International Journal of Informatics and Communication Technology (IJ-ICT)
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Remote practical instruction using web browsers
Get PDFThis paper introduces a novel approach to remote coaching, specifically targeting the body movements of learners participating remotely. The proposed system employs a smartphone camera to capture the learner’s body and represent it as a 3D avatar. The instructor can then offer guidance and instruction by manipulating the 3D avatar’s shape, which is displayed on a web browser. The main challenge faced by the system is to enable the sharing and editing of 3D objects among users. Since the HTML5 drag-and-drop feature is inadequate for transforming virtual objects consisting of multiple interconnected rigid bodies, the system tracks the pivot point of the manipulated rigid body. It assigns attributes such as pivot points and action points to each object, extending beyond their 2D screen coordinates. To implement the system, an interactive web application framework following the model-view-view-model (MVVM) architecture is utilized, incorporating Vue.js, Three.js, and Google Firebase. The prototype system takes advantage of the data binding capability of the framework and successfully operates within the 3D space of a web browser. Experimental results demonstrate that it can effectively share transformation information with an average delay of 300 ms
Utilization of the use of technological devices in delivering communication information in the learning process
Get PDFThe development of increasingly sophisticated technology today brings education to participate in using the features available in today’s media such as the transition from face-to-face learning communication in schools to face-to-face assisted by technology such as laptops, tablets, cellphones and other multimedia. Technology currently provides innovation in the learning process both from home and from school. However, there are still many teachers and students who have not utilized technology such as laptops, tablets, cellphones and other multimedia in the learning process. The purpose of the study was to analyze the benefits and relationships of the four main variables of communication assessment elements with digital devices. The research method used was quantitative with a sample of 148 teachers randomly selected from schools that use technology in the learning process. Data collection techniques with instruments. The instruments used were four indicator instruments, namely technology from laptops, tablets, cellphones and other multimedia. Data analysis techniques with descriptive statistics using SPSS version 26.0 calculated the mean, standard deviation, and correlation test. The results of the study found that the four indicators had high reliability and the four indicators had significant utilization, were mutually positive and had a high relationship with each other. The conclusion is that the four technological devices are good for use in digital communication during the learning process and laptops and tablets are more recommended in this study
Autism detection based on autism spectrum quotient using weighted average ensemble method
Get PDFAutism spectrum disorder (ASD) is a condition that occurs in an individual, wherein it is accompanied by various symptoms such as difficulties in socializing with others. Early detection of ASD patients can assist in preventing various symptoms caused by ASD. The focus of this research is to automate the diagnosis of ASD in an individual based on the results of the autism spectrum quotient (AQ) using weighted average ensemble method. Initially, preprocessing is carried out on the dataset to ensure optimal performance of the resulting model. In the preprocessing step, the filling of missing values and feature selection occurs, where the feature selection method being utilized is p-value. The model in this research uses the weighted average ensemble method, which is the model that combines three machine learning classification algorithms. Eight classification algorithms are tested to identify the three algorithms with the best performance, namely gaussian Naïve Bayes (NB), logistic regression (LR), and random forest (RF). Following the testing, the model constructed using the weighted average ensemble method exhibits the highest performance compared to the model built using a single classification algorithm. The performance matrix used to measure the model’s performance is area under the curve (AUC)/receiver operating characteristic (ROC), with the developed model achieving an AUC/ROC value of 0.912
Transformer-based abstractive indonesian text summarization
Get PDFThe volume of data created, captured, copied, and consumed worldwide has increased from 2 zettabytes in 2010 to over 97 zettabytes in 2020, with an estimation of 181 zettabytes in 2025. Automatic text summarization (ATS) will ease giving points of information and will increase efficiency at the time consumed to understand the information. Therefore, improving ATS performance in summarizing news articles is the goal of this paper. This work will fine-tune the BART model using IndoSum, Liputan6, and Liputan6 augmented dataset for abstractive summarization. Data augmentation for Liputan6 will be augmented with the ChatGPT method. This work will also use r ecall-oriented understudy of gisting evaluation (ROUGE) as an evaluation metric. The data augmentation with ChatGPT used 10% of the clean news article from the Liputan6 training dataset and ChatGPT generated the abstractive summary based on that input, culminating in over 36 thousand data for the model’s fine-tuning. BART model that was finetuned using Indosum, Liputan6, and augmented Liputan6 dataset has the best ROUGE-2 score, outperforming ORACLE’s model although ORACLE still has the best ROUGE-1 and ROUGE-L score. This concludes that fine-tuning the BART model with multiple datasets will increase the performance of the model to do abstractive summarization tasks
Optimizing warehouse management system with blockchain and machine learning predictive data analytics
Get PDFBlockchain technology is proving to be a disruptive technology in many areas of supply chain, manufacturing, medical, agriculture, and so on. Warehouses are an inevitable part of the supply chain. Issues like space optimization, route optimization, quick item pick-up, demand forecasting, and transaction management are of importance to address in warehouse management systems (WMS). Traditional database systems have limitations of interoperability among different entities involved in warehouses. This paper presents an innovative application of blockchain technology and machine learning (ML) to build a smart warehouse management system in Web3 (SWMW3). We developed a decentralized application (DApp) using Web3.0 principles, integrating ReactJS for the frontend, express for the backend, and blockchain through smart contracts. This integration enhances security and transparency by storing WMS operational data in the blockchain and automating payments and verifications through smart contracts. Additionally, we implemented a ML model for predicting the total time from order receipt to delivery, leveraging historical data to optimize workflow, reduce delays, and improve overall efficiency. This combination of blockchain for secure transactions and ML for predictive analytics generates a robust, efficient, and optimized management system for the warehouse
Implementing gamification in campus canteen using MDA framework: an overview
Get PDFThis study describes the creation of a mobile-based gamification design for an online canteen system. Long lunch queues make students feel uncomfortable ordering food in the canteen, although student comfort is very important. The increasing number of students causes long queues, resulting in significantly shorter lunch hours and causing discomfort for students. To address this issue, the campus might develop an online canteen using gamification. Gamification is a method that applies gaming knowledge to create experiences that encourage and engage people in non-game environment. Compared to traditional canteen systems, an online canteen that uses gamification can provide students with new experiences by offering attractive rewards, increasing motivation to order food and beverages online, and minimizing the perception of long lineups. Although this proposed design has not yet been tested, researchers believe it has practical applications
Blockchain and ML in land registries a transformative alliance
Get PDFThis study presents a novel method for merging blockchain security and machine learning (ML) valuation to update land register systems. The system offers a safe, open, and effective framework for documenting and managing land ownership, addressing issues with conventional land registry procedures. Blockchain technology creates a tamper-proof record by cryptographically combining transactions and time-stamped entries to provide an immutable and decentralized ledger. In addition to building a solid foundation for the land registry system, this strengthens trust. Simultaneously, ML algorithms examine variables such as amenities and location to remove inflated pricing, providing accurate assessments and encouraging openness in the real estate sector. The system has been put into practice and verified in small-scale applications. Its features include enhanced data security, expedited ownership transfers, and accurate asset appraisals. Collaboration between governments, regulatory agencies, and technology suppliers is necessary for widespread deployment. Land registration procedures will change as a result of the revolutionary partnership between blockchain and ML technology, which offers a more effective, safe, and future-ready environment. Accepting this ground-breaking technique establishes a new benchmark for the updating of land ownership data and is a major step toward a more sophisticated and dependable method in the industry
Predicting rainfall runoff in Southern Nigeria using a fused hybrid deep learning ensemble
Get PDFRainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn impact the quality of life, sewage systems, agriculture, and tourism of a nation to mention a few. It chaotic, complex, and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models, many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999-2019 for the Benin-Owena River Basin. Data is split: 70% for train and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide lead time warnings in flood management
Face recognition using haar cascade classifier and FaceNet (A case study: Student attendance system)
Get PDFFace recognition is increasingly widely utilised, and there are numerous face recognition systems. Face recognition is typically utilised for attendance on e-learning platforms in the field of education. The haar cascade classifier is one method for face identification; it is used to identify facial areas. Faces are classified using an alternative model, FaceNet. In this research, we purposefully designed an e-learning platform that authenticates students based on face recognition. Based on the findings of this investigation, the system can accurately recognise faces. Ten students were evaluated based on their participation in two attendance trials. Successful presence has an achievement success value of 19, and 1 failed out of a total of 20 attempts. Several variables, such as illumination, and the use of marks on hats, that could have influenced attendance caused the experiment to fail
Blockout 2024: digital mobilization movements’ role in raising global awareness and fostering change
Get PDFBlockout 2024, a social media mobilization campaign, gained traction in response to celebrities’ and powerful people’s silence following the Met Gala in New York. Users who have remained silent or unconcerned about the humanitarian situation in Gaza are encouraged to block influencers’ accounts. The review seeks to investigate the Blockout 2024 phenomenon and how it affects celebrities’ social media. This review examines the impact of social media on power dynamics. On social media platforms such as TikTok, Instagram, and X, users have blocked the accounts (especially celebrities’ account) of those who have not responded to the humanitarian disaster. The movement emphasizes the importance of implementing change and making underrepresented voices heard in digital environments. While some celebrities have expressed their support, others have chosen to remain silent, which has resulted in criticism and lost followers. Finally, the Blockout 2024 campaign has gained significant traction on social media platforms such as X, Instagram, and TikTok. Users of social media are becoming more aware of their responsibility to denounce crimes and fight for justice, as evidenced by this group effort. The Blockout 2024 movement has highlighted the potential of digital mobilization to raise global awareness, address humanitarian crises, and hold influencers accountable