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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
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Forest fire risk monitoring using fuzzy logic and IoT technology Sahour, Abdelhakim; Boumehrez, Farouk; Maamri, Fouzia; Djellab, Hanane; Abdelali, Bakhouche
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5242

Abstract

Forest fire is one of the leading causes of ecological damage and environmental problems. This work aims to develop a forest fire risk monitoring system in which an artificial intelligence technique, fuzzy logic, has been used to determine the forest method risk (temperature, relative humidity, and wind speed). Fuzzy set theory implements categories or groupings of data whose boundaries are not clearly defined (i.e. fuzzy), consisting of rule bases, membership functions, and inference methods. We also use wireless sensor networks (WSN) and Internet of Things (IoT) technologies. In order to collect environmental information through WSN based environmental sensors, the collected information is transmitted to a database on a server through an Internet connection. Users can monitor the saved data using an internet browser in each whey. This provides the ability to analyze detailed data and then take the necessary precautions to protect threatened forests.
Classification of Cardiovascular Disease Based on Lifestyle Using Random Forest and Logistic Regression Methods Bietrosula, Ajyan Brava; Werdiningsih, Indah; Wuriyanto, Eto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5388

Abstract

Cardiovascular disease is a non-communicable disease caused by a disturbance in the function of the heart or blood vessels. According to WHO country profile data released in 2018 regarding non-communicable diseases, cardiovascular disease is the highest cause of death in Indonesia. This study aims to classify cardiovascular disease based on lifestyle using the Random Forest and Logistic Regression methods. In the classification process with the Random Forest and Logistic Regression machine learning methods, a combination of parameters from each machine learning method will be tested to see which parameter combination is the best for processing and classifying cardiovascular disease datasets. The dataset used in this research is obtained from Kaggle called Cardiovascular Disease. The dataset was processed through several pre-processing stages, namely missing value imputation, outlier detection, and extreme data checking. After going through the preprocessing process, the amount of data that entered the classification process was 62478 rows of data with 13 attributes or columns, namely age, height, weight, gender, systolic blood pressure, diastolic blood pressure, cholesterol, glucose, smoking, alcohol intake, physical activity, and cardiovascular disease. Dividing the dataset into different percentage distributions of training data and testing data was also tested to see the difference in classification performance of the two methods. The division of training data was 90% and testing data is 10%. The results obtained from this study were the Logistic Regression method had better accuracy results of 73.07% compared to Random Forest with an accuracy result of 71.87%.
A 76 GHz Millimeter-Wave Marine Radar Antenna Design Ahmed, M. F.; Elshamy, M. A.; Shaalan, A. A.
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5512

Abstract

In this work, a 76 GHz microstrip antenna array is proposed for usage in the mm-wave Marine radar application. Millimeter-wave radars are commonly used in automotive applications and a lot of effort is made in this way but it is also can be used in marine applications as they work robustly in bad weather conditions such as fog, dust, smog, smoke, and water vapor. So, it will be very helpful in marine applications. The proposed array antenna is a corporate Series feed 24×8 antenna array that has achieved a return loss of -26.4 dB, a gain of 23.5dBi, bandwidth of 5.2 GHz, and sidelobe levels of -21.4 dB in Hplane and -14 dB in E-plane. This antenna array's 3dB angular width equals 10.9 ° in the H-plane and 5.9 ° in the E-plane. That makes it a suitable choice for the mm-wave marine radar antenna. The final design of the antenna is acceptable compared with another previous work, making this design more considerable as will be shown. Also, an antenna array with 3 transmitters and 4 receivers is presented. Each antenna is a 24-element. the Dolph-Chebyshev technique is utilized to taper the patches. The antenna has been manufactured, and the results of the simulation are confirmed by the experimental measurements.
Classification of Darknet Traffic Using the AdaBoost Classifier Method Sari, Rizky Elinda; Stiawan, Deris; Afifah, Nurul; Idris, Mohd. Yazid; Budiarto, Rahmat
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5543

Abstract

Darknet is famous for its ability to provide anonymity which is often used for illegal activities. A security monitor report from BSSN highlights that 290.556 credential data from institution in Indonesia have been exposed on the darknet. Classification techniques are important for studying and identifying darknet traffic. This study proposes the utilization of the AdaBoost Classifier in darknet classification. The use of variable estimator values significantly impact classification results. The best performance was obtained with an estimator value of 500 with an accuracy of 99.70%. The contribution of this research lies in the development of classification models and the evaluation of AdaBoost models in the context of darknet traffic classification.
Fractal Analysis of Time Domain Dielectric Response to Reduce Complexity of Insulation Condition Diagnosis Methodology Sayais, Sachin; Banerjee, Chandra Madhab; Baral, Arijit
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.4964

Abstract

The health of cellulosic insulation, present in a power transformer, continuously degrades due to its exposure to paper moisture and high temperature. The moisture content of such insulation further accelerates the ageing phenomena. Recent developments made in the field of power transformer insulation diagnosis show that conditionbased maintenance of power transformers is more important rather than time-based maintenance. On the other hand, utilities always prefer to monitor the condition of power transformers in short measurement time. The present work proposes a fractal analysisbased condition monitoring technique. The method utilizes only a 600 s measured profile of polarization current. This paper estimates various ageing-sensitive performance parameters evaluated from fractal features for insulation diagnosis. The suggested technique can be used in a non-intrusive way to estimate performance measures such as %pm and paper conductivity. With the least amount of shutdown time, this technique quickly assesses the insulating state of power transformers. This strategy has shown to be more successful than existing approaches for monitoring insulation status.
A Translation Framework for Cross Language Information Retrieval in Tamil and Malayalam Vel S., Sakthi; R, Priya
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5172

Abstract

Cross Language Information Retrieval (CLIR) stands as an essential element in multilingual information accessibility, enabling users to obtain relevant information even when the query language and the language of the documents diverge. This paper proposes a translation framework for CLIR in Tamil and Malayalam, two Dravidian languages widely spoken in South India. Different challenges prevail in CLIR of these languages due to their linguistic differences, translation equivalence, mapping source to target languages, semantic equivalence, limited dataset and tools for ongoing research in this domain. The proposed methodology resolves some of the issues around training of a corpus utilizing a Long Short-Term Memory (LSTM) based encoder-decoder translation model. The study incorporates two bilingual parallel corpora comprising 373 sentences pairs each. Evaluation of the model's accuracy is conducted by equivalency its translations against reference translations using the Bilingual Evaluation Understudy (BLEU Score). Furthermore, BLEU scores obtained from proposed LSTM-based encoder-decoder model is compared with those from Google Translate. The findings reveal that the LSTM model attains an average BLEU score of 0.933, where, performance of Google Translate, achieved a score of 0.813. Finally, the study conducts a comparative analysis with selected CLIR models in different languages, to evaluate the overall performance of the proposed approach.
Flexible Potentiostat Readout Circuit for Electrochemical Sensors Azmi, Nur Hanisah; Nordin, Anis Nurashikin; Suhaimi, Muhammad Irsyad; Ming, Lim Lai; Ab Rahim, Rosminazuin; Samsudin, Zambri; Md Ralib @ Md Raghib, Aliza Aini
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5520

Abstract

Personalised health wearables reach their full potential when sensors are integrated with its interfacing system. Recent approaches have primarily focused on the development of readout circuits limited to the electrochemical chip and basic signal conditioning components. However, integrating a readout circuit with a microcontroller offers significant advantages such as enhanced data processing capabilities. Other than incorporating a microcontroller within the readout circuit, we also designed the entire potentiostat system on a flexible polyimide substrate, making it suitable for wearable applications. In this work, we describe the design, fabrication and testing of a flexible potentiostat readout circuit for electrochemical sensors. The core of the interface circuit is two chips, a microcontroller ATSAMD21G18A-MUT (Microchip Technology) and a programmable analog front-end integrated circuit from Texas Instruments. These chips along with a voltage regulator, resistors and capacitors were integrated onto a single, flexible, printed circuit board. To verify the functionality of the flexible readout circuit, it was connected to an electrochemical sensor and Cyclic Voltammetry (CV) was performed. The separation between peaks (ΔEp), were measured using the flexible board and compared with a commercial potentiostat (Emstat Pico). EmStat Pico has ΔEp = 0.133V, while our potentiostat produced ΔEp of 0.132V, indicating minimal variations with the same PCB layout, despite using different substrates. The standard rate constant (Ks) of electron transfer can also be obtained from CV and was measured to be 0.0037 for the rigid PCB and 0.0035 for the flexible PCB.
Trust-based Enhanced ACO Algorithm for Secure Routing in IoT Sharmin, Afsah; Motakabber, S. M. A.; Hashim, Aisha Hassan Abdalla
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5118

Abstract

The Internet of Things (IoT) is an expanding paradigm of object connectivity using a range of resource types and architectures to deliver ubiquitous and requested services. There are security issues associated with the proliferation of IoT-connected devices, allowing IoT applications to evolve. In order to provide an energy-efficient and secure routing method for sensors deployed within a dynamic IoT network, this paper presents a trust-aware enhanced ant colony optimization (ACO)-based routing algorithm, incorporating a lightweight trust evaluation model. As it is challenging to implement security in resource-constrained IoT networks, the presented model adopted bioinspired approaches, offering an improved version of ACO towards secure data transmission cost-effectively while taking into consideration residual energy and the trust score of the sensor to be optimized. The trust evaluation system has been enhanced in the development of the proposed routing algorithm and the node trust value is evaluated, sensor node misbehavior is identified, and energy conservation is maximized. The performance evaluation is demonstrated utilizing MATLAB. In comparison to the standard bioinspired algorithms and existing secure routing protocols, the proposed system reduces average energy consumption by nearly 50% regardless of the increase in the number of nodes and end-to-end delay of 40%, while finding the secure and optimal path in unison is designed to ensure trust in the IoT environment.
Bengali Word Detection from Lip Movements Using Mask RCNN and Generalized Linear Model Bhuiyan, Abul Bashar; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5088

Abstract

Speech processing with the help of lip detection and lip reading is an advancing field. For this, we need proper algorithms and techniques to detect lips and movements of lips perfectly. Lip detection and configuration are the most important parts of speech recognition. In this paper, we focus on detecting the lip segment properly. Mask R-CNN (Regional Convolutional Neural Network) performs object detection and instance segmentation per video frame to detect the lip segment. The process of mask R-CNN adds only a small overhead to Faster R-CNN and is quite simple to train, running at 5 frames per second. The Mask R-CNN involves keypoint detection which helps to extract the location of the lip landmarks pixel by pixel. Once the lip region is extracted and the landmarks are highlighted, we observe how the lip landmarks change as the object's lips move over time to each Bengali word. The keypoint changes that are observed during each millisecond are then the landmarks used to train the GLM (Generalized Linear Model). In addition, we compare the performance of GLM with Naive Bayes, Logistic Regression, and Decision Tree. The GLM has exhibited the highest 91.8% accuracy, whereas the Naive Bayes, Logistic Regression, and Decision Tree show the accuracy of 87.1%, 38.3%, and 82.2%, respectively.
DR-CNN+ Approach for Standardized Diabetic Retinopathy Severity Assessment Majid, Samiya; Bala, Indu
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.4890

Abstract

Diabetic retinopathy (DR) is a serious eye disorder that damages the retina and can lead to vision impairment and blindness, especially in individuals with diabetes. Early identification is crucial for a positive outcome, however, diabetic retinopathy can only be diagnosed with color fundus photographs, which is a technique that is difficult and time-consuming. To address this issue, this paper presents a Deep Learning-based algorithm that utilizes DR - convolutional neural network+ (DR-CNN+) to classify retinal pictures into different stages of diabetic retinopathy. The proposed algorithm is trained on a dataset of 11000 colored retinal pictures from the training set and 2200 photos from the testing set. The simulation results demonstrate that the DRCNN+-based algorithm can achieve high levels of accuracy, sensitivity, and specificity. Our proposed DR-CNN+ model not only improves diagnostic performance for diabetic retinopathy severity evaluation, but it also saves training time by 95% when compared to current models." Overall, this paper highlights the potential of using deep learning and CNNs to improve the detection and grading of diabetic retinopathy, which could have a significant impact on the prevention of blindness caused by this disease.

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