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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 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. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 75 Documents
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Search results for , issue "Vol 13, No 4: August 2024" : 75 Documents clear
Applying convolutional neural network and Nadam optimization in flower classification Aini, Qurrotul; Zulfiandri, Zulfiandri; Firmansyah, Rezky; Arif, Yunifa Miftachul
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6203

Abstract

Flowers have a variety of shapes, colors and structures, the images of which need to be classified using guided learning techniques. Several studies classify flowers using machine learning, but their accuracy performance is not good. The thing is, the flowers come in a variety of colors that can sometimes look similar to the background. Therefore, this study aims to classify flowers using a convolutional neural network (CNN) and measure its performance. The method used is mixed methods by collecting existing data from previous studies and connecting it with the realities in the field. The Kozłowski and Steinbrener models were used, while the image data was obtained from the Oxford17 and Oxford102 dataset with 17 and 102 flower types, respectively. The results show 60% and 84% accuracy of CNN using the scratch and transfer learning approach for the Oxford17 dataset. The Oxford102 dataset shows 42% and 64%, respectively, with CNN from baseline and transfer learning.
Predicting the effects of microcredit on women’s empowerment in rural Bangladesh: using machine learning algorithms Polin, Johora Akter; Sarker, Md. Fouad Hossain; Dolon, Mst Dilruba Khanom; Hasan, Nahid; Rahman, Md. Mahafuzur; Vasha, Zannatun Nayem
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7169

Abstract

This study aimed to predict the impact of microcredit on women’s empowerment in Bangladesh using machine learning (ML) algorithms. In rural Bangladesh, where microcredit programs are not significantly employed, data for the study was gathered through a survey. The study gathered data on a range of socioeconomic, demographic, and women’s empowerment indicators. The Naive Bayes (NB), sequential minimal optimization (SMO), k-nearest neighbor (k-NN), decision tree (DT), and random forest (RF) ML techniques were used in the investigation. In terms of the prediction of women’s empowerment, the findings indicated that all five algorithms performed well, with the DT having the highest level of accuracy (83.72%). The results of this study have significant consequences for Bangladesh’s microcredit programs and those in nations that are developing. Microcredit programs can focus their efforts on women who, based on their socioeconomic and demographic features, are most likely to benefit from the program by employing ML algorithms. This may result in more successful microcredit projects that support the empowerment of women and general socioeconomic growth.
Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot Ha, Vo Thanh; Thi Thuong, Than; Ngoc Truc, Le
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7847

Abstract

This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.
Recent advancements in postpartum depression prediction through machine learning approaches: a systematic review Fazraningtyas, Winda Ayu; Rahmatullah, Bahbibi; Dwi Salmarini, Desilestia; Arrieya Ariffin, Shamsul; Ismail, Azniah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7185

Abstract

Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, irrespective of demographic factors. Detecting and managing PPD at an early stage is crucial for effective intervention. In the context of mental health, intelligent predictive models based on machine learning (ML) have emerged as valuable tools. However, there remains a relative scarcity of research specifically targeting postpartum mental health due to several prominent factors that collectively impede the widespread adoption and practical implementation of ML in the field of PPD. This paper provides an updated overview of ML approaches for PPD prediction. A systematic search across IEEE Xplore, PubMed, Science Direct, and Scopus yielded 1,074 relevant articles. The performance of ML algorithms varies depending on the dataset and the problem being addressed. Notably, the findings reveal that the random forest (RF) algorithm consistently demonstrates the highest predictive accuracy, followed by support vector machine (SVM), logistic regression (LR), XGBoost, and AdaBoost. The development of advanced data techniques in PPD has encouraged interdisciplinary collaboration between researchers in psychiatry and computer science that holds great potential for refining the accuracy and reliability of PPD predictive models, ultimately resulting in improved outcomes for mothers and their families through early detection, intervention, and support.
Internet of things-based rice field irrigation evaporation monitoring system Aisyah, Putri Yeni; Widya Pratama, I Putu Eka; Rahmadhana, Furqan; Al Ghifari, Muhammad Ghozi
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.5803

Abstract

The urgency for efficient irrigation in Indonesia’s agriculture sector, particularly in paddy fields, is evident. However, existing methods for monitoring water levels are antiquated, often requiring manual measurements with a ruler. This research introduces a comprehensive “monitoring system for light intensity and water temperature as an analysis of evaporation for rice irrigation based on the internet of things”. The system integrates various sensors an anemometer for wind speed, an ultrasonic sensor for water level, a DS18B20 waterproof sensor for water temperature, and a GY-8511 sensor for sunlight intensity. All data are collected by an Arduino Mega controller, connected to an ESP32 for transmitting the readings to the Blynk app and an I2C 20×4 liquid crystal display (LCD) screen. The control mechanism employs a closed-loop system with a direct current (DC) motor actuator to operate the water gate, which can also be manually controlled via a cellphone. The system effectively meets daily evapotranspiration requirements of 1.44 mm, with optimal conditions yielding water levels of 3 cm, water temperatures of 38.53 °C, sunlight intensity of 4.59 mW/cm², and wind speed of 0.21 m/s.
Detection and mitigation of DDoS attacks in SDN based intrusion detection system Chouikik, Meryem; Ouaissa, Mariyam; Ouaissa, Mariya; Boulouard, Zakaria; Kissi, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7570

Abstract

Software defined networks (SDN) have completely revolutionized the management and operation of networks. This novel technology entails a distinctive approach to management. Amidst the advancements, a notable security concern arises in the form of distributed denial of service (DDoS) attacks. To counteract this attack, the deployment of intrusion detection systems (IDS) assumes paramount importance. IDS plays a critical role in monitoring network traffic, promptly detecting irregularities that may signify a potential denial of service (DoS) assault. This study delves into a comprehensive exploration of a DDoS attack on an SDN network using the OpenDaylight controller and the Mininet emulator. Furthermore, the assessment extends to evaluating the DDoS attack's repercussions and the effectiveness of IDS in mitigating such risks. Various performance metrics, including throughput according to delay time, are monitored to gauge network performance under duress. The difference in throughput curves when comparing scenarios with and without IDS highlights the significant impact of intrusion detection. When the IDS was absent, there was a noticeable increase in oscillations, indicating greater network susceptibility. On the other hand, the presence of an IDS created a more regulated environment, reducing variances and promoting a more stable network.
MCDM-AHP and ELECTRE collaboration apps for the best vendor selection technique Akmaludin, Akmaludin; Samudi, Samudi; Baidawi, Taufik; Dalis, Sopiyan; Suriyanto, Adhi Dharma; Widianto, Kudiantoro
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7545

Abstract

Vendor selection techniques are very important to maintain supply chain services, optimal service creates strong consistency in maintaining the continuity of supply chain business processes. The aim of this research is to provide an objective and consistent understanding of the best techniques in vendor selection which are implemented openly through the collaboration of multi-criteria decision making-analytic hierarchy process (MCDM-AHP) and ELECTRE. Empirical studies show how this approach is able to provide optimal decision-making support for the vendor selection process. Eight criteria are required which have contradictory meanings in their apps. These criteria include quality of goods (QG), payment methods (PMs), payment terms (PTs), minimum transactions (MTs), discounts (DS), delivery times (DTs), inventory (IN), and service (SV). The comparison importance value of the criteria is used as a measure of weighting the criteria through two testing approaches, namely mathematical algebra matrices and expert choice apps, through accurately assessing the optimal eigenvector from the two test approaches. Decision making support was carried out by comparison using 342 preference matrices which were developed into concordance and discordance matrices, the elimination process with threshold matrices found that the ranking results of four vendors were ranked first as worthy of being a selection priority and fifteen other vendors were ranked below.
Data access control for named data of health things EL-Bakkouchi, Asmaa; EL Ghazi, Mohammed; Bouayad, Anas; Fattah, Mohammed; EL Bekkali, Moulhime
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6219

Abstract

The internet of health things (IoHT) represents an innovative network concept that significantly improving healthcare. However, security and privacy are the main concerns of IoHT because the transmitted health data is often sensitive data about patients’ health status, which needs to be secured and protected from unauthorized users and any leakage. Named data networking (NDN) is considered the most promising architecture for the future internet that perfectly fits with the requirements of IoHT systems, especially regarding security and privacy. In this paper, we exploit the fundamental features of NDN to design a robust system for IoHT to ensure secure communication and access to health data. This system presents a content access control model, which prevents attackers and unauthorized users from accessing health data, allows only authorized users to access these data, and prevents users from accessing “corrupted” or “fake” content. The simulation results show that the proposed mechanism slightly delays the secure retrieval of health data. However, this delay is tolerable since the mechanism protects the health data from unauthorized persons and those who try to inject untrusted data into the network.
Stability analysis of power system under n-1 contingency condition Baleboina, Guru Mohan; Rudramoorthy, Mageshvaran
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6133

Abstract

Several voltage stability indices (VSIs) have been developed to assess the potential for voltage collapse. However, certain indexes are computationally costly. Meanwhile, some have been noted to underperform across various conditions. This work proposes a novel line index called the super voltage stability index (SVSI) to calculate the system's voltage stability margin (VSM). The suggested approach is based on the transmission system's two bus systems. The reactive power loss and N-1 contingency conditions to voltage sensitivity is a unique calculation approach used in this study to identify voltage instability. Day to day, the demand for electric power is being increased due to incessant increments in technology and population growth. Therefore, the power system networks are under pressure. The operational conditions of transmission system networks are affected at this point, which may result in voltage collapse. Regular monitoring of power supply is essential to avert voltage collapse. The effectiveness of the suggested index has been assessed using the IEEE 5 and 30-bus systems across diverse operating scenarios, including variations in active and reactive power loading as well as single line losses. The findings indicate that SVSI provides a more reliable indication of the proximity to voltage collapse when compared to conventional line VSIs.
Optimized k-nearest neighbours classifier based prediction of epileptic seizures Jagath Prasad, Himayavardhini; Marjorie S., Roji
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6598

Abstract

Epileptic seizure is an unstable condition of the brain that cause severe mental disorder and can be fatal if not properly diagnosed at an early stage. Electroencephalogram (EEG) plays a major role in early diagnosis of epileptic seizures. The volume of medical databases is enormous. Classification may become less accurate if the dataset contains redundant and irrelevant attributes. To reduce the mortality rate due to epilepsy, a decision support system that can assist medical professionals in taking immediate precautionary measures prior to reaching the critical condition is required. In this work, k-nearest neighbours (KNN) classifier algorithm is optimised using genetic algorithm for effective classification and faster prediction to meet this requirement. Genetic algorithms search for optimal solutions in complex and large environments. Results are compared with other machine learning models such as support vector machine (SVM), KNN, decision tree classifier, and random forest. With optimization using genetic algorithm KNN was able to achieve an enhancement in accuracy at lower training and testing times. It was observed that the accuracy offered by optimized KNN was 92%. Random forest classifiers showed minimum complexity and KNN algorithm provided faster performance with better accuracy.

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