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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
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SMOTE tree-based autoencoder multi-stage detection for man-in-the-middle in SCADA Rolansa, Freska; Istiyanto, Jazi Eko; Afiahayati, Afiahayati; Kusuma Frisky, Aufaclav Zatu
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp133-144

Abstract

Security incidents targeting supervisory control and data acquisition (SCADA) infrastructure are increasing, which can lead to disasters such as pipeline fires or even lost of lives. Man-in-the-middle (MITM) attacks represent a significant threat to the security and reliability of SCADA. Detecting MITM attacks on the Modbus SCADA networks is the objective of this work. In addition, this work introduces SMOTE tree-based autoencoder multi-stage detection (STAM) using the Electra dataset. This work proposes a four-stage approach involving data preprocessing, data balancing, an autoencoder, and tree classification for anomaly detection and multi-class classification. In terms of attack identification, the proposed model performs with highest precision, detection rate/recall, and F1 score. In particular, the model achieves an F1 score of 100% for anomaly detection and an F1 score of 99.37% for multi-class classification, which is preeminence to other models. Moreover, the enhanced performance of multi-class classification with STAM on minority attack classes (replay and read) has shown similar characteristics in features and a reduced number of misclassifications in these classes.
Designing stair climbing wheelchairs with surface prediction using theoretical analysis and machine learning Chawaphan, Pharan; Maneetham, Dechrit; Crisnapati, Padma Nyoman
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp120-132

Abstract

Urban settings present considerable obstacles for those use personal mobility wheelchairs, especially when it comes to manoeuvring stairs. The objective of this study is to improve the safety and ease of use of wheelchairs designed for ascending stairs. The study aims to tackle the significant issue of instability and limited ability to adjust to different types of terrain. This research employs a holistic methodology that combines theoretical dynamic analysis, hardware design and simulation, and field testing, in addition to advanced machine learning approaches for surface prediction. Theoretical models guarantee the stability of the wheelchair, while hardware simulations offer valuable insights into its structural integrity. The data obtained from inertial measurement unit (IMU) sensors during field tests is analysed and categorised using models like random forest and gradient boosting, which exhibit exceptional accuracy in forecasting movement circumstances. The results demonstrate that the implementation of these combined techniques greatly enhances the wheelchair’s capacity to safely manoeuvre over urban barriers. The study finds that the suggested solutions show great potential for creating intelligent mobility aids, which might be used to improve accessibility for those with mobility limitations.
G2M weighting: a new approach based on multi-objective assessment data (case study of MOORA method in determining supplier performance evaluation) Hendrastuty, Nirwana; Setiawansyah, Setiawansyah; An’ars, M. Ghufroni; Rahmadianti, Fitrah Amalia; Saputra, Very Hendra; Rahman, Miftahur
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp403-416

Abstract

Criteria weighting methods in decision support system (DSS) face various challenges and limitations that can affect their accuracy and reliability. One of the main challenges is subjectivity, this subjective assessment can reduce the objectivity and consistency of results. The main objective of the new weighting method grey geometric mean (G2M) weighting is to provide more objective and robust criteria weights under conditions of uncertainty and incomplete data. The new G2M weighting approach has a significant potential impact on the DSS field, it has the potential to generate more effective and efficient decisions, which can improve organizational performance, reduce risk and optimize outcomes. Pearson correlation test results of two sets of rankings generated by DSS methods namely grey relational analysis (GRA), simple additive weighting (SAW), multi-attributive ideal-real comparative analysis (MAIRCA), weighted product (WP), combined compromise solution (COCOSO), vlsekriterijumska optimizacija i kompromisno resenje (VIKOR), and a new additive ratio assessment (ARAS) that there is a strong positive correlation between the two methods using G2M weighting criteria. The high correlation value indicates that the rankings of the methods used tend to move together, giving confidence in the consistency and validity of the resulting ranking results. This gives confidence that both methods can be used simultaneously or interchangeably with consistent results. The use of G2M weighting in the DSS method used can support better decision-making by providing consistent information and validity of ranking results.
Improvement the cogging torque reduction methods by combining the magnet slotted and gradually inclined surface end in permanent magnet generator Abduh, Syamsir; Fikri, Miftahul; Nur, Tajuddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp32-38

Abstract

Cogging torque (CT) in permanent magnet synchronous (PMS) machine, generator or electric motor should be reduced to increase the preperformance in application. Many CT reduction techniques has been proposed in the last few years. This research dealt with the study of techniques for reduction of the CT in PMSG. The PMS generator investigated in this paper is the integral slot number type with 18 slots and 6 poles. The CT has been analyzed to be reduced by employ the slot opening width variation, magnet edges slotting, and gradually inclined surface end. This paper also has analyzed the effect of combination of slot opening width and slotting permanent magnets. The finite element method magnetics (FEMM) is used in this work to perform electromagnetic simulations of the PMSG. Using the FEMM, the CT reduction of permanent magnet synchronous generators studied is analyzed and the CT peak value is compared. It is found that by combining of reduced of slot opening and slotting the permanent magnets can reduce the CT of PMS generator significantly abound 98.55% compared with the base line model.
Advancing supply chain management through artificial intelligence: a systematic literature review Younesse, Ouahbi; Soumia, Ziti; Souad, Lagmiri Najoua
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp321-332

Abstract

This study evaluates the role and impact of artificial intelligence (AI) in supply chain management (SCM). Following a five-step process, the review covered academic publications from 2000 to 2024, drawing from different databases. The review identified 426 relevant articles for analysis, focusing on AI techniques. The analysis explored their applications, advantages, and barriers to adoption in SCM. The study also discussed key challenges, including financial, organizational, strategic, technological, and legal barriers. The findings suggest that while AI techniques offer significant potential for improving SCM, several obstacles hinder their broader implementation. Addressing these obstacles requires investments in infrastructure, skills development, and effective change management.
HorseNet: a novel deep learning approach for horse health classification Atitallah, Nesrine; Abdel-Wahab, Ahmed; Hadi, Anas A.; Abdel-Jaber, Hussein; Mohamed, Ali Wagdy; Elsersy, Mohamed; Mansour, Yusuf
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp555-568

Abstract

In equestrian sports and veterinary medicine, horse welfare is paramount. Horse tiredness, lameness, colic, and anemia can be identified and classified using deep learning (DL) models. These technologies analyze horse images and videos to help vets and researchers find symptoms and trends that are hard to see. Early detection and better treatment of certain disorders can improve horses’ health. DL models can also improve with new data, improving diagnosis accuracy and efficiency. This study comprehensively evaluates three convolutional neural network (CNN) models to distinguish normal and abnormal horses using the generated horse dataset. For this study, a unique dataset of horse breeds and their normal and abnormal states was collected. The dataset includes mobility patterns from this study’s initial data collection. DL models like CNNs and transfer learning (TL) models (visual geometry group (VGG)16, InceptionV3) were employed for categorization. The InceptionV3 model outperformed CNN and VGG16 with over 97% accuracy. Its depth and multi-level structure allow the InceptionV3 model to recognize characteristics in images of varied scales and complexities, explaining its excellent performance.
A novel mobile application for personality assessment based on the five-factor model and graphology Remaida, Ahmed; Sabri, Zineb; Abdellaoui, Benyoussef; Fri, Chakir; Lakhchaf, Yassine; El Idrissi, Younès El Bouzekri; Lafraxo, Mohammed Amine; Moumen, Aniss
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp915-927

Abstract

With the rising interest over the last decade, automated graphology has emerged as a promising filed of research, providing new insights on personality traits prediction on the basis of handwriting analysis. Although, few practical solutions to automate the extraction of handwriting features and personality prediction exist in the literature. This work aims to contribute to closing the gap in automated handwriting personality prediction by proposing a novel mobile application that uses robust feature extraction and machine learning models to predict big five personality traits. Our findings, based on high correlations between handwriting characteristics and personality traits, revealed convincing links. Notably, extraversion and extraversion have strong correlations with top margin feature, whereas agreeableness is expressed through line spacing. These findings emphasize the ability of automated graphology to properly interpret individual personalities. The proposed system achieved exceptional accuracy by using well known machine learning classifiers. The testing accuracy exceeded 92% in binary classification and 87% in multi-class case scenario, proving the adaptability and dependability of the system’s architecture. Our Android app promises to provide users with unprecedented insights into their personalities, establishing a robust tool for psychological assessment and self-discovery.
Measuring political influence during elections using a deep learning approach Cherkaoui, Abderrazzak; El Beqqali, Omar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1273-1288

Abstract

This contribution introduces a methodology for measuring political influence on Twitter during the 2020 U.S. presidential election campaign. The approach employs deep knowledge scores, which are generated through sentiment analysis of Tweets from users responding to influential users, coupled with an assessment of the strength of their interactions. The deep knowledge scores enable the categorization of three types of Twitter’s users engaging with influential users: influenced users, distrustful users, and connected users. Our approach, structured around a five-layer framework, effectively constructs networks of trust and distrust, and establishes the relationship between fluctuations in trust or distrust levels and the topics discussed by influential users.
RNN-driven integration of spatial, temporal, features for Indian sign language recognition and video captioning Pol, Ajay Manohar; Patil, Shrinivas A.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp821-829

Abstract

This paper presents a novel model that integrates spatial features from residual blocks and temporal features from FFT, alongside a sophisticated RNN architecture comprising BiLSTM, gated recurrent units (GRU) layers, and multi-head attention. Achieving nearly 99% accuracy on both WLASL and INCLUDE datasets, this model outperforms standard CNN pretrained models in feature extraction. Notably, the BiLSTM and GRU combination proves superior to other combinations such as LSTM and GRU. The BLEU score analysis further validates the model's efficacy, with scores of 0.51 and 0.54 on the WLASL and INCLUDE datasets, respectively. These results affirm the model's proficiency in capturing intricate spatial and temporal nuances inherent in sign language gestures, enhancing accessibility and communication for the deaf and hard-of-hearing communities. The comparison highlights the superiority of this paper's proposed model over standard approaches, emphasizing the significance of the integrated architecture. Continued refinement and optimization hold promise for further augmenting the model's performance and applicability in real-world scenarios, contributing to inclusive communication environments.
Word embedding for contextual similarity using cosine similarity Asri, Yessy; Kuswardani, Dwina; Sari, Amanda Atika; Ansyari, Atikah Rifdah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1170-1180

Abstract

Perspectives on technology often have similarities in certain contexts, such as information systems and informatics engineering. The source of opinion data comes from the Quora application, with a retrieval limit of the last 5 years. This research aims to implement Indo-bidirectional encoder representations from transformers (BERT), a variant of the BERT model optimized for Indonesian language, in the context of information system (IS) and information technology (IT) topic classification with 414 original data, which, after being augmented using the synonym replacement method, The generated data becomes 828. Data augmentation aims to evaluate the performance of models by using synonyms and rearranging text while maintaining meaning and structure. The approach used is to label the opinion text based on the cosine similarity calculation of the embedding token from the IndoBERT model. Then, the IndoBERT model is applied to classify the reviews. The experimental results show that the approach of using IndoBERT to classify SI and IT topics based on contextual similarity achieves 90% accuracy based on the confusion matrix. These positive results show the great potential of using transformer-based language models, such as IndoBERT, to support the analysis of comments and related topics in Indonesian.

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