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Eligibility rate of applicant’s LinkedIn account: a naïve bayes classification and visualization

10.11591/ijai.v13.i4.pp4334-4343
Khyrina Airin Fariza Abu Samah , Nurul Athirah Ahmad , Anis Amilah Shari , Hana Fakhira Almarzuki , Zuhri Arafah , Lala Septem Riza , Amir Haikal Abdul Halim
In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. However, the screening process and manual recruitment process entail significant time, high costs, and potential bias. Consequently, it may cause recruiting unqualified applicants and may affect the organizations. Thus, this study aims to classify and generate a list of potential job applicants by analyzing seven attributes of their LinkedIn accounts: title, location, skills, education, language, certification, and years of experience. Data are collected from LinkedIn profiles and then undergo data pre-processing. The naive Bayes (NB) algorithm is implemented as the classification algorithm and sets the classification as “eligible” or “ineligible”. The NB model achieved an accuracy testing of 89.8%, indicating good performance in classifying potential job applicants. At the same time, we measure the similarity cosine score to set the mean of the eligibility. The classification results are visualized for the suitable applicants in descending rank, allowing users to choose the applicants’ classification status efficiently. For the system usability, we managed to get 90% from the recruitment expert.
Volume: 13
Issue: 4
Page: 4334-4343
Publish at: 2024-12-01

The relationship between principal inclusive leadership, teacher efficacy and teachers’ innovative behavior

10.11591/ijere.v13i6.29187
Mo Dongxiao , Suhaida Abdul Kadir , Roshafiza Hassan , Enio Kang Mohd Sufian Kang
The objective of this study was to explore the relationship among principals’ inclusive leadership, teachers’ sense of efficacy, and teachers’ innovative behaviors. A sample of 671 middle school teachers from Guangxi, China was utilized. The study revealed that the practice of inclusive leadership by principals had a positive effect on teacher efficacy. Furthermore, teacher efficacy appeared to have a beneficial effect on teacher innovative behavior. Additionally, the study showed that principal inclusive leadership significantly influenced teacher innovative behavior. Lastly, it proved that teacher efficacy played a mediating role in the relationship between principal inclusive leadership and teacher innovative behavior. The study offers strategies for enhancing teacher’s propensity for innovative conduct. When designing programs to promote teacher innovative behavior, it is important to take into account the impact of inclusive leadership by principals and the magnitude of teacher efficacy.
Volume: 13
Issue: 6
Page: 4338-4345
Publish at: 2024-12-01

Control and monitoring of 1 phase generator automatic voltage regulator internet of things

10.11591/ijece.v14i6.pp7158-7168
Arnawan Hasibuan , Farhan Akbar , Selamat Meliala , Rosdiana Rosdiana , Raihan Putri , I Made Ari Nrartha
This research focuses on the importance of maintaining generator output voltage stabilization using automatic voltage regulator (AVR), especially in dealing with load impacts and environmental changes. Through the implementation of internet of things (IoT), this system can be controlled and monitored remotely, enabling real-time monitoring without physical presence at the generator location. The main objective of this research is to increase energy efficiency by optimizing generator operation. System development methods include design, prototyping and testing stages. Test results show that the automatic voltage regulator is effective in maintaining a stable output voltage of around 220 V, even though the current varies according to the existing load. The power produced ranges from 23 to 670 W, with a power factor between 0.7 and 1. Despite a slight voltage drop to 217 V, the power factor increases to 0.93. The system uses NodeMCU to send data to Blynk and Google Spreadsheets servers, as well as servo motors and PZEM-004T sensors for control and monitoring. Overall, this research shows that the internet of things-based automatic voltage regulator system is effective in maintaining stability and increasing generator operational efficiency, with the ability to manage voltage, current, power, and power factor efficiently.
Volume: 14
Issue: 6
Page: 7158-7168
Publish at: 2024-12-01

Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning

10.11591/ijai.v13.i4.pp4572-4582
Thomas Oka Pratama , Sunarno Sunarno , Agus Budhie Wijatna , Eko Haryono
The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involves multiple metrics like mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), mean squared error (MSE), symmetric mean absolute percentage error (SMAPE), and conformal normalized mean absolute percentage error (cnSMAPE). XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnSMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.
Volume: 13
Issue: 4
Page: 4572-4582
Publish at: 2024-12-01

Sentiment-electroencephalogram fusion for efficient product review prediction using correlation-based deep learning neural network

10.11591/ijai.v13.i4.pp4675-4687
Rahul Kumar Sharma , Arvind Dagur
Various techniques have been proposed and implemented in previous work for sentiment analysis prediction. However, achieving satisfactory quality of description and fault prediction remains a challenging task. To overcome these limitations, this study proposes an efficient prediction technique that utilizes sentiment analysis of product reviews and electroencephalogram (EEG) signals using correlation-based deep learning neural network (CDNN). The study employs two types of datasets: EEG signals and Amazon product reviews. During the pre-processing phase, EEG signals undergo normalization, while Amazon product reviews undergo tokenization, stop word removal, and weighting factor application to convert unstructured data into a structured format. Subsequently, the pre-processed EEG signals and reviews are analyzed to extract features like emotion, demographic information, personality traits, and sentiment. These features are then employed in sentiment analysis via an entropy-based deep-learning neural network. The proposed CDNN utilizes the grasshopper optimization algorithm (EGOA) to optimize hyperparameters for each layer. Comparative performance assessment against established methods like convolutional neural network (CNN), long short-term memory (LSTM), multiclass support vector machine (M-SVM), and bidirectional encoder representations from transformers (BERT) is conducted, and the results are evaluated. Experimental result reveal that the proposed system outperforms traditional approaches.
Volume: 13
Issue: 4
Page: 4675-4687
Publish at: 2024-12-01

Effective ethernet controller protocol architecture verification strategy using system Verilog

10.11591/ijece.v14i6.pp6195-6203
Shubha Parameshwara , Ganapathi Vithoba Sagar , Hamsa Rekha Sorekunte Dasappa , Sheetal Nagaraj
The pre-silicon verification is typically more significant than post-silicon verification, which produces an algorithm with the correct functionality and timing parameters. In this paper we propose innovative pre-silicon verification methodology focused on the Ethernet controller architecture as the design under test (DUT). The methodology employs a layered verification architecture implemented using the system Verilog language, aiming to streamline the testing process. A novel test pattern test generator, interfaces and blocks are used to perform the verification. The test patterns are generated based on the operational principles of the ethernet controller block, ensuring comprehensive verification coverage. Additionally, the paper combines different verification parameters with existing approaches to demonstrate the effectiveness of the proposed methodology. It is observed that the performance of the proposed method is better compared to existing methods.
Volume: 14
Issue: 6
Page: 6195-6203
Publish at: 2024-12-01

A deep learning-based surveillance system for enhancing public safety through internet of things and digital technology using Raspberry Pi

10.11591/ijece.v14i6.pp7198-7210
Shreedevi Kareppa Sanapannavar , Chayadevi Mysore Lakshmanagowda , Geetha Sundararajan
In public spaces, individuals encounter challenges due to the prevalence of malicious activities like theft and kidnapping. As the internet of things (IoT) and digital technology continue to expand rapidly, efforts to create safe environments are becoming increasingly sophisticated. To address these security concerns, a proposed solution involves the utilization of video-capturing technology with the help of a Raspberry Pi web camera. Videos of the surroundings are recorded, a digital signature algorithm is applied to protect the videos, and they are then transmitted to authorized individuals who use them for forensic analysis. This process allows for the identification and investigation of any suspicious or criminal activities. The captured video data is compared with a standard dataset using a deep learning process. By analyzing the content of the videos and identifying the potential threat objects, we can allow for prompt intervention or further investigation by relevant authorities.
Volume: 14
Issue: 6
Page: 7198-7210
Publish at: 2024-12-01

Validation of instruments on the headmaster’s technology leadership model by using confirmatory factor analysis

10.11591/ijere.v13i6.29061
Pang Lai Chaw , Mat Rahimi Yusof , Mohamad Khairi Othman
In 21st-century education, the role of school headmasters in technology leadership is increasingly crucial. This is due to the fact that administrators are the main force behind efforts to integrate technology in classrooms. Some headmasters resist technology use and lack understanding of its role in leadership, refraining from participating in Malaysian Ministry of Education (MoE) initiatives for technology change. To promote teachers’ integration of technology into the teaching process, headmasters need a comprehensive understanding of its implementation for both management and leadership purposes. Therefore, this study aims to assess the impact of headmasters’ technology leadership and confirm its aspects in primary schools through cross-sectional quantitative analysis. The principal technology leadership assessment (PTLA) instrument, which was given online to 516 teachers using a straight forward random selection method, served as the basis for the headmaster technology leadership questionnaire. Using IBM-SPSS-AMOS software, descriptive statistics, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) were used to evaluate the data. Six dimensions and 35 items in technology leadership are approved and validated, according to CFA results. This study successfully created an assessment model for headmasters’ technology leadership. To enhance benefits, contribute more, and address identified gaps, future researchers are encouraged to conduct larger-scale studies using both qualitative and quantitative methods.
Volume: 13
Issue: 6
Page: 3717-3724
Publish at: 2024-12-01

Evolution of the concept of device moving from internet of things to artificial intelligence of things

10.11591/ijece.v14i6.pp7236-7243
Gaetanino Paolone , Francesco Pilotti , Andrea Piazza
The internet of things (IoT) refers to a network of physical devices that are embedded with sensors, software, and network connectivity, allowing them to collect and share data. The devices are the core of any IoT ecosystem. Browsing the extant literature, it emerges that the meaning of the term device depends on the reference context. It follows that, it is an important topic to investigate the reasons behind such a degree of indeterminacy. This paper elaborates on the evolution of the concept of device moving from IoT to artificial intelligence of things (AIoT). The finding that comes from this study is that this evolution is a direct consequence of the evolution of the IoT computing paradigms.
Volume: 14
Issue: 6
Page: 7236-7243
Publish at: 2024-12-01

Real-time management and processing of RFID events based on a new RFID middleware architecture

10.11591/ijece.v14i6.pp6583-6599
Achraf Haibi , Kenza Oufaska , Khalid El Yassini , Hajar Bouazza , Mohammed Boulmalf , Mohsine Bouya
Radio frequency identification (RFID) is a contemporary technology that enables the identification of objects and facilitates the transmission of additional information, making it possible to achieve real-time object tracking in a mobile object network and to report information on the object's current state at each step. RFID devices continuously generate large amounts of data, and collecting, filtering, and consolidating these data are therefore crucial tasks, which characterize RFID data management by a set of challenges. However, one of the greatest challenges in this field is managing large volumes of data in complex applications, where real-time operation is vital, given that the volume and speed of RFID data often exceed the capacity of the existing technological infrastructure. The aim of this study is to propose an RFID middleware that manages both the RFID hardware network and the large amounts of data that are captured, in order to process and transmit the collected data under the right conditions for ultimate use by an information system. This new RFID middleware architecture, named BTMiddleware combines complex event processing (CEP) with a MongoDB database to offer large-volume data streaming, processing, and storage in real time, as well as better interoperability thanks to the use of the JavaScript object notation (JSON) format for data presentation.
Volume: 14
Issue: 6
Page: 6583-6599
Publish at: 2024-12-01

About one lightweight encryption algorithm ensuring the security of data transmission and communication between internet of things devices

10.11591/ijece.v14i6.pp6846-6860
Sabyrzhan Atanov , Yerzhan Seitkulov , Khuralay Moldamurat , Banu Yergaliyeva , Abzal Kyzyrkanov , Zhexen Seitbattalov
In this paper, a new encryption algorithm Twine-Mersenne was developed based on the Twine algorithm with the addition of a random number generator for the dynamic generation of S-boxes. Dynamic generation of random numbers based on the Mersenne Twister helps to increase the cryptographic strength of the proposed algorithm. The algorithm we propose solves the issues of optimizing the costs of computing and energy resources of internet of things (IoT) devices, using a combination of lightweight cryptographic principles and fuzzy logic, and also provides reliable security and intelligent authentication of the mobile application user. The paper also considers the practical implementation of the proposed algorithm based on Arduino ESP32, a device with limited computing resources. In addition to this, fuzzy logic has found its practical application in the field of intelligent user authentication in developed mobile applications based on Arduino Studio for mobile cellular applications. As a result, the proposed lightweight encryption algorithm has proven itself to be an effective tool in ensuring the security of data transmission and communication between IoT devices.
Volume: 14
Issue: 6
Page: 6861-6873
Publish at: 2024-12-01

Indonesian sentiment towards global economic recession in 2023 using optimized hyperparameters of support vector machine kernels

10.11591/ijai.v13.i4.pp4948-4956
Dairatul Maarif , Adinda Aulia Hafizha , Andi Kurniawan
The potential for the 2023 global recession has troubled people worldwide, particularly in light of the COVID-19 pandemic. This study employs a sentiment analysis approach to examine how the Indonesian internet community, particularly on Twitter, perceives the topics related to the global economic recession. We collected 11,017 uploaded tweets that were analyzed using support vector machine classifier with linear, radial basis function (RBF), sigmoid, and polynomial kernel schemes. Furthermore, we optimized the classifiers with C, Gamma, and degree hyperparameters. Empirical evidence indicates a lack of preparedness to face a global recession, evidenced by most responses towards 2023 global recession exhibiting concerns about high inflation and economic instability. The finding also suggests that the optimized RBF is a superior modeling kernel relative to others. Collectively, these results provide insights with significant implications for sentiment analysis, natural language processing, and the study of behavioural economics.
Volume: 13
Issue: 4
Page: 4948-4956
Publish at: 2024-12-01

A novel approach for imbalanced instance handling toward better preterm birth classification

10.11591/ijece.v14i6.pp6129-6139
Himani Deshpande , Leena Ragha
Preterm birth (PTB) is a major cause of child and mother mortality, a PTB classification model can assist in assessing the health condition ahead of time and help avoid complications during childbirth. Mother’s significant feature (MSF) dataset created for this study has features derived from mother’s physical, lifestyle, social and stress attributes. MSF dataset consists of 119 features of 1,000 mothers with 172 preterm and 828 full-term deliveries, resulting in issues of dataset imbalance namely class inseparability and classification bias. To overcome the imbalance issue, a novel algorithm named majority penalizing minority upsampling (MPMU) is proposed. MPMU forms clusters looking into the degree of dataset imbalance, it analyses the composition of each cluster individually and computes the varied penalty for majority class instances. It further balances dataset composition by oversampling minority class instances. MPMU processed dataset is further used to train the proposed 6L-ANN network which finds the probability of occurrence of PTB. The proposed model has shown efficient results on MSF sub-datasets with precision values ranging from 0.90 to 0.97, area under the curve (AUC) between 0.86 to 0.99, and prediction accuracy ranging from 93.04% to 99.47%. Experiment results show that a mother’s lifestyle and stress features have a strong influence on the childbirth outcome.
Volume: 14
Issue: 6
Page: 6129-6139
Publish at: 2024-12-01

Comparison of algorithms for the detection of marine vessels with machine vision

10.11591/ijece.v14i6.pp6332-6338
José Rodríguez-Gonzales , Junior Niquin-Jaimes , Ernesto Paiva-Peredo
The detection of marine vessels for revenue control has many tracking deficiencies, which has resulted in losses of logistical resources, time, and money. However, digital cameras are not fully exploited since they capture images to recognize the vessels and give immediate notice to the control center. The analyzed images go through an incredibly detailed process, which, thanks to neural training, allows us to recognize vessels without false positives. To do this, we must understand the behavior of object detection; we must know critical issues such as neural training, image digitization, types of filters, and machine learning, among others. We present results by comparing two development environments with their corresponding algorithms, making the recognition of ships immediately under neural training. In conclusion, it is analyzed based on 100 images to measure the boat detection capability between both algorithms, the response time, and the effectiveness of an image obtained by a digital camera. The result obtained by YOLOv7 was 100% effective under the application of processing techniques based on neural networks in convolutional neural network (CNN) regions compared to MATLAB, which applies processing metrics based on morphological images, obtaining low results.
Volume: 14
Issue: 6
Page: 6332-6338
Publish at: 2024-12-01

Research trends about Visual Basic as a programming language in the learning process: a bibliometric analysis

10.11591/ijece.v14i6.pp6498-6507
Adi Nurjaman , Dadang Juandi , Edi Supriyadi , Wahyu Hidayat , Darhim Darhim
This study employs bibliometric analysis to systematically explore the Visual Basic (VB) education research landscape, identifying significant trends, influential authors, and future research directions. Utilizing data from Scopus-indexed journals, we examined 529 papers published between 1994 and October 2023, identified through the keywords "visual basic," "visualbasic," "teaching," and "learning." These papers were analyzed using Biblioshiny to generate a bibliometric map, following four steps: data harvesting, data screening, data visualization, and data analysis. Our research reveals critical VB programming trends from 1994 to 2023, with academic output peaking in 2010 and declining since 2007. Ongoing interest is noted due to legacy system applications. Global publication reach facilitates cross-border information exchange, and top affiliations and authors underscore extensive and influential participation in this field. The research emphasizes incorporating fundamental and advanced themes in educational curricula. It suggests future research focusing on new programming paradigms, longitudinal studies, and VB relevance to technological advancements and industrial needs. Enhanced collaboration, interdisciplinary research, and attention to global trends are essential for maintaining the relevance of VB programming education, optimizing legacy systems, improving educational practices, and preparing students for modern programming environments.
Volume: 14
Issue: 6
Page: 6498-6507
Publish at: 2024-12-01
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