JOIV : International Journal on Informatics Visualization
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Improving Data Reliability Assessment in ETL Processes through Quality Scoring Technique in Data Analytics
Get PDFThe foundation of a relevant and accurate data analysis is reliable data. Technique and measurement are essential to evaluate current data quality regarding reliability and establish a baseline for ongoing improvement initiatives. Without tools or visualizations, data engineers may find it challenging to monitor and maintain the reliability of the massive data from the extraction, transformation, and loading (ETL) data load process. Data reliability assessment is a helpful technique in analyzing the quality of data reliability and information on the present state of data before commencing any analytics. The proposed technique hinges on the metric and measurement defining data reliability and the dashboard platform where the integration with the user in dictating the weight of data and the final output, which is the final data reliability score, will be projected. The score obtained affirms whether improvements are needed on the data or if an organization can proceed with data analytics. The technique considers the data extraction, transformation, and loading (ETL) procedures used to gather datasets. Data significance or weight was determined according to the analytics needs and preferences, indicating an acceptable score for generating insights. Ultimately, when utilizing the data reliability assessment metrics technique, we are credited with an overall picture of our data’s reliability aspect, as only one look is offered based on the intended analysis. This new approach boosts the confidence among data practitioners and stakeholders, especially those relying on findings generated from data analysis. Furthermore, the overview assists in enhancing the current state of data, where the derived score helps identify possible areas of improvement in the ETL process. Accuracy and efficiency assessment of the proposed technique also showed positive feedback in measuring the method in measuring the reliability of data
Building Historical Narratives: The Development of Virtual Reality Learning Media for Exploring Historical Sources Bung Hatta's Birthplace
Get PDFThe shortcomings in the history of education, constrained by conventional teaching approaches, inadequate utilization of historical sites, and logistical hurdles, diminish student engagement and comprehension. This study aims to develop a virtual reality (VR) learning medium around Bung Hatta's birth house, aiming to enhance students' historical understanding through captivating and immersive experiences. We adapted the Borg and Gall development model into four phases: preliminary study, strategic planning, development, and validation. We utilized 360-degree pictures in conjunction with extensive historical data regarding Bung Hatta's birthplace, integrating these elements into an immersive virtual reality environment. We used expert validation to guarantee material accuracy, subsequently doing practical testing with history educators and students to evaluate usability and overall effectiveness. The findings revealed a practicality score of 87%, highlighting the substantial influence of VR media on enhancing student motivation and understanding of historical material. This technology facilitates the virtual investigation of significant historical locations, effectively surmounting geographical and financial barriers and enhancing accessibility, engagement, and the relevance of history for learners. This method enhances critical thinking and deepens appreciation of national heritage while offering a strong framework for incorporating VR into educational curricula. Further research must examine the long-term impacts of VR on various learning outcomes and its relevance across the educational curriculum. Moreover, integrating VR technology in diverse historical disciplines may augment its effectiveness and applicability in education. Virtual reality techniques improve students' understanding of history while strengthening 21st-century skills such as critical thinking and creativit
Serial Multimodal Biometrics Authentication and Liveness Detection Using Speech Recognition with Normalized Longest Word Subsequence Method
Get PDFBiometric authentication aims to verify whether an entity matches the claimed identity based on biometric data. Despite its advantages, vulnerabilities, particularly those related to spoofing, still exist. Efforts to mitigate these vulnerabilities include multimodal approaches and liveness detection. However, these strategies may potentially increase resource requirements in the authentication process. This paper proposes a multimodal authentication process incorporating voice and facial recognition, with liveness detection applied to voice data using speech recognition. This paper introduces Normalized Longest Word Subsequence (NLWS), a combination of Intersection Over Union (IOU) and the longest common subsequence, to compare the prompted system sentence with the user's spoken sentence at speech recognition. Unlike the Word Error Rate (WER), NLWS has a measurable range between 1 and 0. Furthermore, the paper introduces decision-level fusion in the multimodal approach, employing two threshold levels in voice authentication. This approach aims to reduce resource requirements while enhancing the overall security of the authentication process. This paper uses cosine similarity, Euclidean distance, random forest, and extreme gradient boosting (XGBoost) to measure distance or similarity. The results show that the proposed method has better accuracy compared to unimodal approaches, achieving accuracies of 98.44%, 98.83%, 97.46%, and 99.22% using cosine similarity, Euclidean distance, random forest, and XGBoost calculations. The proposed method also demonstrates resource savings, reducing from 5.19 MB to 0.792 MB, from 7.3294 MB to 1.9437 MB, from 6.6512 MB to 1.3284 MB, and from 7.8632 MB to 2.1517 MB in different distance or similarity measurement
MobileNet Backbone Based Approach for Quality Classification of Straw Mushrooms (Volvariella volvacea) Using Convolutional Neural Networks (CNN)
Get PDFStraw mushrooms (Volvariella volvacea) are a crucial commodity in Indonesia, with consumption on the rise due to their nutritional value and increasing demand for healthy food options. Despite this growth, farmers often struggle with accurately assessing the post-harvest quality of mushrooms according to market standards, which can diminish their economic value. Manual classification, which relies on human judgment and estimation, is frequently inefficient and susceptible to errors such as inconsistencies in quality assessment and limitations in detecting subtle variations. This study aims to automate the classification of straw mushrooms based on quality using deep learning, specifically by employing MobileNetv3 as the backbone for classifying mushrooms based on their shape and color by the Indonesian National Standards (SNI). The MobileNet-CNN Backbone model implemented in this study demonstrated exceptional performance, achieving a classification accuracy of 99%, thus proving its effectiveness and reliability in replacing traditional manual methods. The results of this research indicate significant potential for applying deep learning models to enhance the efficiency and precision of mushroom quality assessment. However, there remain challenges that require further development, including adding more diverse background data, improving image resolution, and refining data augmentation techniques. Addressing these challenges is essential for achieving optimal results in varying environmental conditions, ensuring the model can be broadly implemented in the agricultural industry. Such advancements could lead to more consistent and accurate quality assessments, benefiting producers and consumers in the mushroom market
Clustering Defensive Shariah-compliant Stocks Using Financial Performance as the Indicator
Get PDFMalaysian stocks, including Shariah-compliant stocks, have experienced turbulence last year. Although there are defensive stocks, the well-performing ones are not easily identified. Researchers have proposed various metrics to identify defensive stocks. However, most of the approaches require human intervention. In this study, we focus on Shariah-compliant stocks and propose to automate the labeling of stocks in terms of their financial performance via clustering. The study aims to identify the optimal clustering method to label the clusters. This was achieved by first employing k-Means, Agglomerative, and Mean Shift clustering to group similar stocks before labeling. When labeling, the criteria to distinguish well-performing defensive Shariah-compliant stocks were high dividend yield, low price-earnings ratio, low Beta value, and low price-to-book value. After labelling each stock with its financial performance (Low, Medium, High), we performed classification using Logistic Regression, k-Nearest Neighbors, Support Vector Machine, Decision Tree, and Random Forest to verify the credibility of the labels. Based on the results, the clusters created by k-Means clustering outperformed the rest in matching accuracy. Further investigation was conducted on the k-Means data set by dividing the data according to sector and classifying each sector’s data separately. Logistic Regression outperformed other classification algorithms with an accuracy of 71.5%. The findings also suggested accuracy increased when stocks were classified according to sectors. Further considerations include performing outlier analysis on the data to select well-performing stocks
Elevated Novice Developer Productivity and Self-efficacy by Promoting UX Journey in Software Requirement Elicitation
Get PDFThis study explores the effectiveness of the UX Journey methodology in increasing developer productivity and self-efficacy. Materials: The UX journey, consisting of around 30 activities, offers a user-centric approach to developing solutions, with 86 volunteer respondents from 505 populations. Method: Through a comparative analysis of developer productivity metrics and the General Self-Efficacy Scale questionnaire, this study investigates the impact of UX Journey on self-efficacy before and after implementation. Results: The study's findings reveal a significant positive correlation between UX Journey and increased productivity and an association between self-efficacy variables. By incorporating a comprehensive set of activities and a user-centric approach, the UX Journey enables developers to navigate the design process efficiently while gaining a deeper understanding of user needs. The positive correlation between the UX Journey and increased productivity, as well as the relationships between self-efficacy variables, emphasize the value of this methodology in fostering practical design thinking. Implication for Further Research: While this study has limitations regarding sample size and contextual specificity, it provides valuable insight into the benefits of UX Journey and paves the way for further research. In addition, the study focused on specific design projects within a particular context, which might restrict the broader applicability of the results. Significant results indicate that the proposed method is as effective as the elicitation method in general, with the advantage that the developer can understand the needs and empathy of the users. UX journeys can enhance the design process and foster a deeper understanding of users' needs across multiple domains
Comparison Analysis of CXR Images in Detecting Pneumonia Using VGG16 and ResNet50 Convolution Neural Network Model
Get PDFPneumonia is a lung disease that causes serious fatalities worldwide. Pneumonia can be complicated for medical professionals to identify since it shares similarities with other lung diseases like lung cancer and cardiomegaly. Hospitals face difficulty finding professional radiologists who help to detect pneumonia through radioactive processes. This research proposes VGG16 and ResNet50-based system architecture using the Convolutional Neural Network (CNN) module, which allows the detection of pneumonia. This research identifies pneumonia using chest X-ray (CXR) images through VGG16 and ResNet50 of CNN model architectures. The performance of the proposed models is compared by performance parameters such as processing time, accuracy, and loss. The Pneumonia dataset was obtained from Kaggle and divided into 70% for training, 15 % for validation, and 15% for testing. The results show that the proposed ResNet50 model architecture has a better result than the VGG16 model architecture. It can be clearly observed based on both models' loss and accuracy results. Moreover, the processing time for ResNet50 in training and predicting the CXR images is much faster than the VGG16 model's processing time. Hence, ResNet50 performs better than VGG16 based on the result of loss and accuracy and the processing time for the model to train and predict the data. In conclusion, the findings show the capability of CNN models for detecting pneumonia in CXR images, thus reducing the burden of professional radiologists
Firefly Algorithm for SVM Multi-class Optimization on Soybean Land Suitability Analysis
Get PDFSoybean is the primary source of vegetable protein nutrition, containing fat and vitamins that Indonesian people widely consume. The decline in soybean production in Indonesia every year is due to the reduced area of soybean cultivation, thereby increasing dependence on imports from other countries. Land suitability maps can provide directions for priority locations for soybean cultivation based on land characteristics and weather to produce optimal production. The SVM multi-class algorithm has been applied to classify land suitability data to create a land suitability map but has yet to obtain optimal accuracy, especially for sigmoid kernels. The objective of this study is to enhance the performance of the sigmoid kernel SVM by utilizing the firefly algorithm. The study focuses on evaluating the suitability of soybean cultivation in Bogor and Grobogan Regencies. The results of the tests indicate that the firefly algorithm-optimized SVM (FA-SVM) significantly improves accuracy compared to the SVM without optimization. The accuracy achieved by FA-SVM is 89.95%, while the SVM without optimization only achieves an accuracy of 65.99%. The best parameters produced by the firefly algorithm are C=2.33 and σ=0.45 obtained from firefly customization, and the number of generations is 10. Based on this, the optimization algorithm can be used to produce an optimal model. The best optimal model obtained can be used as a guide for priority locations/areas for soybean cultivation by farming communities, so as to produce maximum soybean productivity
A Model for Enhancing Pattern Recognition in Clinical Narrative Datasets through Text-Based Feature Selection and SHAP Technique
Get PDFClinical narratives contain crucial patient information for predicting cardiac failure. Accurate and timely cardiac failure recognition (CFR) significantly impacts patient outcomes but faces challenges like limited dataset sizes, feature space sparsity, and underutilization of vital sign data. This study addresses these issues by developing a methodology to improve CFR accuracy and interpretability within clinical narratives. Four datasets—the Framingham Heart Study, Heart Disease from Kaggle, Cleveland Heart Disease, and Heart Failure Clinical Records—undergo preprocessing, including handling missing values, removing duplicates, scaling, encoding categorical variables, and transforming unstructured data using natural language processing (NLP). Various feature selection methods (Chi-Squared, Forward Selection, L1 Regularization) are used to identify influential features for CFR, and the SHapley Additive exPlanations (SHAP) technique is integrated to improve interpretability. Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) models are trained and evaluated. Performance was evaluated using accuracy, precision, recall, f1-score, and area under the receiver operating characteristic curve (AUC-ROC). Results indicate that L1 Regularization with LR and Chi-Squared with RF perform best for specific datasets. The final model, combining all datasets with Forward Selection and RF, achieves high accuracy (91%), precision (87%), recall (97%), f1-score (91%), and AUC-ROC (94%). This study concludes that advanced text-based feature selection and SHAP interpretability significantly enhance CFR model accuracy and transparency, aiding clinical decision-making. Future research should incorporate more diverse datasets, explore advanced NLP techniques, and validate models in various clinical settings to enhance robustness and applicability
Problem-Frame-Oriented Requirements Traceability to Enhance Requirements Management
Get PDFManaging software requirements is a challenge in software development and maintenance. Requirements changes are inevitable, particularly in a rapid iterative development approach that leads to occasional changes in software requirements. Unable to manage this properly will impact the overall quality of the software. Thus, requirements traceability is essential because it ensures that all requirements are adequately addressed, changes are managed effectively, and that there's a clear linkage between business requirements and the system's functionality. Inadequate traceability mechanisms can make changing the requirements and detecting their impact difficult. Thus, it is crucial to establish precise requirements traceability and maintain clear links to manage the requirement changes effectively. Our research explores using a problem frames modeling approach to address this issue. It starts by representing requirements as problems, creating a requirements relationship diagram, and generating a corresponding relationship matrix. The values in the traceability matrix help identify which elements are most affected by requirement changes, allowing developers to prioritize changes that minimize overall system impact. Furthermore, using problem frame modeling, complex problems can be broken down into manageable sub-problems, providing a clear structure for understanding the requirements. Additionally, a tool has been created to streamline the process, and a case study is used to demonstrate the functionalities. An evaluation has been conducted to assess the usability of the proposed work. The requirements relationship diagrams and relationship matrices visually and quantitatively map the links between requirements, enabling traceability and identifying the impact of changes in requirements