JOIV : International Journal on Informatics Visualization
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Smart Contract and IPFS Decentralized Storage for Halal Certification Process
Get PDFThe halal industry today has achieved rapid development. Halal product is mandatory for Muslims and a big business for Indonesia. For others, it affirms the product's quality assurance and becomes a trending lifestyle. The product owner must submit an application and undergo several processes to obtain a halal certificate. However, there are challenges in the certification process and documentation. The proposed system automates the flow between certification processes through digital signing and stores the certificate and fatwa file. The study investigates the utilization of blockchain to manage the process and the integration of decentralized storage (IPFS) to store the digital version of the fatwa and certificate. A smart contract is designed and deployed on the Ethereum blockchain, and the transaction time and cost are analyzed. A smart contract enforces that certain actions are executed once the required conditions are fulfilled. The proposed system would cost 24.6 USD and require 227 seconds on average for the system setup. Each submission requires 9.86 USD and takes 92 seconds on average. Verification is free, and the average result can be obtained in one second. The appointed officer sets each entity to interact with the contract, and the digital documents (fatwa and certificate) are available online using IPFS. Progress of the certification is transparent to the public, increasing the public's trust. The study demonstrates a smart contract's capability to manage a product's certification process
The E-govqual and Importance Performance Analysis (IPA) Models Analysis: Review a Web Service Quality of E-government
Get PDFThe e-government web portal serves as a crucial platform for providing information that can be easily and universally accessed. This serves as an intermediary between the municipal administration and the community, ultimately resulting in improved and streamlined public services. Several variables, including age and proficiency in using the integrated system, provide insights on how to gauge user satisfaction levels of the information system and its current users. The e-govqual and Importance Performance Analysis (IPA) models are accurate indicators of user satisfaction. This article seeks to understand the perception of users of the e-government web portal of the municipal Tanah Datar municipality. It aims to compare the servqual and the IPA model to determine the most suitable method for assessing public perceptions and identifying priority attributes to improve service quality. These two approaches share the same objective, but employ different methodologies. The user's perception of performance is designated as the independent variable (X) using a quantitative approach, while service quality expectations are designated as the dependent variable (Y). This is achieved by combining the Likert scale with five dimensions. This study uses questionnaires to gather data from 275 participants and uses two models, E-govqual and Importance Performance Analysis (IPA), to assess user satisfaction. The findings indicate that it is crucial for the government to respond quickly to user issues, provide feedback on user input, and regularly update the material on the Web portal
Atomic Structure Simulation and Properties’ Prediction using Machine Learning on Neodymium Oxide Nanoparticles Zinc Tellurite Glasses Aided by FTIR and TEM Analysis
Get PDFThe optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Function (PDF) and Monte Carlo simulations and visualisation for precise molecule distribution representation, an intuitive Python interface was created to emphasize these features. The density increased with increasing Nd2O3 concentrations, from 5346 to 5606 kg/cm2. Density data was used to infer the molar volume. The best projected density was achieved by the Gradient Boosting Regressor model, with a R2 of 0.9988 and an RMSE of 0.0032; the best predicted molar volume was achieved by linear regression, with a R2 of 1 and an RMSE of 2.67e-15. These models successfully represent the correlations between dopant concentration and glass properties, advancing our knowledge of the optical properties for further glass technology research
Remote Laboratory Based on the Internet of Things for E-Learning: A Development Model of Newton’s Law Experiment
Get PDFRemote laboratory is a development of digital technology to support the quality of learning in this digital era. However, scientific processes often cannot be accommodated in digital spaces such as e-learning. This research highlights a remote laboratory system that can accommodate scientific process improvement in e-learning. The research objective is to develop and determine the performance of the remote laboratory system of Newton’s Law experiment based on IoT for e-learning as an experiment development model. Research methods can be classified into design and development, abbreviated as DDR. The remote laboratory system is designed and developed in six phases. This system is developed by five main components, namely, a photodiode sensor, MCU nodes, motor drivers, stepper motor, and ESP 32 CAM. The results indicate that the remote laboratory system of Newton's law experiment has demonstrated positive performance, and the accuracy and precision of measurement from the remote laboratory system are classified as high. Accordingly, the remote laboratory system of Newton's law experiment can be used as an alternative to support scientific processes in e-learning. It is expected to serve as a guide for virtual laboratory design, enlightening the audience on the potential of this system. It is used extensively for experimental teaching in modern physics education. The success in designing and developing an experimental model of Newton's law by implementing a remote laboratory based on IoT provides a good opportunity to develop various more sophisticated physics experimental systems to support the science process and e-learning
Technologies on Intelligent Financial Risk Early Warning in Higher Education Institutions: A Systematic Review
Get PDFFinancial risk early warning (FREW) is critical for developing Higher Educational Institutions (HEIs). This review uses the Systematic Literature Review (SLR) method to discuss the current research status, leading causes, early warning techniques, and algorithms of financial risk management in HEIs. Based on the WoS database, 139 articles meeting the research criteria were selected from 451 relevant literature for in-depth analysis. The results show that the current research on financial risk management in HEIs mainly focuses on developing risk identification, assessment, and early warning models. The primary sources of university financial risk include the instability of fundraising and distribution, decreased financial allocation, and intensified market competition. In response to these risks, scholars have proposed various early warning models and technologies, such as univariate, multivariable, and artificial neural network models, to predict and manage these risks better. In terms of methodology, this review provides a comprehensive perspective on the study of university financial risk through quantitative and qualitative analysis. This study reveals this field's main research trends and gaps through literature screening and cluster analysis. Finally, this study discusses the practical significance of financial risk management in HEIs, highlighting its role in the stability and growth of these institutions. It suggests future research directions, especially in improving the accuracy and applicability of the Early Warning System (EWS), to further enhance the financial stability of HEIs. This literature review has crucial theoretical value for the academic community and provides practical guidance for HEI administrators
Predicting and Explaining Customer Response to Upselling in Telecommunications: A Malaysian Case Study
Get PDFThis research explores the predictive capabilities of XGBoost (XGB) and Random Forest (RF) models for customer upsell responses, emphasizing the use of Explainable Artificial Intelligence (XAI) techniques to gain insights. Initially trained without hyperparameter tuning, both models were later optimized using 5-fold cross-validation. While RF consistently achieved high accuracy (0.99), XGB exhibited lower accuracy (0.85) yet demonstrated superior precision and recall. Post-tuning, XGB maintained its competitive edge despite a slight decrease in ROC-AUC scores (0.76 and 0.75 versus RF's 0.67 and 0.72), indicating proficiency in classifying positive cases. XAI techniques complemented XGB’s prediction, revealing significant predictors such as inactive duration in days, race (Chinese), total communication count, age, and active period in days. Lesser predictive value was attributed to factors such as race (Indian), gender (female), and region (northern). While the feature importance plot provided a broad overview, it did not detail specific attribute relationships to predictions. To address this, a summary violin plot was employed to illustrate how feature importance varies with actual values, enhancing the understanding of each feature's impact. Results indicated that longer inactivity periods negatively influenced predictions, while non-Chinese ethnicity, higher communication frequency, and younger age were associated with positive outcomes. Dependence plots further elucidated these relationships, highlighting how older non-Chinese customers and those with shorter inactive periods and frequent communication were more likely to accept offers. Local explanations using Shapley's force plot and LIME offered deeper insights into specific instances. Overall, the study underscores the complementary use of XAI techniques to understand a model’s predictions
Traffic Violation Detection Using Computer Vision Techniques
Get PDFThe increasing number of road accidents is still a global concern. Traditional approaches to detecting traffic violators on the road, such as radar guns and sensors, are expensive and time-consuming to maintain and install. This often results in inefficient and ineffective detection of traffic violators. This paper proposes a more cost-effective and efficient approach to traffic violation detection utilizing visual data from CCTV footage. Specifically, the method targets two common violations: crossing red lights and overtaking on double lines. In this study, YOLO is integrated for road object detection, providing the detection of vehicles and traffic lights on the road for our system. Then, the Deep SORT tracker tracks detected vehicles, ensuring continuous monitoring over time. An automated lane detection technique is formulated to identify the stopping line/lane for red light violation detection, enabling precise detection of vehicles that cross the stop lane during red light. For overtaking detection, the system detects the double line to serve as the boundary that vehicles should not cross, identifying illegal overtaking. Furthermore, point-line distance calculation is utilized to detect traffic violators by analyzing their tracked trajectories and positions. The proposed solution is evaluated using real-world CCTV footage from online repositories to reflect the real-world scenarios as closely as possible. Experimental results show that the proposed techniques achieve promising detection of real-time traffic violators, which leads to a safer environment for road users
Features, Analysis Techniques, and Detection Methods of Cryptojacking Malware: A Survey
Get PDFVarious types of malwares are capable of bringing harm to users. The list of types are root exploits, botnets, trojans, spyware, worms, viruses, ransomware, and cryptojacking. Cryptojacking is a significant proportion of cyberattacks in which exploiters mine cryptocurrencies using the victim’s devices, for instance, smartphones, tablets, servers, or computers. It is also defined as the illegal utilization of victim resources (CPU, RAM, and GPU) to mine cryptocurrencies without detection. The purpose of cryptojacking, along with numerous other forms of cybercrime, is monetary gain. Furthermore, it also intended to stay concealed from the victim's viewpoint. Following this crime, to the author's knowledge, a paper focusing solely on a review of cryptojacking research is still unavailable. This paper presents cryptojacking detection information to address this deficiency, including methods, detection, analysis techniques, and features. As cryptojacking malware is a type that executes its activities using the network, most of the analysis and features fall into dynamic activities. However, static analysis is also included in the security researcher’s option. The codes that are involved are opcode and JavaScript. This demonstrates that these two languages are vital programming languages to focus on to detect cryptojacking. Moreover, the researchers also begin to adopt deep learning in their experiments to detect cryptojacking malware. This paper also examines potential future developments in the detection of cryptojacking
Measuring Score of Ethnic Tolerance Index among Peacekeepers using a MyETI System Dashboard
Get PDFIn post-conflict nations, the state-building process might take up to ten years to provide positive outcomes. This scenario has become increasingly challenging due to the escalation of intolerance among different ethnic groups, leading to incidents of communal violence in the aftermath of the war. Therefore, it is imperative to have ethnic tolerance and cultural understanding in peacekeeping operations that occur in a more intricate setting. The presence of ethnic tolerance among peacekeepers is crucial for ensuring the efficacy of peacekeeping missions. The increase of intolerant perspectives often causes the beginning of ethnic conflicts in multi-ethnic societies. Therefore, the main objective of peacekeepers deployed in these countries is to reinstate peace and security. This study proposes employing an online analysis to assess the ethnic tolerance index among peacekeepers accurately. The suggested method entails collecting and analyzing real-time survey data via a MyETI system dashboard, which may precisely evaluate the ethnic tolerance index score among Malaysian individuals. The MyETI e-survey has 103 questionnaires organized into four main categories: ethnic cross-relationships, governance, ethnic tolerance, and religious beliefs. To achieve the study's goal, 103 Malaysian peacekeepers who have previously been deployed to different United Nations Peacekeeping Operations (UNPKO) will be requested to answer the questions using the MyETI dashboard. The results could enhance the ethical guidelines for cultural competence, prioritizing understanding ethnic tolerance in peacekeeping operations or deployments
Flexible Semantic Qur’an Question Answering Using Graph-Based Summarization and KNN
Get PDFResearchers in the computer science field have been attracted by Qur'an-based research. This research area focuses on representing the ontology-based Qur'an. A semantic-based search will be beneficial in extracting information from the Quran, which has complex knowledge and language. This work aims to develop flexible semantic Qur'an question-answering by applying graph-based summarization and K-nearest neighbors (KNN) methods to add flexibility to semantic-based searches in the Indonesian Language. Meanwhile, the Qur'an is based on a unique Arabic language. It is a part of the complexity of this work. The graph-based summarization method effectively summarizes a complex question. It was proved by ROUGE testing with F1, precision, and recall scores of 72%, 62%, and 72%, respectively. The KNN method evaluated by the expert resulted in an average approval percentage on the 1st, second, and third topics of 62.11%, 66.15%, and 19.61%. As for other issues related to the questions, 70% needs to be displayed. The analysis of the obtained result indicates that the classification step needs to be improved in the tiny dataset. This work will contribute to Qur'an Question Answering as it is considered that the Qur'an is a different object compared to the other content of Question Answering. The Qur'an is an object that contains a massive amount of multi-interpretation. Lots more work in the future. The dataset is also limited by the scope of the theme of this research, which is only the pillars of Islam, so many topics still need to be included in the datase