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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 :
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Articles 9,138 Documents
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Seeking best performance: a comparative evaluation of machine learning models in the prediction of hepatitis C Cabanillas-Carbonell, Michael; Zapata-Paulini, Joselyn
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp374-386

Abstract

Hepatitis C is a disease that affects millions of people worldwide. It is spread through contact with contaminated blood through injections, transfusions, or other means. It is estimated that with early detection patients have a higher rate of recovery. The objective of this study is to perform a comparative evaluation of different models focused on the prediction of hepatitis C, to determine which of the models offers better performance in accuracy, precision, and sensitivity. The models used were logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), and gradient boosting (GB), aimed at hepatitis C prediction. The training of the models was carried out using a dataset composed of 615 records, which incorporate 14 attributes. The structure of the article is divided into six sections, including introduction, review of related articles, methodology, results, discussion, and conclusions. The performance of the models was evaluated through metrics such as accuracy, sensitivity, F1 count, and, mainly, precision. The results obtained place the DT model as the most efficient predictor, reaching a precision, accuracy, sensitivity, and F1-score of 95%.
Unraveling the relationships among essential oil compounds in Aquilaria species using GC-MS and GC-FID techniques Syafiqah Noramli, Nur Athirah; Ahmad Sabri, Noor Aida Syakira; Roslan, Muhammad Ikhsan; Ismail, Nurlaila; Yusoff, Zakiah Mohd; Taib, Mohd Nasir
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp167-177

Abstract

Agarwood, a prized non-timber resource from the Aquilaria genus, is highly valued for its aromatic and medicinal properties, playing a significant role in the healthcare, fragrance, and pharmaceutical industries. This research analyzes essential oils from four Aquilaria species-A. beccariana, A. malaccensis, A. crassna, and A. subintegra-using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID). The primary objective is to optimize classification efficiency by reducing computational time and reducing multicollinearity through feature selection. Pearson correlation analysis revealed strong relationships among six chemical compounds-β-selinene (A), dihydro-β-agarofuran (B), δguaiene (C), 10-epi-γ-eudesmol (D), γ-eudesmol (E), and pentadecanoic acid (F). Through feature selection, the three most significant compoundsdihydro-β-agarofuran (B), γ-eudesmol (D), and 10-epi-γ-eudesmol (E)-were identified, achieving a remarkable 90.02% reduction in computational time (from 0.0403 to 0.0040 seconds). These findings highlight the effectiveness of structured feature selection in refining essential oil profiling and enhancing species classification accuracy. Future research directions include exploring machine learning-based feature selection techniques to further streamline feature reduction processes and expand the scope of essential oil authentication. This study contributes to advancing the scientific understanding and practical utilization of agarwood essential oils, paving the way for more efficient and reliable analytical frameworks.
Devising the m-learning framework for enhancing students' confidence through expert consensus Sun, Teik Heng; Lakulu, Muhammad Modi; Mohd Noor, Noor Anida Zaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1035-1052

Abstract

Past research has shown the relationship between self-regulated learning (SRL) and academic success. Self-regulated learners will monitor their learning, reflect on what they have learnt, adjust their learning strategies accordingly, and repeat this entire process throughout their learning. The ability to perform SRL will require the individual to have the belief and confidence in his/her capacity to succeed and accomplish the tasks. Therefore, this study aims to devise a mobile learning (m-learning) framework for enhancing the students’ confidence. To achieve this, the Fuzzy Delphi method was used to validate the proposed framework where the survey questionnaire was distributed to 21 experts who are the experts in their respective fields for their consensus to be obtained. Consensus showed that “assessment data” can indicate the students’ confidence when they attempt the assessment. Experts opined that “goal expectation,” and “viewed lessons, chapters, or syllabus” exert the most influence on the students’ confidence when they attempt their assessment. There was strong consensus from experts that “data security” is the most important element in the system infrastructure, and the “text mining technique” element can be used to evaluate the students’ confidence.
Analyzing and clustering students admission data in Yala Rajabhat University Thailand Pamutha, Thanakorn; Promthong, Wanchana; Pahlawan, Sofwan
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1310-1325

Abstract

This research explores the use of clustering techniques to analyze student admission data at Yala Rajabhat University, Thailand, aiming to enhance recruitment strategies and understand student profiles. Employing K-means, Hierarchical Clustering, and Density-based spatial clustering of applications with noise (DBSCAN), the study groups admission data based on factors like educational institution, geographic location, and program chosen. The methodology incorporates normalization and principal component analysis (PCA) to ensure data quality, while the Elbow Method determines the optimal number of clusters for effective data segmentation. The davies-bouldin index (DBI) evaluates the clustering configurations, ensuring that clusters are well-separated and cohesive. The results reveal distinct student profiles that can inform targeted marketing and improve recruitment strategies. This study not only provides strategic insights into student recruitment but also contributes to the literature on the use of data science in educational settings, highlighting the transformative impact of advanced analytics on institutional effectiveness. The research emphasizes the importance of data-driven approaches in adapting to the changing dynamics of student admissions and the competitive landscape of higher education.
CriteriaChecker: a knowledge graph approach to enhance integrity and ethics in academic publication Sharma, Garima; Tripathi, Vikas; Singh, Vijay
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp973-986

Abstract

Academic writing is an integral part of scientific communities. This is a formal style of writing used by researchers and scholars to communicate critical analysis and evidence based arguments. This work showcased a graph-based approach for scraping, extracting, representing and evaluating the available academic writing forgery detection criteria and further enhancing the model by proposing a set of new age criteria. The proposed work is based on knowledge graphs and graph analytics capable of selecting subset of 16 criteria from the available superset of a cent of criterias provided by Bealls, Cabells, Shreshtha, and Think.Check.Submit, Scopus, and other relevant authors. The process for detecting the influencial parameters consists of 04 phases: dataset preparation, knowledge graph representation and making inferences through graph analytics and evaluation of results. The experimental results are then compared to the retraction database that consisting of information about retracted articles. The work enables the construction of an experiential knowledge graph that effectively identifies influential criteria, enhancing this list by incorporating new age criteria into current influential set and concluding in result by successfully detecting the academic predatory behavior.
An optimized architecture for real-time fraud detection in big data systems, ecosystems, and environments Abutaleb, Gaber Elsayed; Alhabshy, Abdallah A.; Elemary, Berihan R.; Ali, Ebeid; Eldahshan, Kamal Abdelraouf
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1221-1235

Abstract

The exponential growth of data in recent years has created significant challenges in fraud detection. Fraudulent activities are increasingly widespread across sectors, such as banking, web networks, health insurance, and telecommunications. This trend highlights a growing need for big data technologies such as Hadoop, Spark, Storm, and HBase to enable real-time detection and analysis of data fraud. This study aims to enhance understanding of the fraud classifications and their spread in various sectors. Fraud detection involves analyzing data and developing machine learning (ML) models or traditional rule-based systems to identify abnormal activities as they occur. The analysis in this paper examines both the advantages and limitations of these solutions, particularly regarding scalability and performance. This paper evaluates the methods and big data tools used in fraud detection and prevention through a comprehensive literature review, emphasizing the implementation challenges. This review discusses existing solutions, operational environments, and the ML algorithms and traditional rules employed. The main objective of this study is to address these challenges by proposing an innovative architecture that equips organizations with the latest knowledge and methodologies in big data technologies for real-time fraud detection and prevention.
Optimization of hybrid PV-wind systems with MPPT and fuzzy logic-based control Fenniche, Ayoub; Harrouz, Abdelkader; Bellebna, Yassine; Laidi, Abdallah; Benlaria, Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp747-760

Abstract

The growing demand for sustainable and reliable energy solutions has driven the development of hybrid renewable energy systems (HRES) that combine multiple energy sources. This research explores the integration of solar energy and wind energy systems, utilizing permanent magnet synchronous generators (PMSG) for wind energy conversion. PMSGs are gaining popularity due to their high efficiency and ability to operate effectively in variable-speed wind conditions, making them ideal for hybrid systems. The study focuses on optimizing the energy extraction from both PV and wind systems using maximum power point tracking (MPPT) boost converters. The control for the MPPT boost converters is based on fuzzy logic (FL), a method that offers flexibility and adaptability in managing the non-linear and dynamic characteristics of renewable energy sources. A hybrid system consisting of PV, wind energy, and a battery storage system connected to a DC bus is simulated using MATLAB Simulink. The model demonstrates the effectiveness of integrating PV and wind energy with MPPT-controlled boost converters and fuzzy logic control, ensuring optimal energy utilization, stable system performance, and efficient energy storage. This research underscores the potential of hybrid renewable energy systems, showcasing how advanced control strategies can significantly improve the efficiency and reliability of energy generation and storage solutions.
Wirelength estimation for VLSI cell placement using hybrid statistical learning Ting Ming, Joyce Ng; Ab Rahman, Ab Al-Hadi; Khan, Nuzhat; Bakht, Muhammed Paend; Sadiah, Shahidatul; Rusli, Mohd Shahrizal; Marsono, Muhammad Nadzir
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp840-849

Abstract

Optimizing wirelength involves predicting the total length of wires needed to connect different components within a chip during cell placement. It is a fundamental challenge in very-large-scale integration (VLSI) of integrated circuit (IC) design, as it directly impacts the overall performance and manufacturability of chips. Accurate wire-length estimation in the early stages of the design process is critical for guiding subsequent optimization tasks. This paper proposes a novel hybrid linear regression wirelength (hybrid-LRWL) method that combines the strengths of existing methods rectilinear Steiner minimal tree (RSMT) for low-degree nets and a statistical learning-based approach for high-degree nets. Additionally, it compares the performance of three well-established wirelength estimation techniques: half-perimeter wirelength (HPWL), rectilinear minimum spanning tree (RMST), and RSMT. The methods were evaluated using the International Symposium on Physical Design (ISPD) 2011 benchmark suite, considering accuracy and computational efficiency. The experimental results demonstrated that the proposed hybrid method achieves superior accuracy, with a mean error of less than 0.05% in total wirelength, closely approximating RSMT results. The proposed method reduces computational time up to 3.6 times faster than traditional RSM-based methods. The results establish a strong framework for accurate and efficient wirelength estimation in VLSI design for modern, high-performance ICs.
Date fruit classification using CNN and stacking model kourtiche, Ikram; Bendjima, Mostefa; Kourtiche, Mohammed El Amin
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1373-1383

Abstract

In North Africa and the Middle East, the date is the most popular fruit, with millions of tons harvested annually. They are a crucial component of the diet due to their exceptional content of essential vitamins and minerals, which confer a high nutritional value. The ability to accurately identify and differentiate between date varieties is therefore of paramount importance in agriculture. It is crucial for improving agricultural practices, ensuring harvest quality, and contributing to the economic development of date-producing regions. In this paper, we propose a hybrid method for classifying date fruit varieties based on two stages. In the first stage, we select the two best-performing pre-trained models from six experimented deep learning models, and we concatenate the feature maps extracted from these two models. In the second stage, we apply different classification methods, including artificial neural networks (ANN), support vector machines (SVM), and logistic regression (LR). The performance achieved by these methods is 97.22%, 98.46%, and 99.07%, respectively. Then, with the stacking model, we combined these methods, and the performance result was increased to 99.38%. This result demonstrates the effectiveness of the hybrid model for identifying date fruit varieties.
Creating inclusive UX: uncovering gender-bugs in higher education website through GenderMag’ing Milagroso Santos, Maria Isabel; Palaoag, Thelma Domingo; Gamilla, Anazel Patricio
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp996-1004

Abstract

Higher education websites serve as service-providing and information-disseminating platforms which may contain gender-related usability issues that affect how male and female users interact with digital platforms. This study applied the gender inclusiveness magnifier (GenderMag) method to identify and assess these gender-specific usability barriers. Researchers conducted cognitive walkthrough sessions using gendered personas, Abi (female) and Tim (male), uncovering key inclusivity bugs aligned to specific cognitive facets-motivation, information processing style, computer self-efficacy, risk aversion, and learning style. Insights from these walkthroughs guided the creation of a structured usability survey, administered to 200 respondents equally divided between males and females, comprising faculty and upper-year BS information technology students. Statistical analysis revealed significant gender differences specifically in information processing style (p=0.0003), emphasizing distinct preferences for content organization and navigation between genders. The integration of usability factors with GenderMag’s cognitive facets effectively pinpointed areas requiring inclusive design adjustments, guiding future efforts to enhance equitable digital interactions in educational environments.

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