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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 111 Documents
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Search results for , issue "Vol 14, No 5: October 2024" : 111 Documents clear
Named entity recognition on Indonesian legal documents: a dataset and study using transformer-based models Yulianti, Evi; Bhary, Naradhipa; Abdurrohman, Jafar; Dwitilas, Fariz Wahyuzan; Nuranti, Eka Qadri; Husin, Husna Sarirah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5489-5501

Abstract

The large volume of court decision documents in Indonesia poses a challenge for researchers to assist legal practitioners in extracting useful information from the documents. This information can also benefit the general public by improving legal transparency, law enforcement, and people's understanding of the law implementation in Indonesia. A natural language processing task that extracts important information from a document is called named entity recognition (NER). In this study, the NER task is applied to legal domains, which is then referred to as legal entity recognition (LER) task. In this task, some important legal entities, such as judges, prosecutors, and advocates, are extracted from the decision documents. A new Indonesian LER dataset is built, called IndoLER data, consisting of approximately 1K decision documents with 20 types of fine-grained legal entities. Then, the transformer-based models, such as multilingual bidirectional encoder representations from transformers (BERT) or M-BERT, Indonesian BERT or IndoBERT, Indonesian robustly optimized BERT pretraining approach (RoBERTa) or IndoRoBERTa, XLM (cross lingual language model)-RoBERTa or XLMR, are proposed to solve the Indonesian LER task using this dataset. Our experimental results show that the RoBERTa-based models, such as XLM-R and IndoRoBERTa, can outperform the state-of-the-art deep-learning baselines using BiLSTM (bidirectional long short-term memory) and BiLSTM-conditional random field (BiLSTM-CRF) approaches by 7.2% to 7.9% and 2.1% to 2.6%, respectively. XLM-RoBERTa is shown to be the best-performing model, achieving the F1-score of 0.9295.
Demographic information combined with collaborative filtering for an efficient recommendation system Nabil, Sana; Chkouri, Mohamed Yassin; Bouhdidi, Jaber El
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5916-5925

Abstract

The recommendation system is a filtering system. It filters a collection of things based on the historical behavior of a user, it also tries to make predictions based on user preferences and make recommendations that interest customers. While incredibly useful, they can face various challenges affecting their performance and utility. Some common problems are, for example, when the number of users and items grows, the computational complexity of generating recommendations increases, which can increase the accuracy and precision of recommendations. So, for this purpose and to improve recommendation system results, we propose a recommendation system combining the demographic approach with collaborative filtering, our approach is based on users’ demographic information such as gender, age, zip code, occupation, and historical ratings of the users. We cluster the users based on their demographic data using the k-means algorithm and then apply collaborative filtering to the specific user cluster for recommendations. The proposed approach improves the results of the collaborative filtering recommendation system in terms of precision and recommends diverse items to users.
Deep learning model for elevating internet of things intrusion detection Dash, Nitu; Chakravarty, Sujata; Rath, Amiya Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5874-5883

Abstract

The internet of things (IoT) greatly impacts daily life by enabling efficient data exchange between objects and servers. However, cyber-attacks pose a serious threat to IoT devices. Intrusion detection systems (IDS) are vital for safeguarding networks, and machine learning methods are increasingly used to enhance security. Continuous improvement in accuracy and performance is crucial for effective IoT security. Deep learning not only outshines traditional machine learning methods but also holds untapped potential in fortifying IDS systems. This paper introduces an innovative deep learning framework tailored for anomaly detection within IoT networks, leveraging bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU) architectures. The hyper parameters of the proposed model are optimized using the JAYA optimization technique. These models are validated using IoT-23 and MQTTset datasets. Several performance metrics including accuracy, precision, recall, f-score, true negative rate (TNR), false positive rate (FPR), and false negative rate (FNR), have been selected to assess the effectiveness of the suggested model. The empirical results are scrutinized and juxtaposed with prevailing approaches in the realm of intrusion detection for IoT. Notably, the proposed method emerges as showcasing superior accuracy when contrasted with existing methods.
Biometric classification system for dorsal finger creases utilizing multi-block circular shift combination local binary pattern Riaz, Imran; Ali, Ahmad Nazri; Ibrahim, Haidi; Huqqani, Ilyas Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5234-5243

Abstract

In recent years, there has been a growing interest in biometric recognition based on finger dorsal patterns, making it a significant area of research. This paper introduces a biometric classification system that utilizes dorsal finger middle creases. The viability of this trait is assessed through the implementation of a new method known as multi block circular shift combination local binary pattern (MBCSC-LBP). The MBCSC-LBP approach involves dividing the image into multiple blocks to enhance robustness and capture both local and global information, thereby extracting discriminative features. These features from each block are then concatenated to form a comprehensive feature vector. To evaluate the accuracy of the proposed MBCSC-LBP feature extractor, a support vector machine (SVM) with a linear kernel is utilized. The classification accuracy achieved by this method is 96.22% indicating a promising performance.
Design of a high-speed 7.2 Gbps/lane receiver for MIPI D-PHY interface utilizing 18 nm FinFET technology Hoang, Trang; Ha, Anh Nam
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4956-4969

Abstract

This study presents an advanced design for a high-speed receiver tailored for the MIPI D-PHY Interface, capable of handling data rates up to 7.2 Gbps per lane. The design is developed using 18 nm fin field-effect transistor (FinFET) technology and is rigorously simulated under varying process, voltage, and temperature conditions (PVTs) to ensure robustness. The architecture of the receiver integrates several key components: differential pair sensing, a folded cascode continuous time linear equalization (CTLE), a single-ended operational amplifier, and a cross-coupled stage. Operating at a supply voltage of 0.72 V in the worst-case scenario, our CTLE achieves a peaking gain of 17.77 dB at 4.26 GHz. The design demonstrates a maximum jitter of 19.63 ps at an offset voltage of ±2 mV. Notably, the power efficiency of our receiver is optimized to 0.85 mW/Gb/s, totaling 6.1 mW, with dual supply voltages of 1.98 and 0.88 V. This work contributes to the field by offering a highly efficient solution for fast data transmission with reduced power consumption and enhanced signal integrity.
Enhancing the resistance of password hashing using binary randomization through logical gates Anbari, Muhamad Zaki; Sugiantoro, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5400-5407

Abstract

Digitalization in various sectors makes information security issues very crucial. Information security follows the authentication, authorization, and accounting (AAA) principle, where one of the most important parts is authentication. The most widely used authentication method is username-password. The best method to secure a user-pass is to convert the plaintext using a hash so that the converted plaintext cannot be recovered. However, with higher technology, hackers can crack the ciphertext using brute force. This research proposes a username-password scrambling algorithm before it is fed into the hash function to improve resilience from attacks. This algorithm is named logical gates (LG). It works by converting the user pass into binary form, adding salt, and scrambling it with certain logical gates before inserting it into the hash function. Testing is divided into two: time of execution and attack resistance. Time of execution results show that LG takes 0.0443432033 s, while without LG takes 0.01403197646 s. The resistance of attack results show that the plaintext of the hash amplified by LG cannot be cracked at all and increases the attack time by 321.3% at prefix and 161.3% at postfix, while without LG, the plain text can be found for a certain duration of time.
Blood glucose prediction using non-invasive optical system based on photoplethysmography Reguig, Mohammed Anes Bereksi; Labdelli, Nassima
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5200-5208

Abstract

Several people must frequently evaluate their blood glucose since it is an important indicator of health problems mainly diabetes. Different medical systems are commercialized to measure blood glucose levels; some are invasive others are noninvasive. The main purpose of this article is to develop a non-invasive device for measuring blood glucose levels based on the detection and analysis of the photoplethysmogram signal. The developed systems include an optical sensor to detect the photoplethysmography (PPG) signal, digitalizing and acquiring boards to a computer and a software program to process and analyze the digitalized PPG signal regarding some features extracted from its waveform. These features are the systolic amplitude Sa and the b/a amplitude ratio in the second derivative PPG (SDPPG) waveform. An invasive glucometer is also used along with the Sa and b/a ratio determined from the developed system to generate a calibration model which is used to deduce blood glucose level (BGL) values. The result showed that the calibration model using the b/a ratio is more accurate for non-invasive blood level measurement then that of Sa with a difference in glucose estimation around 2 mg/dl and with the correlation coefficient (R2) of the glucose level prediction between 0.8904 and 0.9775.
Comparison between incremental conductance and perturb and observe algorithms in photovoltaic system under low temperature and irradiation levels Abssane, Sara; Outzourhit, Abdelkader; Amatoul, Fatima Zahra
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4897-4906

Abstract

This paper compares two mostly used maximum power point tracking (MPPT) methods, perturb and observe (P&O), and incremental conductance (INC), for a photovoltaic (PV) system integrated with a step-up converter and resistive load. The evaluation was conducted using MATLAB/Simulink under two specific environmental conditions: low irradiation and temperature levels. The results indicate that under irradiation levels below 200 W/m², the INC algorithm outperforms P&O by exhibiting minimal fluctuations and achieving higher efficiency. Conversely, under low temperature (below 25 °C) the P&O method reaches the highest efficiency, exceeding 99%. These findings highlight the importance of selecting the appropriate MPPT algorithm based on specific environmental conditions to optimize the energy output of PV systems.
A novel approach to simplified and secure message cryptography using chaotic logistic maps and index keys Al-Ofeishat, Hussein Ahmad; Alkasassbeh, Jawdat S.; Alzyoud, Khalaf Y.; Al-Taweel, Farouq M.; Alrawashdeh, Hisham; Al-Rawashdeh, Ayman Y.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5139-5152

Abstract

This paper proposes a novel method of message cryptography aiming to provide a simple, secure, and highly efficient approach to encryption and decryption. Unlike existing methods that rely on complex logical operations, our method utilizes simple rearrangement operations, reducing computational complexity while ensuring robust security. It employs a sophisticated, high-entropy private key designed to withstand hacking attempts. This key generates two chaotic keys using chaotic logistic map models, which are sorted to form two index keys essential for rearranging message blocks and characters during encryption and decryption. The process is facilitated by two simple operations, Get_index and Get_min, based on the index keys. These operations achieve streamlined procedures without compromising security. The method's speed is evaluated across various message lengths, demonstrating significant improvements in encryption time and throughput. The comparative analysis highlights the superior efficiency of this method compared to existing methods. Rigorous testing confirms that the proposed method meets stringent quality and sensitivity requirements, ensuring robust cryptographic standards. This innovative approach offers a promising solution for secure message encryption and decryption, combining simplicity, security and efficiency to meet the evolving needs of secure communication systems.
Optimizing heart disease prediction through ensemble and hybrid machine learning techniques Reddy, Nomula Nagarjuna; Nipun, Lingadally; Baba, MD Uzair; Rishindra, Nyalakanti; Shilpa, Thoutireddy
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5744-5754

Abstract

In this modern era, heart diseases have surfaced as the leading factor of fatalities, accounting for around 17.9 million lives annually. Global deaths from heart diseases have surged by 60% over the last 30 years, primarily because of limited human and logistical resources. Early detection is crucial for effective management through counseling and medication. Earlier studies have identified key elements for heart disease diagnosis, including genetic predispositions and lifestyle factors such as age, gender, smoking habits, stress, diastolic blood pressure, troponin levels, and electrocardiogram (ECG). This project aims to develop a model that can identify the best machine learning (ML) algorithm for predicting heart diseases with high accuracy, precision, and the least misclassification. Various ML techniques were evaluated using selected features from the heart disease dataset. Among these techniques, a combination of random forest (RF), multi-layer perceptron (MLP), XGBoost, and LightGBM employing an ensemble method with a stacking classifier, along with logistic regression (LR) as a metamodel, achieved the highest accuracy rate of 95.8%. This surpasses the efficiency of other techniques. The suggested method provides an encouraging framework for early prediction, with the overarching goal of reducing global mortality rates associated with these conditions.

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