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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 2,901 Documents
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Enhancing costumer churn prediction with stacking ensemble and stratified k-fold Rofik, Rofik; Unjung, Jumanto; Prasetiyo, Budi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8112

Abstract

In the era of rapid technological advancement, the telecommunications industry undergoes significant changes. Factors such as the speed of technological change, high customer expectations, and changing preferences are the main obstacles that affect the dynamics of telecommunications companies. One major issue faced is the high customer churn rate, adversely impacting company revenue and profitability. Previous studies indicate that customer churn prediction remains complex in the telecommunications industry, with opportunities to optimize algorithm selection and prediction model construction methods. This research aims to improve the accuracy of customer churn prediction by employing a complex model that utilizes stacking ensemble learning techniques. The proposed model combines 6 base algorithms: extreme gradient boosting (XGBoost), random forest, light gradient boosting machine (LightGBM), support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN), with XGBoost as the meta-learner model. The research process involves preprocessing, class data balance with synthetic minority oversampling technique (SMOTE), training using stratified k-fold, and model evaluation. The model is tested using the Telecom Churn dataset. The evaluation results show that the constructed stacking model achieves 98% accuracy, 98.74% recall, 98.03% precision, and 98.38% F1 score. This study demonstrates that optimizing the stacking ensemble model with SMOTE and stratified k-fold enhances customer churn prediction accuracy.
Hybrid deep learning: a comparative study on ai algorithms in natural language processing for text classification Mahmudul Hasan, Md.; Kumar Das, Rajesh; Hassan, Mocksidul; Razia, Sultana; Ferdous Ani, Jannatul; Akter Khushbu, Sharun; Islam, Mirajul
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7617

Abstract

The objective of this research project is to assess the effectiveness of various machine learning algorithms, including deep learning and combination approaches, in performing tasks such as categorizing products into specific categories using data from an e-commerce platform named "OTHOBA." In this study, a dataset consisting of 19,087 data samples is used to evaluate the effectiveness of seven supervised machine learning models. Among these models are three based on deep learning: long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and 1D convolutional (Conv1D), as well as a multi-layer model that combines Conv1D and LSTM approaches. The task at hand is the classification of product categories. The LSTM-based model demonstrates the highest accuracy rate of 96.23% among the deep learning models, while the logistic regression (LR) models achieve the highest accuracy scores of 97.00% for product category classification. Overall, the proposed models and techniques show significant progress in natural language processing (NLP) research for text classification, specifically in English, and have practical applications for e-commerce sites.
An efficient snow flake schema with hash map using SHA-256 based on data masking for securing employee data Bharath, Tumkur Shankaregowda; Channakrishnaraju, Channakrishnaraju
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8767

Abstract

In various organizations and enterprises, data masking is used to store sensitive data efficiently and securely. The data encryption and secret-sharing-based data deploying strategies secure privacy of subtle attributes but not secrecy. To solve this problem, the novel snowflake schema with the hash map using secure hash algorithm-256 (SHA-256) is proposed for the data masking. SHA-256 approach combines data masking by secret sharing for relational databases to secure both privacy as well as the confidentiality of secret employee data. The data masking approach supports preserving and protecting the privacy of sensitive and complex employee data. The data masking is developed on selected database fields to cover the sensitive data in the set of query outcomes. The proposed method embeds one or multiple secret attributes about multiple cover attributes in a similar relational database. The proposed method is validated through different performance metrics such as peak signal-to-noise ratio (PSNR) and error rate (ER) and it achieves the values of 50.084dB and 0.0281 when compared to the existing methods like Huffman-based lossless image coding and quad-tree partitioning and integer wavelet transform (IWT).
Doppler radar-based pothole sensing using spectral features in k-nearest neighbors Aiman Dani Asmadi, Muhammad; Zainuddin, Suraya; Mohd Nasir, Haslinah; Syafiza Md Isa, Ida; Emileen Abd Rashid, Nur; Pasya, Idnin
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8398

Abstract

Potholes, resulting from wear, weather, and traffic, pose a substantial road safety concern, driving up maintenance costs and government liabilities. Numerous studies have explored pothole detection systems, however, there is a limited focus on radar-based approaches. This study investigates the use of Doppler radar mounted on moving vehicles to collect asphalt road surface data, with the aim to leverage this unique perspective point. Spectral features from power spectral density (PSD) are extracted and explored by incorporating Doppler signal PSD features into a k-nearest neighbors (KNN) within a machine learning framework for road condition classification. Six KNN algorithms are applied, and results indicate that potholes exhibit distinct spectral differences characterized by higher variability, with fine KNN performing the best, achieving an accuracy rate of 95.38% on the test dataset. In summary, this research underscores the effectiveness of Doppler radar-based pothole sensing and emphasizes the significance of algorithm and feature selection for achieving accurate results, proposing the viability of radar systems and machine learning.
Hybrid approach for tweets similarity classification founded on case based reasoning and machine learning techniques Bensassi, Ismail; Kouissi, Mohamed; Ndama, Oussama; En-Naimi, El Mokhtar; Zouhair, Abdelhamid
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8452

Abstract

Twitter sentiment analysis becomes a popular research subject in the last decade. It aims to extract sentiments of users through their public opinion about a given topic. This article proposes a hybrid approach for Twitter sentiment analysis founded on dynamic case based reasoning (DCBR), multinomial logistic regression machine learning algorithm and multi-agent system. Our approach proposes a method to find similar tweets based on content similarity measure using the scientific measurement of keyword weight term frequency-inverse document frequency (TF-IDF). This approach includes gathering and pre-processing tweets, getting score and polarity of tweets, the use of multinomial logistic regression machine learning algorithm to classify our tweets into various classes, using the feature extraction method to extract useful features and then the K-nearest neighbors (KNN) algorithm to make it easier to find similar tweets to our tweet target case. This approach is adaptive and generic and able to track users' tweet to predict their behavior and sentiments in critical situations and delivering personalized content. The current study focuses on Covid-19 tweets, and a public Twitter dataset is used for this purpose.
A scoring approach for detecting fake reviews using MRCS similarity metric enhanced by personalized k-means Ennaouri, Mohammed; Zellou, Ahmed
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8288

Abstract

Online commerce has grown in the digital age, and as a result, consumers now depend more than ever on other consumer feedback to make informed purchasing decisions. However, as the importance of reviews has increased, so has the prevalence of fake ones, which now infiltrate platforms and manipulate users' perceptions. This presents a significant challenge to preserving confidence and integrity in online marketplaces. This study addresses the difficulty of identifying fake reviews by introducing a distinctive methodology that incorporates advanced natural language processing (NLP) tools. By including a new metric, mean review cosine similarity (MRCS), which enhances textual similarity assessment for more accurate detection, we improve the identification process. Additionally, an exaggeration detection technique is included, enhancing the model's capacity to identify deceptive variations in review content. Furthermore, an adaptive clustering method differs from traditional k-means classification through modifying clusters to adjust to the constant evolution of misleading linguistic patterns. Empirical validation on the Yelp labeled dataset demonstrates the approach's accuracy (90%), with high precision (89%), recall (95%), and F1 score (92%), indicating its effectiveness and highlighting areas for further refinement.
Object detection in video surveillance using MobileNetV2 on resource-constrained low-power edge devices Lokhande, Harshad; R. Ganorkar, Sanjay
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8131

Abstract

Edge-based video surveillance systems encounter significant obstacles in object detection due to the limited computational power and energy efficiency of edge devices, which are required to deliver real-time processing capabilities. Traditional object detection models are excessively resource-hungry for these environments, making optimization techniques absolutely essential. This study robustly explores the implementation of quantized transfer learning utilizing SSD MobileNet V2 with 8-bit quantization to significantly elevate the performance of object detection on resource-constrained edge devices. Experimental results decisively indicate that the Raspberry Pi 5 and Nvidia Jetson Orin Nano surpass other devices, achieving total latencies of 5 ms and 85 ms, respectively, affirming their exceptional suitability for real-time applications. The quantized int8 model secures an impressive accuracy of 80.65% while dramatically lowering memory consumption and latency when compared to the unoptimized int32 model. Furthermore, the model demonstrates outstanding performance on a masked-unmasked dataset with an F1 score of 0.92 for masked detection. These findings underscore the transformative potential of quantization in enhancing both inference speed and resource efficiency in edge-based surveillance systems. Future research will boldly investigate advanced hybrid quantization strategies and architectural enhancements to achieve an optimal balance of efficiency and accuracy, alongside scalability across a broader spectrum of edge devices and datasets.
Enhanced student attendance and communication in educational management systems El Mustapha, Louragli; Gmih, Yassine; Soussi, Sohaib; Abdelmajid, Farchi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7915

Abstract

The internet of things (IoT) and radio frequency identification (RFID) technology were explored to devise a beneficial approach for managing student attendance. The research developed a system that uses RFID tags embedded in student bracelets to gather presence data via strategically placed sensors. The system leverages real-time databases and Google technologies to enhance the student experience through an online platform, while also utilizing RFID for authentication. Focusing on improving user experience (UX) through effective design, the proposed system offers a pleasurable and cost-effective solution. Developed using popular web technologies such as Firebase, React.js, and Tailwind, along with Arduino chips and sensors, the system provides a practical solution for managing student attendance, academic performance, and administrative communication. The research highlights the potential of RFID technology in improving student management and academic performance. By decreasing the effort needed by traditional systems and proving cost-effective in the long term, it could act as a potential choice for implementation in educational institutions worldwide.
A new deep learning approach for predicting high-frequency short-term cryptocurrency price Akouaouch, Issam; Bouayad, Anas
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7377

Abstract

Cryptocurrencies are known for their volatility and instability, making them an attractive but risky investment for traders, analysts, and researchers. As the allure of Bitcoin (BTC) and other cryptocurrencies continues to grow, so does the interest in predicting their prices. To forecast the market rate and sustainability of cryptocurrencies, this study uses machine learning-based time series analysis. The study employs forecast periods ranging from 1 to 10-minutes to categorize the consistency of the market. High-frequency pricing of cryptocurrencies is anticipated with a timestep of up to 10 seconds using various deep learning (DL) models. A hybrid model combining long short-term memory (LSTM) and gated recurrent unit (GRU) is created and compared with standard LSTM and GRU models. Mean squared error (MSE) is the benchmark for estimating the models' performance. The study achieves better results than benchmark models, with MSE values for BTC, Cardano (ADA), and Cosmos (ATOM) in a 5-minute window size being 0.000192, 0.000414, and 0.000451, respectively, and for a 10-minute window size being 0.000212, 0.000197, and 0.000746. Compared to existing models, the suggested model offers a high price predicting accuracy. This study on crypto price prediction using machine learning applications is a preliminary investigation into the topic.
Development a decision support system for selection healthcare chatbot Phooriyaphan, Sirirak; Rachsiriwatcharabul, Natworapol
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8484

Abstract

It’s an increasing number of healthcare in many countries. Healthcare chatbot can save money, time and meet patient satisfaction. The healthcare would like to select the best or optimal healthcare chatbot but in the real situations, some healthcare may select the healthcare chatbot by own opinions in the organization with several criteria. The purpose of this research is to design and develop a decision support system (DSS) to select healthcare chatbot under the criteria of: i) functionalities; ii) multilingual ability; iii) usability; and iv) security and privacy. According to this research, it can help healthcare to make a reliable decision. The DSS allows users to select the most suitable alternatives of chatbot. The DSS is analyzed by using analytic hierarchy process (AHP). The result show that the DSS was designed to help in complex decision making and show the making decision of decision maker in the reliable and accurate decision. The result found that it is an appropriate technique for using in the DSS to select the suitable healthcare chatbot in accordance with overall criteria effectively including the sensitivity analysis.

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