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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 :
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Articles 66 Documents
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Search results for , issue "Vol 35, No 2: August 2024" : 66 Documents clear
AMSVT: audio Mel-spectrogram vision transformer for spoken Arabic digit recognition Mahmoudi, Omayma; El Allali, Naoufal; Bouami, Mouncef Filali
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1013-1021

Abstract

This work presents a novel model to recognize spoken digits in the Arabic language. Due to the transformer-based models' tremendous success in natural language processing (NLP), several attempts have been made to extend transformer-based designs to other domains, such as vision and audio. However, our approach consists of extracting and inputting Mel-spectrogram features into our model of the proposed audio Mel-spectrogram vision transformer (AMSVT) for training. The signal processing community has been interested in these models due to the successful use of vision transformers (ViT) in several computer vision applications. This is because signals are frequently recorded as spectrograms (using the Mel-spectrogram, for example), which may be given directly as input to vision transformers. Our model outperformed a group of models in terms of accuracy and time, such as convolutional neural network (CNN)-based and recurrent neural network (RNN)-based.
Machine learning for real estate valuation: Astana, Kazakhstan case Barlybayev, Alibek; Sankibayev, Arman; Niyazova, Rozamgul; Akimbekova, Gulnara
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1110-1121

Abstract

Purpose of this research is to investigate the accuracy of machine learning models in forecasting and evaluating house prices, and to understand the key factors that impact pricing. The study involved analyzing data scraped from real estate ads in the “sale of secondary housing” category on the website krisha.kz. The paper emphasizes the importance of understanding the factors that affect house prices, such as quality, location, size, and building materials. It was concluded that these factors have a strong correlation with house price prediction. The information available on krisha.kz was found to be a useful resource for finding good apartments. The data collected by the scraper was analyzed by models: Linear regression (LR), interactions linear regression (ILR), robust linear regression (RLR), fine tree regression (FTR), medium tree regression (MTR), coarse tree regression (CTR), linear support vector machine (LSVM), quadratic SVM (QSVM), medium gaussian SVM (MGSVM), rational quadratic gaussian process regression (RQGPR), boosted trees (BoosT), bagged trees (BagT), neural network based on the bayesian regularization algorithm (BR-BPNN). BR-BPNN showed better results than other models, with an MSE of 32.14 and R of 0.9899.
Employing educational data mining techniques to predict programming students at-risk of dropping out B. Casillano, Niel Francis; Cantilang, Karen W.
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1219-1226

Abstract

This research primary aimed at evaluating various predictive models in predicting programming students at risk of dropping out. It also aimed at identifying attributes that are significant in predicting students at risk of dropping. The educational data mining process (EDM) was utilized as the research framework. The study conducted a ten-fold cross-validation, revealing that the k-nearest neighbors (kNN) algorithm achieved the highest classification accuracy at 95.5%. The decision tree model followed closely with a 94.9% accuracy, logistic regression exhibited 94.4%, and the neural network model yielded a classification accuracy of 93.2%. Further analysis, including confusion matrices and receiver operating characteristic (ROC) curves, provided detailed insights into the models' performance. Notably, the decision tree algorithm excelled in identifying students who did not drop out, with a misclassification rate of 9 out of 30 for dropped students. Analysis also showed that students’ assignments completed (AC), laboratory work (LW), and attendance (ATT) were the strongest predictors in identifying students at risk of dropping. Results of the study can be used by instructors to identify in advance student at risk of dropping and provide them with the necessary intervention to improve performance in programming.
A time-efficient nonlinear control method for the hyperchaotic finance system synchronization Haris, Muhammad; Shafiq, Muhammad; Ahmad, Israr; Ali, Zulfiqar; Manickam, Geethalakshmi; Ghaffar, Abid
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp834-843

Abstract

Irregular and complex behavior in the financial system can disrupt stability and smooth economic growth. It causes randomness within the system, generating chaos; hindering synchronization behaviour. Achieving smooth and rapid synchronization between two coupled hyperchaotic finance (HF) systems with lessened fluctuation of input and output signals is vital for continuing financial stability and fostering economic growth, a challenge addressed in this article. The paper proposes a novel time-efficient nonlinear control (TENLC) technique and investigates HF systems synchronization using the drive-response system (DRS) arrangement. The proposed TENLC strategy realizes fast and smooth synchronization behaviour between two coupled HF systems, reducing closed-loop state-variable trajectory oscillations. The controller is designed to retain the nonlinear components within the closed-loop system and does not depend on the system's parameters, simplifying the design and analysis process. The Lyapunov stability technique confirms the closed-loop's global stability at the origin. Proofs of mathematical analysis and computer-based simulation results validate the theoretical findings, showing that the presented TENLC strategy converges the state error trajectories to zero in a short transient time with lessened fluctuations for all signals. The comparative computer-based simulation analysis confirms that the presented TENLC approach outperforms other synchronization control techniques.
Adjusted TextRank for keyword extraction in petrochemical project correspondence documents Atmoko, Indri; Yulianti, Evi; Jiwanggi, Meganingrum Arista
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1171-1180

Abstract

A large petrochemical construction project is typically executed by multiple parties, all bound by contract agreement. During the execution phase, issues and problems may arise because the work details are not clearly specified in the contractual agreement. These issues are formally communicated and documented through written correspondence letters. By identifying important keywords within these formal letters, a comprehensive narrative of the project, including its associated issues, can be identified and analyzed. In this research, we introduce an adjusted TextRank algorithm that integrates external features from the Indonesian FastText language model and term frequency-inverse document frequency (TF-IDF) scores to identify important keywords within a dataset of correspondence letters of petrochemical projects. This enhancement involves refining phrase detection, semantic relationship estimation between words, and part-of-speech (POS) identification for words or phrases. Our results show that the proposed adjustments result in improved evaluation scores compared to the baseline standard TextRank and standard TF-IDF, respectively by 24.1% and 25% in terms of F-1 scores.
Hybrid fuzzy logic and gravitational search algorithm based routing for wireless sensor networks Ramappa, Shwetha Golluchinnappanahalli; Subbarao Venkata Narayana, Murthy
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1296-1310

Abstract

Over the recent years, the wireless sensor network (WSN) has garnered significant attention from both researchers and the general populace. Its application in diverse environmental scenarios, including weather monitoring, temperature regulation, humidity tracking, and military surveillance, extends beyond conventional boundaries. WSNs consist of numerous nodes, each functioning as a sensor with the primary responsibility of data sensing. These nodes operate under constraints such as power, energy, efficiency, and deployment considerations. Moreover, the power and other resources cannot be replaced and renewed therefore prolonging the network lifetime has become the main aspect of WSNs. Energy aware routing play’s important role to ensure the efficient data transmission with minimal power consumption and ensures the prolonged network lifetime. In this work, we focus on optimizing the routing process therefore we present a hybrid model which uses fuzzy logic for path identification and gravitational search optimization (GSA) for efficient path selection. The fuzzy logic considers energy consumption, residual energy, distance and delay parameters to identify the most suitable path for data transmission. The experimental analysis shows a significant improvement in network lifetime, delay an aspect delivery.
The impact of feature extraction techniques on the performance of text data classification models Maiti, Abdallah; Abarda, Abdallah; Hanini, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1041-1052

Abstract

Sentiment analysis is a crucial discipline that focuses on the interpretation of feelings and points of view in textual data. Our study aims to assess the impact of different feature extraction methods on the accuracy of opinion research models. Techniques such as bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), Word2Vec, global vectors (GloVe) and bidirectional encoder representations from transformers (BERT) were used with three machine learning algorithms and three deep learning networks as classifiers. The IMDB movie review dataset was used for evaluation. The results showed that combining BERT with LSTM, CNN and RNN improved performance, achieving an accuracy rate of 94%, precision of 94.14%, recall of 93.27% and an F1 score of 89.33%. These results highlight the significant contribution of ERTB to model performance, outperforming other feature extraction techniques in text classification. The study concludes that the fusion of BERT and LSTM significantly improves model accuracy for opinion retrieval, recommending BERT as the main feature extraction method for optimizing performance in NLP tasks.
Emotion recognition from Burmese speech based on fused features and deep learning method Mar, Lwin Lwin; Pa, Win Pa
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp888-897

Abstract

Burmese language is challenging for speech emotion classification. Moreover, it is lack of resource and few research was made in this topic. To solve the challenging problem, novel feature extraction for Burmese language is proposed. For lack of resource, Burmese speech emotion corpus called BMISEC is built. To support the challenging problem, the advantages of feature extractions are fused to create a robust feature. Four features are fused. Novel text-tone feature, local binary pattern, mel-frequency cepstral coefficient and discrete wavelet transform are fused. To progress the performance, deep learning method called DenseNet-Emotion is used for classification. Support vector machine is used in DenseNet’s classifier layer. To show the robustness of the proposed system, three types of experiments are made on Tensorflow framework. They are ablation study, experiments with three publicly available datasets and experiments with the previous research methods and they are compared with the proposed method. It is found that feature fusion is superior to only one feature in emotion recognition. BMISEC gets better performance than other datasets. Moreover, the proposed method gets the superior result than previous research methods. The proposed method gets the accuracy of 88.388% for 50 epochs.
Impacts of Eu2+ -doped K3LuSi2O7 phosphor and a scattering particle on conventional white light emitting diodes Duy, Le Doan; Thai, Nguyen Le; Cong, Pham Hong; Tran, Thinh Cong
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp743-750

Abstract

The K3LuSi2O7 phosphor doping Eu2+ rare-earth ions (KLS:Eu) was reported to possess broad emission band from near-ultraviolet to nearinfrared. Additionally, this phosphor showed a wide absorption band of 250-600 nm, allowing it to be excited by blue-light chip of 460 nm, making it one of the suitable phosphor materials for a light emitting diode (LED). Besides, the scattering particle material CaCO3 is incorporated into the yellow phosphor layer to serve the scattering-enhancement purpose. The combination of both materials aims at accomplishing improvements in performance of commercial LED package. The concentration of KLS:Eu is constant while that of CaCO3 is modified. As a result, the scattering factor is regulated and become the key factor influencing the optical outputs of the simulated LED. The increasing CaCO3 concentration enhances the phosphor scattering efficiency of light, helping to improve the lumen output and color-temperature consistency of the LED. However, the color rendering performance declines as a function of the CaCO3 growing amount, despite the presence of a KLS:Eu phosphor layer. Further works should be done to optimize the application of KLS:Eu in cooperation with scattering particles for a higher-quality LED device.
Hybrid encryption based on a generative adversarial network Amir, Iqbal; Suhaimi, Hamizan; Mohamad, Roslina; Abdullah, Ezmin; Pu, Chuan-Hsian
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp971-978

Abstract

In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against different configurations. Neural network models were developed for Alice, Bob, and Eve using random datasets and encryption. The models were trained adversarially using the GAN to find optimal parameters, and their performance was analyzed by studying Bob’s and Eve’s accuracy and bits error. The parameters of 8,000 epochs, a batch size of 4,096, and a learning rate of 0.0008 resulted in 100% accuracy for Bob and 52.14% accuracy for Eve. This implies that Alice and Bob’s neural network effectively secured the messages from Eve’s neural network. The findings highlight the advantages of employing neural network-based encryption methods, providing valuable insights for advancing the field of secure communication and data protection.

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