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Predicting Student's Soft Skills Based on Socio-Economical Factors: An Educational Data Mining Approach
Get PDFRecent changes in the labor market and higher education sector have made graduates' employability a priority for researchers, governments, and employers in developed and emerging nations. There is, however, still a dearth of study about whether graduate students acquire the employability skills that businesses want of them because of their higher education. To determine a student's future employment and career path, it is critical to evaluate their soft skills. An emerging area called educational data mining (EDM) aims to gather enormous volumes of academic data produced and maintained by educational institutions and to derive explicit and specific information from it. This paper aims to predict students' soft skills such as professional, analytical, linguistic, communication, and ethical skills, based on their socio-economic, academic, and institutional data by leveraging data mining methods and machine learning techniques. All five soft skills were predicted using prediction models created using linear regression, probabilistic neural networks, and simple regression tree techniques. This study used a dataset from an open source that Universidad Technologica de Bolivar published. It covers academic, social, and economic data for 12,411 students. The experimental results demonstrated that the linear regression algorithm performed better than the others in predicting all five soft skills compared to machine learning methods. This finding can assist higher education institutions in making informed decisions, providing tailored support, enhancing student success and employability, and continuously modifying their programs to meet the needs of students
Machine Learning-Based Fire Detection: A Comprehensive Review and Evaluation of Classification Models
Get PDFFires, regardless of their origin being natural events or human-induced, provide substantial economic and environmental hazards. Therefore, the development of efficient fire detection systems is of utmost importance. This study provides a comprehensive examination of the extant body of literature about studies on fire detection utilizing machine learning techniques. Significantly, the studies employed three distinct categories of datasets: pictures, data derived from Wireless Sensor Networks (WSNs), or a hybrid amalgamation of both. Our work mainly aims to categorize fire-related data utilizing four distinct classification models: Support Vector Machines (SVMs), Decision Trees, Logistic Regression, and Multi-Layer Perceptron (MLP). The model with the highest accuracy and ROC curve performance was identified through experimental analysis. The results of our study indicate that the MLP model exhibits the highest overall accuracy, achieving a score of 0.997. In this study, we analyze the learning curves to showcase the positive training dynamics of our model. Additionally, we explore the scalability of our model to ensure its suitability in real-world situations. In general, our research underscores the possibility of employing machine learning methodologies for fire detection, specifically emphasizing the effectiveness of the Multilayer Perceptron (MLP) model. This study contributes to the existing literature by offering valuable insights into the performance of several categorization models and conducting a comprehensive investigation of the Multilayer Perceptron (MLP) architecture. The results of our study have the potential to contribute to the advancement of fire detection systems, leading to enhanced accuracy and efficiency. This, in turn, may mitigate the adverse impacts of fires on both society and the environment
Omni-Channel Service Analysis of Purchase Intention
Get PDFThe COVID-19 pandemic has caused a decline in various aspects of the economy, including the fashion sector. Many fashion retailers have closed, so sales have fallen. However, many retailers can also adapt and change using new communication channels. This change presents new challenges for fashion companies and retailers to integrate channels into omnichannel services. This study aims to analyze the factors influencing customer behavior in omnichannel services through their intention to accept and use new technology in shopping. This study adopts the UTAUT2 model by adding two new variables: personal innovation and perceived security. This model was tested on 353 samples from Uniqlo customers residing in Indonesia. This research method uses a Quantitative PLS-SEM approach. This study tested the outer model, inner model, and hypothesis t-test with a bootstrap procedure using SmartPLS software. The results showed that the performance expectation factor did not affect the omnichannel purchase intention variable because the t-statistic value is less than 1.65. Meanwhile, other factors such as effort expectation, social influences, habits, hedonic motivation, perceived security, and personal innovativeness affect omnichannel purchase intentions because the t-statistic value is more than 1.65. The most positive and significant factor is personal innovativeness. Based on the results of this study, it is revealed that digitalization creates challenges for companies in maintaining digital businesses. Through various omnichannel service channels, this research can identify the factors influencing consumers' purchase intentio
433Mhz based Robot using PID (Proportional Integral Derivative) for Precise Facing Direction
Get PDFThis research endeavor aims to evaluate the effectiveness of the robot's direction control system by employing PID (Proportional Integral Derivative) output and utilizing wireless communication LoRa E32 433MHz. The experimental robot used in this study was a tank model robot equipped with 4 channels of control. LoRa was implemented in the robot control system, in conjunction with an Android control application, through serial data communication. The LoRa E32 module system was selected based on its established reliability in long-range communication applications. However, encountered challenges included the sluggishness of data transmission when using LoRa for transferring control data and the decreased performance of the robot under Non-Line of Sight conditions. To overcome these challenges, the PID method was employed to generate control data for the robot, thereby minimizing the error associated with controlling its movements. The PID system utilized feedback from a compass sensor (HMC5883L) to evaluate the setpoint data transmitted by the user, employing Kp, Ki, and Kd in calculations to enable smooth movements toward the setpoint. The findings of this study regarding the direct control of the robot using wireless LoRa E32 communication demonstrated an error range of 0.6% to 13.34%. A trial-and-error approach for control variables determined the optimal values for Kp, Ki, and Kd as 10, 0.1, and 1.5, respectively. Future investigations can integrate additional methodologies to precisely and accurately determine the PID constants (Kp, Ki, and Kd) mathematically
Design and Development of Sound and Rhythm Perception Assessment Application for Students with Hearing Impairment
Get PDFTechnology use is becoming increasingly popular in life, including in educational aspects. Some widely used applications in education include metaverse, blended learning, game learning, cloud-based learning, mobile applications, and social media learning. Apps are generally in the form of software applications or programs designed to run on smartphones. In this study, we propose using applications in assessing children with hearing impairments at school. Design and Development of the Sound and Rhythm Perception Assessment Application uses the ADDIE development model of Analysis, Design, Development, Implementation, and Evaluation. The test subjects in this study were validation test subjects consisting of 3 experts to test the feasibility of the application. Data was collected through a questionnaire in the form of a tool tested for validity and reliability with a score of 90.1% for learning design, 88.9% for layout, and 94.7% for software. Validation was carried out through focus group discussions. The application was tested on four teachers who teach students with hearing impairments. The results of the main field experiment show that teachers can use the application to help them assess students with hearing loss. With availability, the accuracy of the Design and Development of the Sound and Rhythm Perception Assessment Application can be further improved by conducting training with more teachers who teach children with hearing impairments at school
Drowsiness Detection System Through Eye and Mouth Analysis
Get PDFTraffic jams are one of the serious issues in many developed countries. After the pandemic, many employees were allowed to travel interstate to work. This contributes to more severe jams, especially in the capital and nearby states. Long-distance driving and congestion can easily make the drivers sleepy and thus lead to traffic accidents. This paper aims to study and analyze facial cues to detect early symptoms of drowsy driving. The proposed method employs a deep learning approach, utilizing ensemble CNNs and Dlib's 68 landmark face detectors to analyze the facial cues. The analyzed symptoms include the frequency of eyes opened or closed and yawning or no yawning. Three individual CNN models and an ensemble CNN structure are built for the classification of the eyes and mouth yawn. The model training and validation accuracy graph and training loss and validation loss graph are plotted to verify that the models have not been overfitted. The ensemble CNN models achieved an approximate accuracy of 97.4% from the eyes and 96.5% from the mouth. It outperforms the other pre-trained models. The proposed system can immediately alert the driver and send text drowsy messages and emails to the third party, ensuring timely intervention to prevent accidents. The proposed method can be integrated into vehicles and transportation systems to ensure driver's safety. It can also be applied to monitor the driving behavior of those who drive long distance
Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data
Get PDFDiabetic Retinopathy (DR) is a complication of diabetes mellitus, which attacks the eyes and often leads to blindness. The number of DR patients is significantly increasing because some people with diabetes are not aware that they have been affected by complications due to chronic diabetes. Some patients complain that the diagnostic process takes a long time and is expensive. So, it is necessary to do early detection automatically using Computer-Aided Diagnosis (CAD). The DR classification process based on these several classes has several steps: preprocessing and classification. Preprocessing consists of resizing and augmenting data, while in the classification process, CSVM method is used. The CSVM method is a combination of CNN and SVM methods so that the feature extraction and classification processes become a single unit. In the CSVM process, the first stage is extracting convolutional features using the existing architecture on CNN. CSVM could overcome the shortcomings of CNN in terms of training time. CSVM succeeded in accelerating the learning process and did not reduce the accuracy of CNN's results in 2 class, 3 class, and 5 class experiments. The best result achieved was at 2 class classification using CSVM with data augmentation which had an accuracy of 98.76% with a time of 8 seconds. On the contrary, CNN with data augmentation only obtained an accuracy of 86.15% with a time of 810 minutes 14 seconds. It can be concluded that CSVM was faster than CNN, and the accuracy obtained was also better to classify DR
Digital Literacy toward Historical Knowledge: Implementation of the Bukittinggi City History Website as an Educational Technology
Get PDFThis research aims to identify the challenges and opportunities in integrating digital literacy skills into history education. This research focuses on understanding educators' difficulties in integrating digital technologies into the traditional history curriculum. Educational technology as a means and facility to support education and learning is no exception for historical knowledge through access to historical websites. This study analyses digital literacy toward historical knowledge using the Bukittinggi City history website. This research is quantitative research with a survey approach with closed-ended questions. The research population is the millennial generation in Indonesia. Samples were taken with a non-probability sampling approach with purposive sampling. This study involved 831 respondents spread throughout Indonesia. The data analysis technique is partial Least Square Structural Equation Modelling (PLS-SEM). The results showed no difference in historical knowledge scores between males and girls. With a value of 0.697 and a 69.7% variance, the coefficient of determination (R2) result demonstrates significant volatility in historical knowledge. Additionally, Q2's value serves as a gauge for the model's predictive usefulness. The predictive relevance of the model's independent variables was assessed using the predictive relevance test (Q2). Men might be more adept at using online resources to broaden their knowledge of the city's past. Understanding the disparities in digital literacy between men and women will significantly impact the design of educational and literacy programs in Bukittinggi. Enhancing digital literacy can promote access to and understanding of the city's history, especially among wome
AI Educational Mobile App using Deep Learning Approach
Get PDFMoving to Industrial Revolution (IR 4.0), the early education sector is not left behind. More of the teaching method is being digitized into a mobile application to assist and enhance the children’s understanding. On the other hand, most of the applications offer passive learning, in which the children complete the activity without interacting with the environment. This study presents an educational mobile application that uses a deep learning approach for interactive learning to enhance English and Arabic vocabulary. Android Studio software and Tensorflow tool were used for this application development. The convolution neural network (CNN) approach was used to classify the item of each category of vocab through image recognition. More than thousands of images each time were pre-trained for image classification. The application will pronounce the requested item. Then, the children will need to move around looking for the item. Once the item’s found, the children must capture the image through the camera’s phone for image detection. This approach can be integrated with teaching and learning techniques for fun learning through interactive smartphone applications. This study attained high accuracy of more than 90% for image classification. In addition, it helps to attract the children's interest during the teaching using the current technology but with the concept of ‘Play’ and ‘Learn’. In the future, this paper recommended the involvement of IoT platforms to provide widen applications
Factors Influencing Readiness towards Halal Logistics among Food and Beverages Industry in the Era of E-Commerce in Indonesia
Get PDFBased on Global Islamic Economy Indicator 2019/2020 report, Indonesia is in the fourth position globally as a country that uses a Sharia economic system. Seeing Indonesia's opportunities, it should be able to act as a regional and global halal hub. Efforts to encourage the halal industry through strengthening the halal value chain are one of the strategies to encourage Indonesia to become a global halal hub player. This study utilizes the structural equation modeling to examine relationships among key factors affecting readiness towards halal logistics in the food and beverages industry in Indonesia. 13 key factors are confirmed with measurement-model results, including (1) Cleanliness, (2) Safety, (3) Islamic Dietary Law, (4) Physical Segregation, (5) Material Handlings, (6) Storage and Transport, (7) Packaging and Labelling, (8) Ethical Practices, (9) Training and Personnel, (10) Resource Availability, (11) Innovative Capability, (12) Marketing Performance, (13) Financial Performance. The population in this study is in the food and beverage industries, especially in Semarang, Yogyakarta, Malang, and Surabaya. Cluster random sampling was used in this research with as many as 150 sample respondents. A survey with an online questionnaire was conducted in this research. The structural-model results reveal directions of relationships among key factors. Resource availability, training and personnel, and innovative capability are the most important factor in halal supply chain readiness. Further research can focus on other industrial sectors, such as fashion and tourism, as stated in the 2019-2024 Indonesian Sharia Economic Masterpla