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
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
 890   891   892   893   894 
Handling missing values and clustering industrial liquid waste using K-medoids Maharrani, Ratih Hafsarah; Abda'u, Prih Diantono; Ikhtiagung, Ganjar Ndaru; Rahadi, Nur Wahyu; Zaenurrohman, Zaenurrohman
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1411-1420

Abstract

The textile industry is a significant contributor to environmental pollution due to its wastewater, which contains hazardous substances such as dyes, heavy metals, and chemicals that can severely harm aquatic ecosystems. Effective management of this wastewater is crucial to mitigate its environmental impact. This study focuses on classifying industrial liquid waste data using the K-medoids clustering method, chosen for its robustness to noise and outliers compared to K-means. To address challenges in wastewater data processing, such as missing values and varying data scales, two approaches are compared: replacing missing values with zero and K-nearest neighbors (KNN) imputation, alongside Z-score normalization for data uniformity. The clustering quality is evaluated using the Davies-Bouldin index (DBI) for cluster variations of k=2, 3, 4, and 5. The results show that the best clustering quality is achieved at k=2, with the smallest DBI values obtained using KNN imputation (0.139) and zero replacement (0.149). The superior performance of KNN imputation highlights its effectiveness in handling missing data. These findings provide valuable insights into the characteristics of textile industry wastewater pollution, offering a robust framework for effective wastewater management. The study concludes with practical recommendations for policymakers and industry stakeholders to adopt advanced data-driven approaches for sustainable wastewater treatment strategies.
Machine learning framework and tools in precision farming Baburao, Patil Sagar; Kulkarni, R. B.; Patil, Suchita S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1063-1071

Abstract

Farming using machine learning (ML) techniques has a role to play in the current globalization scenario due to the advantages it offers for costeffective harvesting of the crop. The areas such as crop disease detection, soil nutrient detection, fertilizer analysis and optimization, weather and irrigation schedule prediction, are investigated utilizing a range of deep learning and ML techniques, such as K-nearest neighbors (KNNs), convolutional neural networks (CNNs), and support vector machines (SVMs). The article concentrates on preparing the recommendation system for the farmer to take a quick and timely decision for crop disease, use of optimal fertilizer for crop growth, and water requirement prediction to overcome water wastage. A massive amount of data, including image data from publicly accessible sources, such as PlantVillage, Kaggle is used to train the model. Sensor data is fed into the ML model for the nutrients analysis and water requirement analysis. An Android application is developed, which can be used from any handheld device by the farmers to take advantage of the proposed recommendation system. The result shows the promising future with better accuracy than previously available models in the same area. Parameters including recall, accuracy, precision, and F1-score are considered to gauge performance.
A novel multimodal model for detecting Vietnamese toxic news using PhoBERT and Swin Transformer V2 Le, Ngoc An; Hoang, Xuan Dau; Vu, Xuan Hanh; Ninh, Thi Thu Trang
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1350-1359

Abstract

News articles with fake, toxic or reactionary content are currently posted and spreaded very strongly due to the popularity of the Internet and especially the explosion of social networks and online services in cyberspace. Toxic news, especially reactionary news aimed at Vietnam, such as online articles spreading false information, slandering leaders, inciting destruction of the great national unity bloc, have a great impact on social life because they can spread quickly and have many forms of expression, such as news in the forms of text, images, videos, or a combination of text and images. Due to the seriousness of articles posting fake, toxic or reactionary news in cyberspace, there have been a number of studies in Vietnam and abroad for detection and prevention. However, most of the proposals focus on handling fake and toxic news posted using the English language. Furthermore, due to a large number of online news are posted in the form of images, or text embedded in images and videos, it is very difficult to process these news, leading to a relatively low detection rate. This paper proposes a multimodal model based on the combination of PhoBERT and Swin Transformer V2 for detecting fake and toxic news in both forms of text and images. Comprehensive experiments conducted on a dataset of 8,000 text and image news articles demonstrate that the proposed multimodal model surpasses both individual models and previous approaches, achieving 95% accuracy and 95% F1-score.
The development of contextual chat interactions with retrieval-augmented generation system for facilitating learning hadith Nurtantyana, Rio; Priyadi, Yudi; Darwiyanto, Eko
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp987-995

Abstract

This study explores the development and implementation of a retrieval-augmented generation (RAG) system using the large language model (LLM) to enhance the learning of hadith through a chat interface for high school students. This study addresses challenges in optimizing RAG configurations and problems associated with traditional educational methods that lack interactivity. In addition, the RAG system was designed to replace real teacher interactions, offering a chat feature that provides contextual answers to real-life scenarios related to Hadith. Various configurations were tested, with a focus on the Matn component, achieving a high accuracy score with a mean of .754 and demonstrating efficiency in context relevance with a mean of .797. Results indicated significant accessibility using our RAG system for learning hadith via WhatsApp’s chat interface. Hence, this study highlights the potential of RAG systems in transforming educational environments and offers insights into the development of technology for interactive Hadith learning solutions.
Deep learning-based multi-tier sensitivity analysis network for document sensitivity classification Ansari, Sadiya; Akther, Shameem
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1249-1260

Abstract

In the digital age, the exponential growth of data necessitates robust and efficient systems for document classification to maintain data security and compliance. Text classification plays a crucial role in identifying sensitive information by automatically categorizing documents based on their content. Using advanced machine learning and deep learning models, it analyzes text to detect keywords, patterns, and contextual cues that indicate the presence of sensitive data. This paper presents a novel framework, the multi-tier sensitivity analysis network (MTSAN), designed to accurately classify documents into public, private, and confidential categories. The proposed system integrates several advanced components, including the multi-tier sensitivity encoding network (MTSEN). MTSAN leverages a combination of convolutional networks and graph convolutional networks (GCNs) to capture both local and global contextual information. The dual-scope graph convolution block (DSGCB) is introduced to address both global dependencies and local dynamics, employing a novel fusion mechanism to merge global and local features effectively. Additionally, the cross-tier information fusion block (CTIFB) facilitates the seamless integration of multi-level features, further refining the classification process. The results demonstrate that the proposed MTSAN model outperforms traditional machine learning approaches and contemporary deep learning models such as bidirectional encoder representations from transformers (BERT), achieving superior accuracy and F1 scores in classifying sensitive information.
Advanced deep attention neural inference network for enhanced arrhythmia detection and accurate classification Sumitha, H.; Devanathan, M.
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1164-1175

Abstract

Arrhythmias are irregular heartbeats that can lead to severe health risks, including sudden cardiac death, necessitating accurate and timely detection for effective treatment. Traditional diagnostic methods such as stress tests, resting electrocardiograms (ECGs), and 24-hour Holter monitors are limited by their monitoring capacity and often result in delayed diagnoses, compromising patient safety. To address these challenges, this paper introduces the deep attention neural inference network (DANIN) methodology. DANIN integrates one-dimensional ECG signals with two-dimensional spectral images using multi-modal feature fusion, capturing comprehensive cardiac information in both temporal and frequency domains. The methodology employs advanced deep attention network-based models for superior feature extraction, recognizing intricate patterns and long-range dependencies within the data. Additionally, the inclusion of an inference model system enhances interpretability and usability, making the model highly suitable. Further, DANIN is evaluated considering the MIT-BIH dataset, and extensive comparative analysis with state-of-the-art techniques demonstrates that DANIN significantly improves accuracy, precision, recall, and F1-score, highlighting its potential to revolutionize arrhythmia detection and improve patient outcomes.
An implementation of GAN analysis for criminal face identification system Sarosh, Ayesha; Komali, Govindu; Battu, Vishnu Vardhan; Kocharla, Laxmaiah; Kopparavuri, Eswaree Devi; Obulesu, Ooruchintala; Mande, Praveen; Mohammad, Amanulla
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp963-972

Abstract

In recent times, the criminal activities are growing at an exponential rate. For the prevention of crime, one of the main issues that are before the police are accurate identification of criminals and on the other hand the availability of police officers are not adequate. The most tedious task is tracking the suspect once a crime was committed. Over the years, several technical solutions have been presented to detect the criminals however most of them were not effective. One of the most significant characteristics for the identification of a person is face. Even identical twins have their own unique faces. Face identification is a challenging topic in computer vision because the human face is a dynamic entity with a high degree of visual variation. In this area, identification accuracy and speed are significant challenges. Hence to solve these issues, an implementation of generative adversarial network (GAN) analysis for criminal face identification system is presented. GAN is used for the identification of criminals. Recall, precision, accuracy, and F1-score are used to assess the performance of the presented technique. Compared to previous models, this model will achieve better performance for criminal face detection.
Deepfake detection using convolutional neural networks: a deep learning approach for digital security Twince Tobing, Fenina Adline; Kusnadi, Adhi; Pane, Ivransa Zuhdi; Winantyo, Rangga
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1092-1099

Abstract

The development of artificial intelligence technology, especially deep learning, has facilitated the emergence of increasingly sophisticated deepfake technology. Deepfakes utilize generative adversarial networks (GANs) to manipulate images or videos, making it appear as if someone said or did things that never actually happened. As a result, deepfake detection has become a critical challenge, particularly in the context of the spread of false information and digital crime. The purpose of this research is to create a method for detecting deepfakes using a convolutional neural network (CNN) approach, which has been proven effective in visual pattern recognition. Through training with a dataset of original facial images and deepfakes, the CNN model achieved an accuracy of 81.3% in detecting deepfakes. The evaluation results for metrics such as precision, recall, and F1-score indicated good performance overall, although there is still room for improvement. This study is expected to make a significant contribution to enhancing digital security, especially in detecting visual manipulations based on deepfakes.
Comparative analysis of Cohen-Coon and Ziegler-Nichols tuning methods for three-phase induction motor with speed sensorless control Halim, Christian Vieri; Indriawati, Katherin
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp885-895

Abstract

The use of speed sensors in the speed controller of three-phase induction motors affects the reliability of the induction motors. In addition, the drive engine that is often used in industry is a three-phase induction motor. So, speed sensorless control is needed for induction motors to achieve the best performance. This study uses a discrete disturbance observer (D0) as feedback on the speed sensorless control. The controller used in this method is a discrete PI with the Cohen-Coon (CC) and Ziegler-Nichols (ZN) tuning method. The purpose of this study is to obtain a comparative analysis of the CC and ziegler nichols tuning method using a discrete PI on the speed sensorless control scheme with torque load variation. This study was carried out experimentally using an Alliance AY3A-90L4 induction motor. The results show that the CC tuning method is better under parameter efficiency and robustness against disturbance and ZN is better under parameter reliability.
A hybrid machine learning approach for malicious website detection and accuracy enhancement Abu-Khadrah, Ahmed; Alkhamis, Shayma; Ali, Ali Mohd; Jarrah, Muath
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1027-1034

Abstract

Malicious URLs are web addresses purposely generated for a user’s detriment. Some examples include phishing scams in which the victim is fooled into logging into a fake site or portals for downloading malware where any click on a link invites a hostile program to the user’s device. The damage done to an individual’s finances, confidential information, and even reputation due to malicious URLs makes it crucial to devise means of countering these threats. This can be achieved by creating an intelligent model that identifies suspicious characteristics common to these websites. The objective of this research is to design a novel hybrid machine learning algorithm-based model for detecting malicious websites. A random forest, decision tree, and extreme gradient boosting (XGBoost) are the three hybrid classification algorithms proposed for the study. Accuracy in detection will help prevent and reduce the effects of such websites. The accuracy rate in this research is 98.7%, precision is at 98.9%, and recall at 98.5%. With these results, it follows that the hybrid model is more effective than training any individual algorithm with the given dataset.

Page 892 of 914 | Total Record : 9138

 890   891   892   893   894 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue