JOIV : International Journal on Informatics Visualization
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Fermented and Unfermented Cocoa Beans for Quality Identification Using Image Features
Get PDFFermented cocoa bean products are one of the high-quality requirements of the cocoa processing industry. On an automated industrial scale, early identification of cocoa bean quality is essential in the processing industry. This study aims to identify the condition of quality cocoa beans based on fermentation and non-fermentation characteristics. This study applies analysis based on static images taken using a camera with a distance variation of 5 cm, 10 cm, and 15 cm in both classes, with 500 image data each. The Feature extraction Approach uses the Oriented Gradient (HOG) method with a Support Vector Machine (SVM) classification technique. Image analysis of both object classes was also performed with a color change to show the dominance of the color pattern on the skin of the cocoa beans to be analyzed. The results showed that fermented cocoa beans show a color pattern and texture that tends to be darker and coarser than non-fermented cocoa beans. Computational results with performance analysis using Receiver Operating Characterisic (ROC) on both classes showed the results that the distance of 5 cm and 15 cm has 100% accuracy, but based on the best performance, comprehensively seen in terms of Precision, Recall, and F1-Score shows the best value is at a distance of 15 cm. The results of this research based on the literature review conducted have better achievements, thus enabling further research on the development of conveyor models with real-time video data for automation systems
Determination of Training Participants in Community Work Training Centers Using the Naïve Bayes Classifier Algorithm
Get PDFCommunity work training centers are skills training institutions that aim to improve the skills of the surrounding community by providing training programs that align with industry needs. Registration of training participants at the Al-Ikhwan Islamic Boarding School community work training centers often faces obstacles, namely, the selection process is still manual, so it takes a long time, and there is a possibility of errors. This study aims to apply the Naive Bayes Classifier Algorithm to determine whether applicants pass training at the Al-Ikhwan Islamic Boarding School community work training centers. This classification method is used to help optimize the applicant selection process by considering administrative factors, income, and training quotas. RapidMiner software is used as a tool to implement the algorithm. This study found that the Naive Bayes Classifier Algorithm can provide good accuracy results in determining applicants who pass the training selection. The test results show that the resulting model has an accuracy of 90.00% in determining passing training participants with data that has the highest chance of passing, namely data that has the attributes of the female gender, age 20 years, last education Senior High School/Vocational High School, student work/student, income 364,912, father's work as laborer, father's income 3912,280, mother's work as an IRT, and mother's income 885,964. This research increases efficiency and accuracy in determining training applicants at the Al-Ikhwan Islamic Boarding School community work training centers
Data-Driven User Personas in Requirement Engineering with NLP and Behavior Analysis
Get PDFAs technology rapidly evolves, software development faces growing complexity, requiring adaptation to dynamic user expectations. This study addresses a critical gap in the existing literature by integrating behavioral data and sentiment analysis into the user persona development process within the requirement engineering framework. The primary objective is to create more accurate and representative user personas that better guide software design and development. To achieve this, the research employs advanced Natural Language Processing (NLP) techniques to systematically analyze extensive behavioral and sentiment data collected from social media platforms. The integration process involves segmenting user data into behavioral patterns and emotional states, which are then synthesized to develop nuanced user personas. These personas are expected to significantly improve the accuracy of user requirements, leading to enhanced software performance, increased user satisfaction, and greater development efficiency. The target application area for this research is mobile telecommunications, where precise user understanding is critical. The results indicate that this approach not only refines the traditional persona method but also addresses the evolving needs of users more holistically. By advancing the methodology for user-centered design, this study contributes to the broader field of requirement engineering. Future research will validate and refine this approach across diverse domains, ensuring its adaptability and effectiveness in different contexts. This paper thus has the potential to make a significant impact on how user personas are developed and utilized in software engineering
Transliterating Javanese Script Images to Roman Script using Convolutional Neural Network with Transfer Learning
Get PDFThe Javanese script holds immense cultural significance within Indonesia despite its diminishing usage in contemporary contexts. Its presence remains notable in specific regions of Java and remains integral to many historical documents and texts. Consequently, there is an urgent need for a transliteration system adept at converting Javanese script into contemporary scripts like Roman or Indonesian, thereby contributing to preserving Java's linguistic and cultural legacy. However, reading or transliterating Javanese script can be time-consuming, especially for longer texts, presenting considerable challenges for non-native readers. This study aims to develop an effective transliteration system for converting Javanese script into Roman script. This system addresses the pressing need to preserve Java's linguistic and cultural heritage by facilitating the readability and accessibility of Javanese script, especially for non-native readers. This study introduces an Optical Character Recognition (OCR) system tailored to identify Javanese script characters and transcribe them into Roman characters, explicitly focusing on fundamental nglegena and sandhangan swara characters. Individual characters are isolated by leveraging horizontal and vertical projection techniques, facilitating subsequent classification using a Convolutional Neural Network (CNN) employing transfer learning methodologies. The system's achievement of an impressive average similarity score of 90.78% is noteworthy, with the Xception architecture demonstrating superior efficiency in transliteration tasks. Implementing such a system harbors significant promise in safeguarding the Javanese script and enhancing its accessibility to a broader audience. This research contributes substantially to preserving and propagating Indonesia's rich cultural and linguistic heritage amidst the digital age
Issues in Chinese Requirements Specifications: Insights from Survey Data and Static Analysis
Get PDFRequirements engineering is crucial for software project success. Issues like requirements ambiguity, inconsistency, and unverifiability contribute to unclear, conflicting, or untestable specifications, which can undermine the effectiveness and success of a software project. These issues have been identified as factors contributing to software project failure. However, there’s limited research on the current state of these issues in China. The research objectives of this study are to address the most commonly used methods for expressing Chinese software requirements and uncover issues related to ambiguity, inconsistency, and unverifiability, which can be solved by using artificial intelligence techniques to investigate possible solutions to these problems. An online survey of 422 software professionals in China identifies key issues in Chinese software requirement expressions that AI techniques can address. The study examines various expression methods, tools for enhancing clarity, and challenges specific to Chinese requirements. Findings reveal that ambiguity, inconsistency, and unverifiability significantly impact project success. While natural language specification and prototyping improve clarity, they may increase the time required for requirements engineering. Effective communication is typically achieved through natural language, prototyping, storyboarding, and pseudo-coding, whereas decision tables and block diagrams are less commonly used and linked to problematic requirements. Using tables, prototype diagrams, and natural language descriptions helps mitigate these issues, though it may extend engineering time. The study suggests strategies to improve the efficiency and quality of requirements expression and highlights the need to develop Chinese boilerplates and refining tools to enhance clarity in the future
Boosting Performance of SVM in Koi Classification Using Direct Methods-Based Optimization
Get PDFMany koi fish enthusiasts keep or buy them just for their attractive colors without knowing what type of koi fish they are. The manual classification of koi fish species is still frequently incorrect. As a result, it is critical to apply a machine learning technique to identify various koi fish species. This research implemented a computer vision algorithm to classify koi fish species using the Support Vector Machine (SVM) as the classifier. However, the maximum accuracy SVM can achieve in our koi fish classification system is 79%. To achieve better performance, the SVM was optimized by applying various optimization methods from the Direct Method group, i.e., the Generalized Pattern Search (GPS), the Powell method, and the Nelder-Mead method. Three optimization methods from the Direct Method group have successfully improved the performance of SVM in this task. Experimental results demonstrated that using the Generalized Pattern Search (GPS) in our classification system can increase the accuracy to 98%. Also, implementing the Powell and the Nelder-Mead method can make the koi classification system obtain a better accuracy of 99%. These results indicate that the proposed approach is a viable solution to overcome the limitations of the SVM algorithm
Enhancing Potato Leaf Disease Detection: Implementation of Convolutional Vision Transformers with Synthetic Datasets from Stable Diffusion
Get PDFNumerous studies have addressed the classification of potato plants. However, the available datasets often lack the necessary diversity to improve the accuracy of predictive classification models effectively. Our research capitalizes on synthetic datasets generated through the Stable Diffusion 1.5 image generation method to address this challenge. This study suggests a new way to solve the problem by using artificial datasets created with the Stable Diffusion 1.5 method to teach a Convolutional Vision Transformer (CvT) model how to identify diseases on potato leaves accurately. Our objective is to train the CvT model employing synthetic datasets to excel in detecting potato leaf diseases. Our methodology encompasses the model's training using synthetic datasets from Stable Diffusion 1.5. We employ a comprehensive dataset of 11,121 synthetic images to train the Convolutional Vision Transformer (CvT) model, which enables it to accurately identify various potato leaf diseases such as black leg/soft rot, mosaic, leaf roll, early blight, and late blight. We conduct evaluations at multiple training stages to gauge the model's performance and accuracy. The outcomes of our research underscore the effectiveness of employing synthetic datasets from Stable Diffusion 1.5, which significantly augments the available image data while preserving a high level of accuracy. The CvT model proficiently identifies potato leaf diseases with an evaluation accuracy of 84%. Additional testing reveals that by the fifth epoch, the CvT model attains an accuracy of 81% when assessed using 82 randomly selected images of diseased plants from Google. The implications of this research are far-reaching, particularly within the domains of image processing and agriculture. The strategy of utilizing synthetic datasets to train the CvT model presents an efficient remedy to address the limitations of original image datasets. The adept disease detection capability of the CvT model holds the potential to expedite plant condition identification, mitigate crop loss, and ultimately amplify agricultural productivity. This study effectively demonstrates that the Convolutional Vision Transformer (CvT), when leveraged with synthetic datasets from Stable Diffusion 1.5, produces a model capable of accurately identifying potato leaf diseases. These findings bear positive implications for both the agricultural and image-processing sectors.
An Automated Face Detection and Recognition for Class Attendance
Get PDFClass attendance is a crucial indicator of students' seriousness towards learning. Many institutions continue to use manual methods, which are usually error-prone and unproductive. By leveraging computer vision algorithms, the system accurately captures and verifies the identity of students attending class. This paper aims to investigate and create an automated facial recognition system for classroom attendance to increase the precision and effectiveness of the attendance tracking system. To achieve this, we propose a system using computer vision technologies, namely Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) for face detection and deep Convolutional Neural Networks (CNN) for face identification. The facial recognition system simplifies attendance recording, requiring participants to only gaze into the camera for the system to record their presence automatically. The system is rigorously tested and evaluated, and its accuracy is compared to our institution's current QR code attendance method. The study results reveal that the recommended approach is more accurate and competent than the existing procedures. The system allows for precise attendance records with real-time face detection and recognition capabilities. This technology ensures accurate and reliable attendance data, empowering organizations to make informed decisions, effectively manage resources, and provide a seamless experience for all students. In addition, a similar attendance system can be deployed for any event in an organization, thereby enhancing overall operational efficiency
Enhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning
Get PDFAdvancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, pre-processing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies
An Automated Fingerprint Image Detection and Localization Approach-based Unsupervised Learning Algorithms using Low-quality Biometrics Plam Data
Get PDFIn this study, fingerprint identification and classification of low-quality fingerprints have been analyzed accordingly. As technology advances and methodologies evolve, staying at the forefront of research and innovation is imperative. The challenges addressed in this paper provide a foundation for future investigations and underscore the importance of developing resilient and adaptable biometric systems for real-world applications. The quest for accurate, efficient, and robust fingerprint identification in adverse conditions is a testament to the continuous evolution and refinement of machine learning and deep learning approaches in biometrics. While deep learning models exhibited improved performance, it is essential to acknowledge the need for further research and development in this domain. Additionally, integrating multimodal biometric systems and combining fingerprint data with other biometric modalities might present a viable avenue for mitigating the limitations associated with degraded fingerprints. In this paper, we develop a fingerprint identification approach for low-quality fingerprint images. The success rate accuracy of the propped algorithm for the low-quality fingerprint images should be significantly better than that of the standard local minutia approach. The main design of our deep learning approach is based on detecting and extracting the primary correlation during the training and using the correlation feature map to calculate the distance between the low-quality fingerprint images during the predicting phase. The experimental results show a very promising repulsing and high prediction accuracy