JOIV : International Journal on Informatics Visualization
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Extreme Gradient Boosting Algorithm to Improve Machine Learning Model Performance on Multiclass Imbalanced Dataset
Get PDFUnbalanced conditions in the dataset often become a real-world problem, especially in machine learning. Class imbalance in the dataset is a condition where the number of minority classes is much smaller than the majority class, or the number is insufficient. Machine learning models tend to recognize patterns in the majority class more than in the minority class. This problem is one of the most critical challenges in machine learning research, so several methods have been developed to overcome it. However, most of these methods only focus on binary datasets, so few methods still focus on multiclass datasets. Handling unbalanced multiclass is more complex than handling unbalanced binary because it involves more classes than binary class datasets. With these problems, we need an algorithm with features that can support adjustments to the difficulties that arise in multiclass unbalanced datasets. One of the algorithms that have features for adjustment is the ensemble algorithm, namely Xtreme Gradient Boosting. Based on the research, our proposed method with Xtreme Gradient Boosting showed better results than the other classification and ensemble algorithms on eight datasets with five evaluation metrics indicators such as balanced accuracy, the geometric-mean, multiclass area under the curve, true positive rate, and true negative rate. In future research, we suggest combining methods at the data level and Xtreme Gradient Boosting. With the performance increase in Xtreme Gradient Boosting, it can be a solution and reference in the case of handling multiclass imbalanced problems. Besides, we also recommended testing with datasets in the form of categorical and continuous data
Capturing User Experience of Customer-Centric Software Process through Requirement Process: Systematic Review
Get PDFAgile and User Experience have become popular for decades due to the ability to understand customer needs. However, both methods have different perspectives on the point of view, value, and quality. Moreover, user research in UX is usually conducted in the long term. The human aspect is a critical thing in Agile, the purpose of this aspect is to understand the value and need of the product, and with the user stories, several developers try to understand the human aspect of customers. In the elicitation process of the UX, developers used user stories to capture customer personality. One important factor is emotion; UX researchers measure emotions from the product journey, but it is unpleasant when the customer finds out the product does not meet expectations. This study aims to research the implementation of capturing emotion in user experience among Agile software development activities from several perspectives. In addition, Limited resources in software projects require innovation that can guarantee the sustainability and quality of the product. In this paper, we used modified systematic mapping to extract, classify, and interpret articles from popular publishers and map the user experience life cycle to answer several existing problems. This research shows that a combination of user requirement and UX increase the product's usability. Moreover, involving the user in the development center increases the project's success
Web-based E-learning in Elementary School: A Systematic Literature Review
Get PDFThis article presents literature review on web-based e-learning in elementary school in the latest literature. SLR method and PRISMA protocol with the stages of identification, screening, eligibility, inclusion, and abstraction, data analysis assisted by the Publish or Perish 7 application, VOSviewer, and NVIVO 12 Plus. The results of searching for articles on Scopus through the Publish or Perish 7 application are 507. Then the articles were filtered according to compatible themes into 50 articles. The topic findings are web-based e-learning, elementary school, the impact of web-based e-learning and web-based e-learning concept, academic performance, teaching/learning strategies, online learning, Covid-19, HPC database, web-based applications, distance learning, 3D visualization, automation, strategic learning, semantic web, technology, education, linguistic content, big data architecture, learning setting, e-readiness, linguistic content, STEM, etc., that are directly or indirectly connected. The 50 articles were analyzed according to the specified topics through the NVIVO 12 Plus application, and the results were described according to the research questions. The findings in this article explain that web-based e-learning integrates pedagogy and technology and becomes part of digital multimedia implemented in e-learning, blended learning, and face-to-face that impacts elementary school students and teachers directly or indirectly. Future research needs to explore web-based e-learning in schools that is current, safe, and needed by students and teachers
Verification of Ph.D. Certificate using QR Code on Blockchain Ethereum
Get PDFOne of the major challenges the university faces is to provide real-time verification of their student's degree certification upon request by other parties. Conventional verification systems are typically costly, time-consuming and bureaucratic against certificate credential misconduct. In addition, the forgery of graduation degree certificates has become more efficient due to easy-to-use scanning, editing, and printing technologies. Therefore, this research proposes verifying Ph.D. certificates using QR codes on the Ethereum blockchain to address certificate verification challenges. Blockchain technology ensures tamper-proof and decentralized management of degree certificates as the certificates stored on the blockchain are replicated across the network. The issuance of certificates requires the use of the issuer's private key, thus preventing forgery. The system was developed using Solidity for the smart contract, PHP, HTML/CSS for the web-based implementation, and MetaMask for blockchain integration. User testing confirmed the successful implementation and functionality of the system. Users can add, update, and delete certificates, generate and scan QR codes, and receive instant verification feedback. The verification system effectively meets all requirements, providing a robust solution for validating Ph.D. certificates. Future research may focus on scalability and adoption, privacy and data protection, user experience, and integration with existing systems. Other researchers can optimize the verification system for widespread adoption and utilization by exploring these areas. This research contributes to securing and efficiently verifying academic certificates using QR codes on the Ethereum blockchain. Ultimately, this work advances the field of certificate verification and promotes trust in academic credentials
K-Means Clustering Algorithm for Partitioning the Openness Levels of Open Government Data Portals
Get PDFMore and more local governments in Indonesia are making their data available to the public. This benefits data scientists, researchers, business owners, and other potential users seeking datasets for empirical research and business innovation. However, just because Open Government Data (OGD) portals are accessible does not mean that they necessarily adhere to the established rules and principles of data openness. To evaluate the level of openness of 24 OGD portals in Indonesia, this study used the K-means Clustering algorithm to partition them into three levels: Leaders, Followers, and Beginners. A group of 30 participants, including researchers, data scientists, business enablers, and graduate students, rated the portals on 32 sub-questions related to the eight main principles of data disclosure, focusing on health, population, and education datasets. The study found that eight portals were categorized as Leaders, ten as Followers, and seven as Beginners regarding their level of openness. The study demonstrated that the K-means Clustering algorithm can be effectively used to assess the degree of openness of OGD portals in Indonesia based on eight main principles of data openness. The study recommends increasing the number of OGD portals in eastern territories to supplement the existing case studies in the western and central regions
A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm
Get PDFA smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed leve
Implementation of Big Data Information System Using Open-Source Metabase for Civil Registration and Vital Statistics Data Visualization in Surabaya
Get PDFCivil registration involves the mandatory and continuous documentation of important life events of a country's population under local legal requirements. In many countries, these documents are required to access government services such as education, healthcare, social services, formal employment, insurance benefits, and inheritance rights. Indonesia should prioritize building a comprehensive Civil Registration and Vital Statistics (CRVS) using a big data information system to ensure every individual has a legal identity, can access government services, and collect accurate and reliable statistics on vital events through geospatial maps. Surabaya, a city in Indonesia, still needs a comprehensive Civil Registration and Vital Statistics (CRVS) system. We produce many informative visualizations from the query and modeling processes in Metabase. Based on the PIECES framework, this application's importance level is 4.56 or 91.25%, meaning the application is important, and the satisfaction level is 4.29 or 85.76%, meaning the application is satisfied for the respondents. This research provides a brief overview of how Metabase works and how it can be used to generate visualizations of job-type data. It demonstrates the ease with which visualizations can be changed and customized. It had a good affordability point, making its implementation easier and more beneficial. It also emphasizes the importance of having a powerful tool like Metabase for data analysis and decision-making, especially for the dispendukcapil as a civil registration agency
k-Means Cluster-based Random Undersampling and Meta-Learning Approach for Village Development Status Classification
Get PDFThere is a significant imbalanced class in the village development index (called IDM - Indeks Desa Membangun) dataset, marked by the number of self-supporting classes more than the disadvantaged class. The traditional classifiers are able to achieve high accuracy (ACC) by training all cases of the majority class but forsaking the minority class, so that possible for the classification results to be biased. In this study, a random under-sampling technique was employed based on k-means cluster (KMC) and a meta-learning approach to improving ACC of the village status classification model. Furthermore, the AdaBoost and Random Forest were used as meta technique and base learner, respectively. The proposed model has been evaluated using the area under the curve (AUC), and experimental results showed that it yielded excellent performance compared to the prior studies with the AUC, ACC, precision (PR), recall (RC), and g-mean (Gm) values of 95.50%, 95.52%, 95.5%, 95.5%, and 92.95%, respectively. Similarly, the result of the t-test also showed the proposed model yielded excellent performance compared to previous studies. It can be concluded that the AdaBoost algorithm improved misclassification and changed the distribution of data loss function in random forests. It indicates that the proposed model effectively deals with imbalanced classes in the village development status classification model.Â
Adopting the eGameFlow Model in an Educational Game to Increase Knowledge about Vaccination
Get PDFVaccination is one preventive measure to prevent oneself from getting any diseases. Vaccinations work by training our immune system to produce antibodies and weaken the targeted disease. Unfortunately, the number of unvaccinated children has increased because some parents reject or doubt the effectiveness of vaccines. This skepticism could result in a resurgence of vaccine-preventable diseases. Additionally, some contribute to vaccine refusal due to other reasons like vaccine misinformation, religious convictions, and insufficient knowledge. The game aims to develop knowledge and awareness to enhance vaccine behavior and acceptance among individuals. eGameFlow model were used as the methodology for game development. This model was chosen as it focuses on the educational game environment, which addresses learning components in the game. There are eight criteria to be considered to evaluate enjoyment using this model. To measure the users’ enjoyment, a set of questionnaires adopted from the eGameFlow model has been used for the evaluation. It is created explicitly to measure learners’ enjoyment of e-Learning games. This game gathers positive feedback from 30 respondents and shows promising results to achieve the objective. All eGameFlow criteria were positive towards enjoyment, with knowledge improvement being the highest contributor. The overall average of the evaluation was at an agreeable level, with a score of 81%, considered as achieving the goal. For future enhancement in increasing player’s enjoyment and game effectiveness, the game can be created in 3D environment to provide deep immersion and autonomy to the player
Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia
Get PDFCurrently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification