cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
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
 824   825   826   827   828 
Feature selection technique on convolutional neural network – multilabel classification task Hayami, Regiolina; Yusoff, Nooraini; Daud, Kauthar Mohd; Mukhtar, Harun; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp2001-2009

Abstract

Automated text-based recommendation, an artificial intelligence development, finds application in document analysis like job resumes. The classification of job resumes poses challenges due to the ambiguity in categorizing multiple potential jobs in a single application file, termed multi-label classification, deep learning, particularly convolutional neural networks (CNN), offers flexibility in enhancing feature representations. Despite its robust learning capabilities, the black-box design of deep learning lacks interpretability and demands a substantial number of parameters, requiring significant computational resources. The primary challenge in multilabel learning is the ambiguity of labels not fully explained by traditional equivalence relations. To address this, the research employs feature selection techniques, specifically the Chi-square method. The goal is to reduce features in deep learning models while considering label relevance in multi-label text classification, easing computational workload while preserving model performance. Experimental tests, both with and without the Chi-square feature selection technique on the dataset, underscore its substantial impact on the classification model's ability. The conclusion emphasizes the influence of the Chi-square feature selection technique on performance and computational time. In summary, the research underscores the importance of balancing computational efficiency and model interpretability, especially in complex multi-label classification tasks like job applications.
Enhancing phishing URL detection through comprehensive feature selection: a comparative analysis across diverse datasets Preeti, Preeti; Sharma, Priti
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1182-1188

Abstract

Malicious attacks have developed a prominent risk to the safety of online users, with attackers employing increasingly sophisticated systems to deceive unsuspecting victims. This research focuses on the critical aspect of feature selection in optimizing phishing uniform resource locator (URL) detection system. Feature selection boosts machine learning (ML) and deep learning (DL) by picking vital attributes efficiently. This research paper provides a comprehensive examination of feature selection techniques using five diverse datasets. Various methods, including random forest (RF) select from model, SelectKBest with chi-square statistic, principal component analysis (PCA) and recursive feature elimination (RFE), were employed. The experiments, with a particular emphasis on PCA and fourth dataset, revealed that all four models RF, decision trees (DTs), XGBoost, and multilayer perceptron) achieved 100% accuracy in detecting phishing URL attacks. This underscores the efficacy of feature selection methods in enhancing to a deeper understanding of feature selection’s role in bolstering the effectiveness of phishing detection system across diverse datasets, highlighting the importance of leveraging techniques such as PCA for optimal results.
Multi-objective-trust aware improved grey wolf optimization technique for uncovering adversarial attacks in WSNs Bannikuppe Srinivasiah, Venkatesh Prasad; Ranganathasharma, Roopashree Hejjaji; Ramanna, Venkatesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp375-391

Abstract

Wireless sensor network (WSN) is made of several sensor nodes (SN) that monitor various applications and collect environmental data. WSNs are essential for a wide range application, including healthcare, industrial automation, and environmental monitoring. However, these networks are susceptible to several security threats, underscoring the need for robust attack detection systems. Therefore, in this study, a multi-objective-trust aware improved grey wolf optimization (M-TAIGWO) is implemented to mitigate various attacks types. This implemented M-TAIGWO method is used to select secure cluster heads (CH) and routes to obtain secure communication through the network. The implemented M-TAIGWO provides improved security against malicious attacks by increasing the energy efficiency. The important aim of M-TAIGWO is to attain secured data transmission and maximize the WSN network lifetime. The M-TAIGWO method’s performance is evaluated through energy consumption and delay. The implemented method obtains a high PDR of 98% for 500 nodes, which is superior to the quantum behavior and gaussian mutation Archimedes optimization algorithm (QGAOA), with a delay of 15 ms for 100 nodes which is lesser than fuzzy and secured clustering algorithms. In comparison to the trust-based routing protocol for WSNs utilizing an adaptive genetic algorithm (TAGA), this implemented method achieves defense hello fold, black hole, sinkhole, and selective forwarding attacks effectively.
Risk disclosure and financial performance of Islamic banks in Jordan: the moderating role of financial technology Almomani, Mohammed Abd-Akarim; Al-Momani, Adai
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1711-1720

Abstract

Risk disclosure (RD) is important to inform investors. However, few studies examined this variable in developing countries and in Islamic bank context. This research investigates how RD affect financial performance (FP) of Islamic banks in Jordan. It also examines the moderating role of financial technology (FinTech). We use a quantitative method to examine how mandatory risk disclosure (MRD) and voluntary risk disclosure (VRD) impact return on assets (ROA) and return on equity (ROE) in Islamic banks operating in Jordan. Our results show that both MRD and VRD have a significant effect on FP of Islamic banks. Moreover, FinTech acts as a moderator in the connection between risk disclosure (MRD and VRD) and FP performance. The effect was compared before and after coronaviruses disease 2019 (COVID-19) and it shows that the COVID-19 has increased the effect of MRD and VRD on FP of Islamic banks. More focus on VRD and MRD will enhance the FP of Islamic banks in Jordan.
Exploring the tree algorithms to generate the optimal detection system of students' stress levels Yamasari, Yuni; Qoiriah, Anita; Rochmawati, Naim; Prapanca, Aditya; Prihanto, Agus; Suartana, I Made; Ahmad, Tohari
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp548-558

Abstract

The significant changes in the world of education after the coronavirus disease 2019 (COVID-19) pandemic have increased students' anxiety levels. This anxiety can trigger stress which can interfere with students' academic performance. Therefore, this condition is a critical problem that needs to be addressed immediately. However, researchers have not previously conducted much research to detect post-COVID stress levels. Apart from that, the existence of a system capable of carrying out this detection is still lacking. Therefore, this research focuses on building a system for detecting student stress levels. First, an exploration of the tree algorithm was carried out to find the most optimal method for recognizing student stress levels. Then a detection system is built using this optimal method. The research results show that the tree ID3 (Iterative Dichotomiser 3) algorithm achieves the highest accuracy value of 95% compared to other tree algorithms with the scenario of dividing training data into test data of 80%:20%. Moreover, this telegram bot-based detection system works well in recognizing three categories of stress, namely: light-, moderate-, and heavy stress based on black-box testing techniques.
Hybrid feature selection of microarray prostate cancer diagnostic system Ali, Nursabillilah Mohd; Hanafi, Ainain Nur; Karis, Mohd Safirin; Shamsudin, Nur Hazahsha; Shair, Ezreen Farina; Abdul Aziz, Nor Hidayati
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1884-1894

Abstract

DNA microarray prostate cancer diagnosis systems are widely used, and hybrid feature selection methods are applied to select optimal features to address the high dimensionality of the dataset. This work proposes a new hybrid feature selection method, namely the relief-F (RF)-genetic algorithm (GA) with support vector machine (SVM) classification method. The aim is to evaluate the performance of the proposed method in terms of accuracy, computation time, and the number of selected features. The method is implemented using Python in PyCharm and is evaluated on a DNA microarray prostate cancer. The outcome of this work is a performance comparison table for the proposed methods on the dataset. The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. Results show that GA has the highest average accuracy (91.17%) compared to PSO (90.52%) and WOA (85.74%). GA outperforms PSO and WOA due to its superior convergence properties and better alignment with complex problems.
Predicting customer churn in telecommunication sector using Naïve Bayes algorithm Agasti, Biswa Ranjan; Satpathy, Susanta
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1610-1617

Abstract

The telecom sector creates huge amounts of information every day as a result of its large customer base. Business professionals and decision-makers emphasized that maintaining existing clients is less expensive than recruiting new ones. Business analysts and customer relationship management (CRM)need to know the reasons why customers leave and the behavior patterns from earlier churn consumer’s data. Today, there is a problem with customer churn examination and prediction in the telecom industry since it is crucial for the sector to examine customer behavior to identify those who are going to stop their subscriptions. Customer retention could be increased by utilizing detection system to detect consumer behavior. Recent advancements in machine learning(ML)have made churn prediction more precise and practical. It is essential for identifying customers ready to leave using company’s products and services in the early stage. Hence in this work, predicting customers churn in telecommunication sector usingNaïveBayes(NB) model is presented. The performance of presentedNBalgorithmis evaluated using the parameters accuracy, precision, and sensitivity. The NB algorithm will have better performance than pervious approaches.
Factors affecting MOOC and LMS acceptance in basic training of newcomer civil servants in Indonesia Cahyawan, Robby; Djunaedi, Achmad; Subarsono, Agustinus; Susilastuti, Dewi Haryani
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1002-1011

Abstract

Pelatihan dasar calon pegawai negeri sipil (Latsar CPNS) is basic training for newcomer civil servants that must be followed during the pre-service period. The coronavirus disease 2019 (COVID-19) outbreak has caused face-to-face learning in the classroom to be canceled, so the learning process in the training organization must be replaced with a learning process using massive open online course (MOOC) and learning management system (LMS) with a distance learning system. This study used a modified form of the unified theory of acceptance and use of technology (UTAUT) model framework. The core factors in the UTAUT framework called facilitating conditions, will be divided into two factors. The two factors are the availability of infrastructure and devices, and internet capability (IC). The respondents of this study are newly recruited civil servants at the Ministry of Transportation of the Republic of Indonesia with 400 respondents used in the analysis process. We found that performance expectation (PE), effort expectation (EE), social influence (SI), and self-efficacy (SE) affect student behavior (SB). In addition, SI and IC affect SE. Meanwhile, the relationship between infrastructure and device availability (IDA) with SE has an insignificant result. In improving Latsar CPNS services, training organizations should pay attention to several factors that can influence SB.
Enhancing emotion detection with synergistic combination of word embeddings and convolutional neural networks Jadon, Anil Kumar; Kumar, Suresh
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1933-1941

Abstract

Recognizing emotions in textual data is crucial in a wide range of natural language processing (NLP) applications, from consumer sentiment research to mental health evaluation. The word embedding techniques play a pivotal role in text processing. In this paper, the performance of several well-known word embedding methods is evaluated in the context of emotion recognition. The classification of emotions is further enhanced using a convolutional neural network (CNN) model because of its propensity to capture local patterns and its recent triumphs in text-related tasks. The integration of CNN with word embedding techniques introduced an additional layer to the landscape of emotion detection from text. The synergy between word embedding techniques and CNN harnesses the strengths of both approaches. CNNs extract local patterns and features from sequential data, making them well-suited for capturing relevant information within the embeddings. The results obtained with various embeddings highlight the significance of choosing synergistic combinations for optimum performance. The combination of CNNs and word embeddings proved a versatile and effective approach.
Modified back-line inset feed 1x4 array microstrip antenna for 5.8 GHz frequency band Hasan, Md Fazlul; Awang Mat, Dayang Azra; Sayed, Md Abu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp892-900

Abstract

This paper presents the design of 1x4 array microstrip antenna utilizing modified backline feeding technique at 5.8 GHz frequency band. The antenna, designed on flame retardant (FR-4) substrate with a dielectric constant of 4.4, aims to achieve reduced harmonics and mutual coupling between closely spaced antenna elements. The primary scope of the paper is investigating the performance of a single band microstrip antenna employing the proposed modified backline feeding method. Moreover, developed design came out with the result and critical analysis by various parameters such as, gain, return loss, voltage standing wave ratio (VSWR), and directivity. Therefore, the proposed design of microstrip antenna with backward linefeed (BLF) demonstrates a directivity of 10.29 dBi, return loss of -21.947 dB, and VSWR of 1.173; are significant improvement compared to recent literature shown in this paper. The adoption of proposed back line feeding technique (BLF) represents a promising alternative for addressing poor wireless connectivity issues in terms of antenna design, gain, and direction within microstrip technology.

Page 826 of 914 | Total Record : 9138

 824   825   826   827   828 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 41, No 1: January 2026 Vol 40, No 3: December 2025 Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue