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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 :
Arjuna Subject : -
Articles 9,138 Documents
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Systematic literature review on global software development based software cost estimation models and cost drivers Mehmood Ahmed; Noraini Ibrahim; Wasif Nasir; Adeel Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1485-1494

Abstract

Global software development (GSD) is a well - established discipline of software engineering that focuses on the advantage of a global environment. Effective cost estimation is critical for the success of GSD projects. Cost estimation in a GSD environment is a challenging task. As a re sult, GSD must emphasize cost estimation. Findings show that a number of researchers over the past few decades have emphasized GSD - based cost estimation in GSD; to the best of our knowledge, however, existing cost estimation have not taken into account man y GSD - based cost drivers that must be considered when estimating costs. Motivated by all this, the purpose of this study is to review the existing GSD - based cost estimation models/techniques and cost drivers that influence the accuracy of cost estimation. To identify and compile relevant research papers, a systematic literature review was carried out. From twenty - seven selected studies, initially, 86 GSD - based cost drivers and 12 GSD - based cost estimation models/techniques were extracted. After filtration, 26 cost drivers were identified as significant and to be considered in GSD - based cost estimation. This study significantly identifies GSD - based cost drivers and existing cost estimation techniques.
Empowering health data protection: machine learning-enabled diabetes classification in a secure cloud-based IoT framework Dalia Ebrahim Hamid; Hanan M. Amer; Hossam El-Din Salah Moustafa; Hanaa Salem Marie
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1110-1121

Abstract

Smart medical devices and the internet of things (IoT) have enhanced healthcare systems by allowing remote monitoring of patient's health. Because of the unexpected increase in the number of diabetes patients, it is critical to regularly evaluate patients' health conditions before any significant illness occurs. As a result of transmitting a large volume of sensitive medical data, dealing with IoT data security issues remains a difficult challenge. This paper presents a secure remote diabetes monitoring (SR-DM) model that uses hybrid encryption, combining the advanced encryption standard and elliptic curve cryptography (AES-ECC), to ensure the patients' sensitive data is protected in IoT platforms based on the cloud. The health statuses of patients are determined in this model by predicting critical situations using machine learning (ML) algorithms for analyzing medical data sensed by smart health IoT devices. The results reveal that the AES-ECC approach has a significant influence on cloud-based IoT systems and the random forest (RF) classification method outperforms with a high accuracy of 91.4%. As a consequence of the outcomes obtained, the proposed model effectively establishes a secure and efficient system for remote health monitoring.
A scoping review of artificial intelligence-based robot therapy for children with disabilities Rusnani Yahya; Rozita Jailani; Fazah Akhtar Hanapiah; Nur Khalidah Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1855-1865

Abstract

The integration of artificial intelligence (AI)-based robot therapy (AIBRT) has become prominent in addressing the needs of children with disabilities, including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), learning disabilities, and speech delays. However, questions arise regarding the effectiveness of different AI techniques in enhancing therapy for children with specific needs. This review explores current literature on AIBRT for children with disabilities, aiming to understand the efficacy and potential of various AI techniques in improving their therapy. This paper presents a comprehensive search of research articles published from 2019 to September 2023. 39 articles focusing on AI-based robot platforms, the employed treatment or therapy methods, assessment procedures during therapy, and the variables or parameters used to measure intervention effectiveness have been discussed in detail. These AI-based robot platforms have been utilized to engage individuals diagnosed with ASD, offering therapeutic interventions and assessments. In conclusion, the integration of AI and robotics in therapy shows promise for enhancing the development and quality of life for children with disabilities. The findings of this review have implications for therapists, practitioners, and researchers interested in incorporating AI applications into therapy practices. This integration can lead to improved therapy outcomes, optimized children’s development, and enhanced quality of life.
Performance evaluation of multiclass classification models for ToN-IoT network device datasets Soni, Soni; Remli, Muhammad Akmal; Daud, Kauthar Mohd; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp485-493

Abstract

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.
A machine learning approach to cardiovascular disease prediction with advanced feature selection Abdikadir Hussein Elmi; Abdijalil Abdullahi; Mohamed Ali Barre
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1030-1041

Abstract

Cardiovascular diseases (CVDs) pose a significant global public health challenge, necessitating precise risk assessment for proactive treatment and optimal utilization of healthcare resources. This study employs machine learning algorithms and sophisticated feature selection techniques to enhance the accuracy and comprehensibility of CVD prediction models. While traditional risk assessment tools are valuable, they frequently fail to consider the myriad intricate factors that contribute to the heightened risk of CVD. Our methodology employs machine learning algorithms to analyze diverse healthcare data sources and produce advanced predictive models. The salient feature of this research lies in the meticulous application of advanced feature selection techniques, enabling the identification of pivotal factors within heterogeneous datasets. Optimizing feature selection enhances the interpretability of the model, reduces dimensionality, and improves predictive accuracy. The area under the ROC curve (AUC-ROC) score of the wrapper method model significantly decreased from 95.1% to 75.1% after tuning, based on empirical tests that supported the suggested method. This showcases its capacity as a tool for assessing premature CVD susceptibility and developing tailored healthcare strategies. The study highlights the significance of integrating machine learning with feature selection due to the widespread influence of cardiovascular diseases. Integrating this system has the potential to enhance patient care and optimize the utilization of healthcare resources.
Ensemble learning based health care claim fraud detection in an imbalance data environment Shweta S. Kaddi; Malini M. Patil
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1686-1694

Abstract

Healthcare fraud has become a common encounter in the healthcare finance industry. The financial security of healthcare payers and providers is seriously impacted by healthcare fraud. When incorrect or exaggerated medical services are invoiced for reimbursement, fraudulent healthcare claims result. The effective operation of the healthcare system depends on the detection of such fraudulent actions. This paper develops a healthcare claim fraud detection method based on ensemble learning. Stack ensemble learning algorithm performance is compared to that of methods such as multi-layer perceptron (MLP) classifier, support vector classifier (SVC), logistic regression (LR), and decision tree (DT) algorithm. Because of the healthcare data imbalance, the normal transaction is significantly higher than the fraudulent transaction. The machine learning (ML) algorithm performs poorly because imbalanced data causes it to be biased toward the majority class. As a result, the data is unsampled using the synthetic minority oversampling technique (SMOTE) technique to provide balanced data. The experimental results show that for the identification of healthcare claim fraud, the ensemble learning strategy greatly outperforms single learning algorithms. The stack ensemble learning outperforms all the area under the curve for the receiver-operating characteristic (AUC ROC) curves from various algorithms, and the AUC-ROC curve is determined to be producing results that are adequate for all approaches.
An efficient test suit reduction methodology for regression testing Shailendra Gupta; Jitendra Choudhary
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1336-1343

Abstract

This paper's goal is to provide a more effective algorithm for reducing the amount of test cases in a test suit during the regression testing phase. This algorithm divide the entire test suit into equivalence classes in first step and then apply boundary value coverage to select test case out of repeated test cases which has same importance in test suit. This algorithm is based on the concept that before selecting best test cases out of repeated test case in test suit to prepare reduced test suit we can divide all test cases in number of equivalence classes so number of test case under consideration reduced by great extend. This paper proposed a method of experimentation involving test cases from different software application areas; minimization algorithms and the maths and algorithms of minimization algorithms in details. Test case techniques are equivalence portioning and boundary value analysis. Along with this concept, I also discuss a case study to verify and check new algorithm for its efficiency, for that I apply my algorithm on one of the program or group of program. This complete proposed methodology shall be applied to different software applications belonging to soft computing, Engineering software, Financial Software, Cloud Applications, Business Applications, AI and Machine Learning, Data Analytics are being identified. This selection has been made keeping in mind the trends, industrial importance, economic values and research challenges. Minimization in test cases would lead to lesser testing effort and desirable test completion.
Sentiment analysis and classification of Ghanaian football tweets from the 2022 FIFA World Cup Eshun Michael; Gyening Mensah Rose-Mary Owusuaa; Takyi Kate; Appiahene Peter; Peasah Ofosuhene Kwame; Banning Amoako Linda
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp497-507

Abstract

Football as an attractive sport generates huge volumes of tweets concerning fans' opinions, feelings, and judgments during prime events. Such data can be leveraged in sentiment analysis, an algorithmic approach to analyzing text in tweets by extracting emotional tones. This paper presents a novel benchmark dataset of 132,115 tweets collected during the 2022 world cup on ???? (formerly Twitter) for football-related sentiment classification. We also performed sentiment analysis on the dataset using lexicon-based tools, traditional machine learning algorithms, and pre-trained models, robustly optimized bidirectional encoder representations from transformers (BERT)- pretraining approach RoBERTa and distilled version of BERT (DistilBERT) to understand the emotions and reactions of football fans during different phases of the football matches. Results from the study indicate that most tweets had neutral sentiments in both context-aware and context-free analysis. We also describe our novel GhaFootBERT, a sentiment classification model based on transfer learning on BERT, which provides an effective approach to sentiment classification of football-related tweets. Our model performs robustly, outperforming the traditional models with 92% accuracy.
Big data analysis and its impact on the marketing industry: a systematic review Patricio-Peralta, Cesar; Mondragon, Jesús Zamora; Terrones, Luis Segura; Villacorta, Jimmy Ramirez
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.pp1032-1040

Abstract

This systematic review focused on understanding the impact of big data on marketing productivity, following the guidelines of systematic literature reviews and using the PICOC (problem/population, intervention, comparison, results, context) method. 50 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science and business, and published between 2020 and 2023. These articles, selected for their relevance and contribution to the study objectives, showed that the big data offers notable benefits in the marketing industry. The ability to customize marketing strategies to individual customer needs, improved optimization, and a better understanding of customer behaviors and preferences were key aspects. These findings highlight how big data can boost productivity in marketing, strengthening customer relationships and increasing loyalty by improving understanding and adaptation to the specific demands and preferences of each customer. This deeper, more personalized approach to consumers represents a significant shift in the effectiveness and efficiency of marketing strategies in the current era.
Integrating random forest model and internet of things-based sensor for smart poultry farm monitoring system Imam Fahrurrozi; Wahyono Wahyono; Yunita Sari; Anny Kartika Sari; Ilona Usuman; Bambang Ariyadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp1283-1292

Abstract

The global poultry industry has encountered growing concerns related to foodborne illnesses, misuse of antibiotics, and environmental impacts. To tackle these issues, this study aims to develop an intelligent poultry farm with real-time environmental monitoring and predictive models. The primary objective is to combine a machine learning-based prediction model with internet of things (IoT) devices to gather and analyze environmental data, such as temperature, humidity, and ammonia levels, to forecast the conditions within poultry houses. These sensor data and additional information, such as feed consumption, water consumption, poultry weight, capacity, and poultry house dimensions will serve as inputs for supervised machine learning models. Among these models, the proposed random forest (RF) model, when augmented with timestamp features, achieves the highest accuracy rate of 96.665%, surpassing other models such as logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), support vector machine (SVM), and multi-layer perceptron (MLP) in identifying poultry house conditions. Additionally, this study demonstrates how the trained model can be effectively applied in a web-based monitoring system, delivering real-time data to farmers for well-informed decision-making and ultimately enhancing productivity in smart poultry farming.

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