JOIV : International Journal on Informatics Visualization
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Comparison of Noise Using Reduction Method for Repairing Digital Image
Get PDFDigital images are used to become a visual bridge of information. The information data must be precise so that the information can be adequately conveyed, but in the process, digital images sometimes experience a change in quality. One of the causes of this change is noise, where the image affected by noise is of poor quality, so misinformation can occur. This problem can be solved using filtering methods, but there are so many filtering methods. In this study, five filtering methods were used, including the Gaussian filter, mean filter, median filter, wiener filter, and conservative filter, to be compared with two types of noise, such as salt and pepper and speckle, so that the best method for noise reduction in digital images is known based on the criteria that have been set determined. The research results were determined based on the value of the measurement parameters Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The results show that the conservative method is the best based on the parameter values of MSE 3.21 and PSNR = 37.99. However, when viewed visually, the median method is superior for reducing noise in digital images that have been carried out. The results of the research can be used as information to develop future research, especially in the field of digital image processing
Development of extraction features for Detecting Adolescent Personality with Machine Learning Algorithms
Get PDFThis study aims to develop a Natural Language Processing (NLP)-based feature extraction algorithm optimized for personality type classification in adolescents. The algorithm used is TF-IDF + N-Gram Z, which combines Term Frequency-Inverse Document Frequency (TF-IDF) with the N-Gram Z technique to improve the feature representation of the analyzed text. TF-IDF functions to measure the importance of words in a document, while N-Gram Z enriches the context by considering the order of words that appear sequentially. The dataset in this study consists of 3,200 sentences generated by adolescent respondents through a survey designed to explore aspects of their personality. After the feature extraction process is complete, three variants of the Naïve Bayes method are applied for classification, namely Multinomial Naïve Bayes, Bernoulli Naïve Bayes, and Complement Naïve Bayes. Each variant has distinctive characteristics in handling certain data types, such as binomial and multinomial data. The results of the study show that the combined TF-IDF + N-Gram Z algorithm can produce highly representative features, as evidenced by high classification performance. The Multinomial Naïve Bayes and Complement Naïve Bayes variants each achieved 98% accuracy. These findings provide significant contributions to the development of NLP-based personality classification methods for Detecting Adolescent Personality. The combination of the TF-IDF + N-Gram Z algorithm with various Naïve Bayes variants produces an exceedingly high level of accuracy and can be applied in practice in the fields of psychology and adolescent education
Improved Fuzzy Possibilistic C-Means using Artificial Bee Colony for Clustering New Student’s Financial Capability to Determine Tuition Level
Get PDFOutliers in the dataset will affect the quality of the cluster, so a good clustering method is needed. Based on the Mahalanobis distance method, it is known that the research dataset has outliers. Clustering methods that are often used for this type of data are Fuzzy C-means (FCM), Possibilistic C-means (PCM), and Fuzzy Possibilistic C-means (FPCM). This study aims to develop a clustering method that is more robust to outliers by using the Artificial Bee Colony (ABC) algorithm to minimize the objective function of FPCM. This study produces a new algorithm called Artificial Bee Colony Fuzzy Possibilistic C-Means (ABCFPCM) so that the resulting clusters are not easily trapped in the local optimum. This study also provides cluster centroid initialization using K-Means++ to improve cluster quality. ABCFPCM performs best because it significantly increases the Silhouette value and the Between Sum Squares (BSS) and Total Sum Squares (TSS) ratio. ABCFPCM performance provides the best cluster quality of 72.16% based on the BSS/TSS ratio, FPCM of 70.71%, and FCM K-Means++ of 68.14%. K-Means++ in the cluster method does not affect cluster performance except for FCM, where cluster quality is slightly increased. The centroid results of 8 clusters as the best performance of ABCFPCM are used to determine the tuition rate level. The impact of this study is to improve the quality of FPCM performance because it is no longer trapped in a local optimum at the cluster centroid
Comprehensive Review of Security Requirements for Mitigating Threats and Attacks on IoT Assets
Get PDFMachine learning and artificial intelligence are increasingly being utilized to automate identifying and defining security requirements (SR) and addressing diverse IoT security issues. Despite its extensive environment, IoT-focused cyberattacks had the largest attack surface. IoT security requirements include data confidentiality, integrity, authentication, access control, and privacy. Inadequate emphasis on assessing security requirements leads to attacks and threats. To address the security issues that threaten the IoT environment, additional security measures are required to protect IoT-based applications from threats and other vulnerabilities. However, the absence of the security requirement assessment in IoT systems architecture jeopardizes security, exposing the system to vulnerabilities and risking organizational assets and reputation while also escalating the cost and time required to address security issues. In this study major threats and attacks are identified relevant to the assets of IoT security requirements. To systematically identify, analyze, and address potential security threats and attacks related to IoT assets, this research proposes a three-step methodology: (1) analysis of the IoT security requirements, (2) Identification of threats and attacks in IoT, and (3) IoT assets centric security threats and attacks. An illustrative example of IoT asset security is provided to highlight potential attacks and threats relevant to IoT assets. This approach offers a practical and clear foundation for the early identification of IoT security requirements and their seamless integration into requirements engineering (RE) activities, contributing to a more secure and resilient IoT system architecture
Syllable Segmentation with Vowel Detection on Verse Quranic Recitation
Get PDFIn speech recognition, segmentation involves partitioning a continuous audio signal containing speech into smaller units or segments, such as words, phonemes, or syllables. This process is paramount in speech recognition systems, as it delineates the boundaries between distinct speech elements, facilitating subsequent analysis and processing. Segmentation accuracy significantly impacts speech recognition systems' overall precision and performance, enabling more precise identification and processing of individual speech units. Moreover, proper segmentation empowers the automatic speech recognition (ASR) system to distinguish between different syllables or words effectively, leading to more efficient speech recognition outcomes. This research paper investigates the importance of vowel detection for syllable segmentation in speech recognition, particularly in Arabic speech, such as the Quran, where changes in each syllable can alter the meaning. While existing techniques only consider pronunciation by different readers, this study employs onset detection to account for the presence of Arabic vowels. Specifically, the study focuses on detecting the onset of the recitation of Surah Al-Fatihah's fourth verse using 50 data sets in the syllable detection testing process. The results indicate that syllable detection performs excellently on syllables with /a/ and /i/ vowels. However, syllables with /u/ vowels produce results below 70%. The study suggests that the onset-based method is ideal for syllables with the presence of /a/and /i/ vowels, demonstrating the importance of considering Arabic vowel letters in speech recognition
Optimizing Genetic Algorithm by Implementation of An Enhanced Selection Operator
Get PDFThe Traveling Salesman Problem (TSP) represents an extensively researched challenge in combinatorial optimization. Genetic Algorithms (GAs), recognized for their nature-inspired approach, stand as potent heuristics for resolving combinatorial optimization problems. Nevertheless, GA exhibits inherent deficiencies, notably premature convergence, which diminishes population diversity and consequential inefficiencies in computational processes. Such drawbacks may result in protracted operations and potential misallocation of computational resources, particularly when confronting intricate NP-hard optimization problems. To address these challenges, the current study underscores the pivotal role of the selection operator in ameliorating GA efficiency. The proposed methodology introduces a novel parameter operator within the Stochastic Universal Selection (SUS) framework, aimed at constricting the search space and optimizing genetic operators for parent selection. This innovative approach concentrates on selecting individuals based on their fitness scores, thereby mitigating challenges associated with population sorting and individual ranking while concurrently alleviating computational complexity. Experimental results robustly validate the efficacy of the proposed approach in enhancing both solution quality and computational efficiency, thereby positioning it as a noteworthy contribution to the domain of combinatorial optimization
Factors Influencing Information Quality of Information Systems: A Systematic Literature Review
Get PDFIn today's digital era, information quality in information systems is essential for organizational effectiveness and decision-making. This systematic literature review aims to assess and synthesize factors influencing information quality across various systems, focusing on key dimensions such as reliability, accessibility, usability, accuracy, completeness, and timeliness. The existing literature is fragmented, lacking an integrated theory that comprehensively addresses the significance of information quality. Thus, a systematic review was conducted following the PRISMA framework to address this gap and provide evidence-based recommendations for research and practice. Studies were identified, screened, and selected from Scopus and Web of Science. After an initial search using specific keywords, a total of 1,548 articles were found that contained specified terms or strings in various combinations. Of these, 31 studies were chosen for full review based on predefined inclusion and exclusion criteria. The analysis was organized into three primary themes: i) Core Information Quality Factors, ii) Synergizing Information Quality with System and Service Quality, and iii) Impact of Information Quality on User Satisfaction and Organizational Outcomes. The results emphasize the significant role of high information quality in enhancing user satisfaction and operational efficiency. Different industries prioritize various quality dimensions according to their specific needs. Therefore, this review elucidates the imperative function of good quality information in reinforcing information systems' proper functioning, calling for empirical studies to develop holistic frameworks that incorporate multiple dimensions and impact analysis across different domains
Attributes Classification for Elaborating the Information of Digital and Imaging Mapping
Get PDFThe rapid development of information has made it possible for everyone to obtain the latest information, complete and accurate, in real time, anytime, anywhere, all over the world. Any information is fine catching up by updated with the latest news and even detailed information on local conditions. With the same analogy, detailed information regarding land utilization, land containing, landscape provision, earth surface contour, etc., are required to inform and elaborate any appropriate decision needed. The Geographic information systems (GIS) is a recent technology commonly used by research in earth science to facilitate many layered detail information by one way to get up-to-date, detailed information. In this research, the GIS utilizes several types of imaging data such remote sensing images and digitize images. As the name suggests, this system captures detailed geographic information about a location or region. By inputting classified images of remote sensing results into a GIS database at regular intervals (adjusted as necessary, such as every year, every two years, every three years, etc.), the number of information sources that can be obtained increases. There are several reasons for that. First, remote sensing images are images that cover the entire surface of the Earth. Next, remote sensing images are images that contain information about the state of the earth's surface. Third, a variety of information can be obtained by performing appropriate image processing. Furthermore, this research could be elaborated by implementing an artificial intelligent algorithm to create a robust outcome
Toward Adoption User Experience Variables for Solo Software Development in Academic and Industry
Get PDFoai::article/2172User experience (UX) is frequently mentioned as a rapidly expanding profession. It generates prospects in almost every industry for individuals with expertise in applying design principles and techniques centered around user needs. Many universities specializing in informatics and computer science have acknowledged this demand by integrating more coursework focused on UX into their educational programs. However, the widespread adoption of agile software development and efficient product design has prompted businesses to seek experienced candidates for UX positions, even at entry level. Consequently, individuals aspiring to work in this position need UX experience before acquiring it. Materials: We conducted a pilot study on 235 respondents who voluntarily participated in the research to examine the effect of UX on students' ability to identify a problem in software requirements. Method: This article evaluates the endeavors of a particular institution to bridge this experience gap. The article offers insights and recommended practices for effectively integrating. This research involved industries from two developed countries and one developing country, as well as implementing these aspects on students at one of the institutions with a General Self Efficacy (GSE) scale. Results: The results show the dominant user experience quality aspects. In addition, we provide recommendations for applying to several courses and competencies to enhance student self-efficacy. Implication for Further Research: In this paper, we used a combination of developing a conceptual framework and identifying the industry. With these two methods, we solve the UX gap between industry and academia from the perspective of the UX attribute. Some standards are commonly used in academics to construct the curriculum for their student
Forum Text Processing and Summarization
Get PDFFrequently Asked Questions (FAQs) are extensively studied in general domains like the medical field, but such frameworks are lacking in domains such as software engineering and open-source communities. This research aims to bridge this gap by establishing the foundations of an automated FAQ Generation and Retrieval framework specifically tailored to the software engineering domain. The framework involves analyzing, ranking, performing sentiment analysis, and summarization techniques on open forums like StackOverflow and GitHub issues. A corpus of Stack Overflow post data is collected to evaluate the proposed framework and the selected models. Integrating state-of-the-art models of string-matching models, sentiment analysis models, summarization models, and the proprietary ranking formula proposed in this paper forms a robust Automatic FAQ Generation and Retrieval framework to facilitate developers' work. String matching, sentiment analysis, and summarization models are evaluated, and F1 scores of 71.31%, 74.90%, and 53.4% were achieved. Given the subjective nature of evaluations in this context, a human review is used to further validate the effectiveness of the overall framework, with assessments made on relevancy, preferred ranking, and preferred summarization. Future work includes improving summarization models by incorporating text classification and summarizing them individually (Kou et al, 2023), as well as proposing feedback loop systems based on human reinforcement learning. Furthermore, efforts will be made to optimize the framework by utilizing knowledge graphs for dimension reduction, enabling it to handle larger corpora effectivel