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31,456 Article Results

Modeling blockchain technology in assessment management: the initial readiness investigation

10.11591/ijere.v14i1.28416
Joharri Shuhaimi , Hapini Awang , Abdulrazak F. Shahatha Al-Mashhadani , Mohd Fairuz Jafar , Nur Suhaili Mansor
The implementation of the school-based assessment (SBA) came with a system as part of the effort to reduce the workload of teachers in managing the SBA. However, a traditional system is costly and heavily centralized, frequently leading to impediments, delays, irregularities and may even create opportunities for dishonest activities. To enhance the existing traditional system, integrating emerging blockchain technology (BC) is proposed. Given the challenges, complaints, and rejection faced by traditional assessment management systems, this paper presents a conceptual framework based on the technology acceptance model (TAM) and diffusion of innovation (DOI) to examine pre-university teachers’ perception of implementation and readiness to adopt BC-based project paper management (BPPM) which is subsequently verified through a qualitative study. This study employs a process that consists of three main steps: a thorough review of existing literature, thematic analysis, and the process of gathering and determining readiness factors. Teachers’ perspectives were successfully gathered from 59 pre-university teachers by posting semi-structured questions in a Telegram group of Malaysian pre-university teachers. The study found that most pre-university teachers support the BPPM implementation with the condition that the system can address two primary concerns: server scalability and confidentiality issues. The effectiveness of BPPM implementation depends on its capacity to meet user needs and expectations while delivering clear benefits, along with superiority compared with existing systems.
Volume: 14
Issue: 1
Page: 389-397
Publish at: 2025-02-01

Overcoming imbalanced rice seed germination classification: enhancing accuracy for effective seedling identification

10.11591/ijai.v14.i1.pp62-72
Muhlasah Novitasari Mara , Sidiq Syamsul Hidayat , Farika Tono Putri , Dwi Rahmawati , Sri Wahyuni , Muhamad Cahyo Ardi Prabowo , Noer Ni'mat Syamsu Kabir , Ragil Tri Indra
This study aimed to automatically classify rice seedling germination on day seven using image analysis. The categories included normal, abnormal, and dead seeds. Due to the rarity of abnormal seedlings, capturing their images resulted in imbalanced data. To address this, abnormal categories were combined into a single class. We compared logistic regression, random forest, and deep learning models (VGG19, VGG16, Alex Net) for classification. Surprisingly, logistic regression achieved the highest accuracy (93.89%) and F1-scores (0.96 normal, 0.81 abnormal) despite the imbalanced data and complex task. The effectiveness of logistic regression for rice seedling classification with imbalanced data has been demonstrated in this novel research. Historically, deep learning models dominate image recognition, but our findings suggest simpler models can excel in specific scenarios, especially with limited data availability. This highlights the importance of selecting models based on data characteristics. The urgency for this research stems from the need for efficient and accurate rice seedling evaluation. Improved classification can enhance agricultural practices and optimize resource allocation.
Volume: 14
Issue: 1
Page: 62-72
Publish at: 2025-02-01

Unraveling social and economic problems: what basics of critical thinking skills are needed?

10.11591/ijere.v14i1.29180
Wiwik Sri Utami , Kusaeri Kusaeri , Ali Ridho , Ahmad Yusuf , Bambang Sigit Widodo , Hendri Prastiyono , Sri Murtini
Equipping knowledge and developing critical thinking skills about social and economic issues to students is important. This study aimed to analyze the ability of prospective students of Islamic State of Madrasah of Insan Cendekia (ISM-IC) to think critically about socioeconomic problems. This research reveals a critical thinking skills gap among prospective students of Madrasah Tsanawiyah/Islamic Junior High School (IJHS) and non-IJHS who participated in the national selection of new students (NSoNS). This study uses a cross-sectional survey approach with standard instruments from the NSoNS ISM-IC. Data was obtained from 1,832 participants in the social sciences group test, consisting of 1,197 people from IJHS and 635 from non-IJHS. Data analysis using R Programming, package ‘ggstatsplot’, and via Microsoft Excel. The result is: i) a gap in social studies scores between IJHS and non-IJHS students of -0.215, with the social studies scores of IJHS students lower than non-IJHS; ii) critical thinking skills are proven to have a very real impact on the occurrence of social studies score gaps, through the inability of students to answer the higher-order thinking skills (HOTs) category questions. This finding indicates the urgency of the education unit to provide nuanced literacy models and learning and HOTs to increase think of critic ability about socioeconomic matter.
Volume: 14
Issue: 1
Page: 566-574
Publish at: 2025-02-01

An ontology-based knowledge modeling for the rite of Bai Sri Su Kwan: a ritual of the Greater Mekong Subregion

10.11591/ijai.v14.i1.pp788-797
Suwannee Hoaihongthong , Vispat Chaichuay , Kanyarat Kwiecien
The development of ontologies is crucial in digital humanities research. This study focuses on creating a system to extract meaning from knowledge related to the Bai Sri Su Kwan ritual. Addressing semantic gaps, the second phase of our research outlines methods for developing an ontology for Bai Sri Su Kwan rituals. To fully understand this significant ritual in the Mekong Basin, we employed a theoretical framework with seven ontology development steps, using the Hozo Ontology Editor. Our ontology includes nine main classes: Bai Sri Su Kwan (A ritual subclass), persons, chants, belief, purpose, wish, literature, locations, and equipment. The Bai Sri Su Kwan subclass connects with all other classes in the ontology. This ontology forms the basis for a meaning search system for the Bai Sri Su Kwan ceremony in future research stages. The ontology was evaluated syntactically through human assessment and the OOPS! Ontology Pitfall Scanner. Validation results for the Bai Sri Su Kwan Ontology show no pitfalls in critical dimensions, indicating high integrity and reliability.
Volume: 14
Issue: 1
Page: 788-797
Publish at: 2025-02-01

A novel global maximum power point tracking based on flamingo search algorithm for photovoltaic systems

10.11591/ijece.v15i1.pp1015-1026
Abdelghani Draoui , Ahmed Saidi , Boumediene Allaoua , Abdrabbi Bourezg
Due to the high dependency of photovoltaic (PV) solar cell’s output on solar irradiance and, the ambient temperature. Maximum power point tracking (MPPT) algorithms are used extensively to operate the system at its full potentials. Moreover, being installed in outdoor spaces, PV modules are inevitably subjected to partial shading conditions, where different parts of the system are receiving different amounts of solar irradiance. In case of occurrence of partial shading conditions on a PV module that is equipped with bypass diodes, the power-voltage (P-V) curve will have multiple peaks. This multi-peak curve requires using an advanced algorithm which track the global maximum power point (GMPP) instead of being deceived and trapped in a local maximum power point. In this paper, the flamingo search algorithm (FSA) is adapted for GMPP tracking for a PV system under partial shading conditions. The FSA algorithm fetch for the GMPP by reading the PV panel power and setting accordingly the duty cycle of the buck converter. To investigate model validity, simulation is performed using the MATLAB/Simulink platform and results demonstrate good tracking performance and fast response that prove the robustness of the system against rapid variations in solar irradiance levels.
Volume: 15
Issue: 1
Page: 1015-1026
Publish at: 2025-02-01

Perspective on the applications of terahertz imaging in skin cancer diagnosis

10.11591/ijece.v15i1.pp1242-1250
Hamza Abu Owida , Jamal I. Al-Nabulsi , Muhammad Al-Ayyad , Nidal Turab , Nawaf Alshdaifat
Applications of terahertz (THz) imaging technologies have advanced significantly in the disciplines of biology, medical diagnostics, and non- destructive testing in the past several decades. Significant progress has been made in THz biomedical imaging, allowing for the label-free diagnosis of malignant tumors. Terahertz frequencies, which lie between those of the microwave and infrared, are highly sensitive to water concentration and are significantly muted by water. Terahertz radiation does not cause ionization of biological tissues because of its low photon energy. Recently, terahertz spectra, including spectroscopic investigations of cancer, have been reported at an increasing rate due to the growing interest in their biological applications sparked by these unique features. To improve cancer diagnosis with terahertz imaging, an appropriate differentiation technique is required to increased blood supply and localized rise in tissue water content that commonly accompany the presence of malignancy. Terahertz imaging has been found to benefit from structural alterations in afflicted tissues. This study provides an overview of terahertz technology and briefly discusses the use of terahertz imaging techniques in the detection of skin cancer. Research into the promise and perils of terahertz imaging will also be discussed.
Volume: 15
Issue: 1
Page: 1242-1250
Publish at: 2025-02-01

Human motion classification by micro-doppler radar using intelligent algorithms

10.11591/ijece.v15i1.pp455-466
Andres Felipe Arias Ballen , Edith Paola Estupiñan Cuesta , Juan Carlos Martinez Quintero
This article introduces a technique for detecting four human movements using micro-doppler radar and intelligent algorithms. Micro-doppler radar exhibits the capability to detect and measure object movements with intricate detail, even capturing complex or non-rigid motions, while accurately identifying direction, velocity, and motion patterns. The application of intelligent algorithms enhances detection efficiency and reduces false alarms by discerning subtle movement patterns, thereby facilitating more accurate detection and a deeper understanding of observed object dynamics. A continuous wave radar setup was implemented utilizing a spectrum analyzer and radio frequency (RF) generator capturing signals in a spectrogram centered at 2,395 MHz. Six models were assessed for image classification: VGG-16, VGG-19, MobileNet, MobileNet V2, Xception, and Inception V3. A dataset comprising 500 images depicting four movements-running, walking, arm raising, and jumping-was curated. Our findings reveal that the most optimal architecture in terms of training time, accuracy, and loss is VGG-16, achieving an accuracy of 96%. Furthermore, precision values of 96%, 100%, and 98% were obtained for the movements of walking, running, and arm raising, respectively. Notably, VGG-16 exhibited a training loss of 4.191E-04, attributed to the utilization of the Adam optimizer with a learning rate of 0.001 over 15 epochs and a batch size of 32.
Volume: 15
Issue: 1
Page: 455-466
Publish at: 2025-02-01

Hybrid optimization algorithm for analysis of influence propagation in social network

10.11591/ijece.v15i1.pp624-634
Akshata Sandeep Bhayyar , Kiran Purushotham
Influence maximization(IM) is defined as the problem of identifying a node subset in a social network which increases the spread of influence. IM plays a crucial role in social networks by catalyzing the dissemination of influence, resulting in an augmented count of influenced nodes following the propagation process. The existing researches mainly concentrated on increasing the spread of influence, but did not consider the running time of the network. In this manuscript, the salp swarm algorithm (SSA) and bi-adaptive strategy particle swarm optimization (BiAS-PSO) algorithms are integrated and named as SS-BiAS-PSO algorithm to increase the spread of influence based on the IM problem to minimize the running time of the network. The datasets utilized for the research are Ego-Facebook, Epinions, Gowalla, and HepTh, while linear threshold (LT) is utilized as a diffusion method. Then, the proposed SS-BiAS-PSO algorithm is deployed for the analysis of influence propagation. The proposed algorithm reaches a high influence spread of 645, 680, 715, and 750 with less running times respectively for 10, 20, 30, and 40 seed set sizes in Ego-Facebook. The proposed algorithm proves more effective than the existing techniques like traditional SSA and particle swarm optimization (PSO).
Volume: 15
Issue: 1
Page: 624-634
Publish at: 2025-02-01

Ant lion and ant colony optimization integrated ensemble machine learning model for effective cancer diagnosis

10.11591/ijece.v15i1.pp604-613
Pinakshi Panda , Sukant Kishoro Bisoy , Amrutanshu Panigrahi , Abhilash Pati
Statistics from reputable sources, including the World Health Organization (WHO), demonstrate that cancer is a leading cause of death globally, accounting for millions of deaths each year. When it comes to the early identification of cancer, machine learning (ML) is crucial. To analyze complex data and identify minute patterns that may indicate the presence of cancer, it employs robust computational approaches. Improving patient outcomes relies on early cancer detection since it paves the way for faster treatment and intervention, which might lead to better prognoses and higher survival rates. To choose features, this study intends to build an ML-based ensemble model utilizing ant colony optimization (ACO) and ant lion optimization (ALO). Next, ML classifiers are used as the initial predictions' basis learners. The last forecast is the result of combining two ensemble methods: voting and averaging classifiers. Four distinct cancer microarray datasets are used to assess the approach. With an accuracy of 99.08% on the Lung cancer dataset, the voting ensemble classifier outperforms the others, according to the empirical analysis.
Volume: 15
Issue: 1
Page: 604-613
Publish at: 2025-02-01

Enhancing internet of things attack detection using principal component analysis and kernel principal component analysis with cosine distance and sigmoid kernel

10.11591/ijece.v15i1.pp1099-1108
Zyad Elkhadir , Mohammed Achkari Begdouri
The widespread adoption of internet of things (IoT) devices has brought about unprecedented levels of connectivity and convenience. However, it has also introduced significant challenges, particularly in the areas of security and privacy. This study addresses the critical issue of intrusion detection within IoT environments, with a specific focus on analyzing the Iot-23 dataset. Our methodology involves employing principal component analysis (PCA) and kernel PCA for dimensionality reduction. Subsequently, we utilize the k-nearest neighbors (KNN) algorithm for classification purposes. To optimize the performance of the KNN algorithm, we experiment with various feature scaling techniques, such as StandardScaler, MinMaxScaler, and RobustScaler, utilizing different distance metrics. In our analysis, we discovered that employing the cosine distance metric in combination with KNN resulted in superior intrusion detection performance when utilizing PCA. Additionally, when utilizing kernel PCA, we evaluated multiple kernel functions and determined that the radial basis function and sigmoid kernel yielded the most favorable results.
Volume: 15
Issue: 1
Page: 1099-1108
Publish at: 2025-02-01

Analytic hierarchy process geographic information system based model for sustainable construction and demolition waste landfill site selection

10.11591/ijece.v15i1.pp803-816
Mohamed Ayet Allah Bilel Soussi , Nermine El Madsia , Chamseddine Zaki , Alaaeddine Ramadan , Louai Saker , Moustafa Ibrahim
Properly managing waste generated by buildings and public works is a significant challenge in Tunisia, particularly in the city of Bizerte. The inadequate disposal of such waste can cause substantial harm to human life, property, and the environment. This paper proposes an multi-criteria decision making (MCDM) that utilizes the analytic hierarchy process (AHP) decision support tool to identify suitable landfill sites for construction and demolition waste (CDW) in Bizerte. The AHP method is widely used in MCDM applications. The approach involves classifying different scenarios based on various exclusion and appreciation criteria to determine the optimal locations for future landfills. Furthermore, the paper develops a conceptual approach for identifying better sites for the disposal of CDW, resulting in a comprehensive database capable of storing, accessing, and extracting information at both conceptual and operational levels. The proposed model considers spatial, technical, and environmental criteria in the selection of a suitable landfill site. The proposed methodology offers an effective and practical solution for properly managing CDW waste in Bizerte, Tunisia, and can be applied to other regions facing similar challenges.
Volume: 15
Issue: 1
Page: 803-816
Publish at: 2025-02-01

Method for developing and partitioning graph-based data warehouses using association rules

10.11591/ijai.v14.i1.pp810-821
Redouane Labzioui , Khadija Letrache , Mohammed Ramdani
The evolution of modern databases has led to a variety of not only structured query language (NoSQL) models, particularly graph-oriented-databases. This growth has encouraged businesses to explore graph-based business intelligence (BI) solutions. This paper explores three essential aspects in the domain of graph warehouse: the establishment of efficient graph warehouses, the significance of data historization, and the development of effective strategies for graph partitioning. It starts by building a BI system within a graph database. Subsequently, the paper emphasizes the pivotal role of data historization, highlighting the slowly graph changing dimension (SGCD) approach as a versatile framework for accommodating varied dimensional changes, additionally; the paper introduces a novel partitioning strategy utilizing association rules algorithms, for optimized and scalable graph warehouse management.
Volume: 14
Issue: 1
Page: 810-821
Publish at: 2025-02-01

Higher order thinking skills research trends: a bibliometric analysis in selected journals (2014 to 2023)

10.11591/ijere.v14i1.29590
Lilis Lismaya , Hartono Hartono , Bambang Subali , Woro Sumarni , Saiful Ridlo , Murbangun Nuswowati
This article aims to analyze the trend of higher order thinking skills (HOTS) research in selected journals, namely Journal of Research in Science Teaching (JRST), International Journal of Science Education (IJSE) and Review of Educational Research (RER) through bibliometrics on the Scopus database from 2014 to 2023. The articles consist of a total of 947, then divided into 333 articles analyzed, with details of JRST 113 articles, IJSE 130 articles and RER 90 articles. The method used in this research is article analysis using bibliometric stages, followed by keyword search, initial search results, correction of search results, initial data compilation and data analysis. The results showed that the publication of scientific articles in selected journals on HOTS varied from year to year. The results of this study are expected to help other researchers who are interested in reviewing and researching research trends related to HOTS in selected journals and to recommend further research directions.
Volume: 14
Issue: 1
Page: 695-707
Publish at: 2025-02-01

Identification of the satisfaction of university students through sentiment analysis: a systematic review

10.11591/ijere.v14i1.26640
Omar Freddy Chamorro-Atalaya , Florcita Aldana-Trejo , Nestor Alvarado-Bravo , Constantino Nieves-Barreto , Santiago Aguilar-Loyaga , Carlos Gamarra-Bustillos , Almintor Torres-Quiroz , Alípio Riveros-Cuéllar , Manuel Pérez-Samanamud , Luciano Pérez-Guevara
In these times it is necessary to use tools based on artificial intelligence (AI) that contribute to improving the quality of university education for the benefit of students. This article aims to define the state of the question on the application of sentiment analysis in the identification of student satisfaction, based on the systematic review of scientific publications. The research is of an exploratory level and of a mixed approach. The data collection method was based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) declaration, managing to focus the review based on 27 publications, downloaded from Scopus, ERIC, and Google Scholar. From the systematic review, the following conclusions were reached: the fields of application to a greater extent are found in the academic field and university well-being. Likewise, regarding the contributions achieved, these focused to a greater extent on aspects of the teaching activity, valuing their performance and contributing to their feedback for the redesign of didactic strategies. Finally, in terms of limitations, they focused mainly on the low student participation regarding the use of sentiment analysis to identify student satisfaction; this is due to the lack of regulations or regulations for it is application in the university environment.
Volume: 14
Issue: 1
Page: 37-49
Publish at: 2025-02-01

Cloud computing environment based hierarchical anomaly intrusion detection system using artificial neural network

10.11591/ijece.v15i1.pp1209-1217
Mangalapalli Vamsikrishna , Garapati Swarna Latha , Gajjala Venkata Ramesh Babu , Koppisetti Giridhar , Lakshmeelavanya Alluri , Giddaluru Somasekhar , Bhimunipadu Jestadi Job Karuna Sagar , Naresh Dondapati
Nowadays, computer technology is essential to everyday life, including banking, education, entertainment, and communication. Network security is essential in the digital era, and detecting intrusion threats is the most difficult problem. As a result, the network is monitored for unusual activity using this hierarchical anomaly intrusion detection system, and when these actions are detected, an alert is generated. This hierarchical anomaly intrusion detection system, which uses artificial neural network (ANN) and is implemented on a cloud computing environment, analyzes data even in the high levels of traffic and protects computer networks and data from malicious activity. As a result, this system shows better detection, accuracy, and precision rates.
Volume: 15
Issue: 1
Page: 1209-1217
Publish at: 2025-02-01
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