes, and this study focuses on early prediction using classification methods. Machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and XGBoost were employed to classify patient medical records accurately. Techniques like K-Nearest Neighbors (KNN) imputation, Iterative imputation, and Miss Forest were used to handle incomplete datasets, preserving vital information. Hyperparameter optimization, crucial for model performance, was performed using Bayesian Optimization, which minimizes the objective function by modeling past evaluations. The contribution to this study is to see how much influence data imputation has on classification using machine learning methods on missing data and to see how much influence the optimization method has when performing hyperparameter tuning. Results demonstrated that the Iterative Imputation method yielded excellent performance with SVM and XGBoost algorithms. SVM achieved 100% accuracy, precision, sensitivity, F1 score, and AUC. XGBoost reached 99.4% accuracy, 100% precision, 79.6% sensitivity, an F1 score of 88.7%, and an AUC of 0.898. KNN Imputation with SVM showed results similar to Iterative Imputation with SVM, while Random Forest exhibited poor classification outcomes due to data imbalance, causing overfitting.
Reducing Overfitting in Neural Networks for Text Classification Using Kaggle's IMDB Movie Reviews Dataset Poningsih, Poningsih; Windarto, Agus Perdana; Alkhairi, Putrama
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29509

Abstract

Overfitting presents a significant challenge in developing text classification models using neural networks, as it occurs when models learn too much from the training data, including noise and specific details, resulting in poor performance on new, unseen data. This study addresses this issue by exploring overfitting reduction techniques to enhance the generalization of neural networks in text classification tasks using the IMDB movie review dataset from Kaggle. The research aims to provide insights into effective methods to reduce overfitting, thereby improving the performance and reliability of text classification models in practical applications. The methodology involves developing two LSTM neural network models: a standard model without overfitting reduction techniques and an enhanced model incorporating dropout and early stopping. The IMDB dataset is preprocessed to convert reviews into sequences suitable for input into the LSTM models. Both models are trained, and their performances are compared using various metrics. The model without overfitting reduction techniques shows a test loss of 0.4724 and a test accuracy of 86.81%. Its precision, recall, and F1-score for classifying negative reviews are 0.91, 0.82, and 0.86, respectively, and for positive reviews are 0.84, 0.92, and 0.87. The enhanced model, incorporating dropout and early stopping, demonstrates improved performance with a lower test loss of 0.2807 and a higher test accuracy of 88.61%. For negative reviews, its precision, recall, and F1-score are 0.92, 0.84, and 0.88, and for positive reviews are 0.86, 0.93, and 0.89. Overall, the enhanced model achieves better metrics, with an accuracy of 89%, and macro and weighted averages for precision, recall, and F1-score all at 0.89. The applying overfitting reduction techniques significantly enhances the model's performance.

Page 34 of 51 | Total Record : 505

 32   33   34   35   36 

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