1,720,959 research outputs found
Klasifikasi MIT-BIH Arrhythmia Database Metode Random Forest dan CNN dengan Model ResNet-50: A Systematic Literature Review
Full text linkAlthough Machine Learning and Deep Learning technologies have been widely used and have shown high accuracy in many applications, including in the health field, their application in early detection of heart disease still has room for improvement. Further research is needed to enhance the accuracy and efficiency of this process. This study aims to understand and improve the process of ECG signal extraction and classification based on Machine Learning and Deep Learning. Essentially, this research aims to evaluate and compare various models, focusing on the Random Forest and Convolutional Neural Networks (CNN) models. The study reviews several related researches, especially those focusing on the process of extraction and classification of ECG signals using Machine Learning and Deep Learning. After extraction and classification of data, an evaluation and comparison process is conducted to determine the best performing model. From the research conducted, it was found that Machine Learning methods generally show an accuracy rate between 97.02% - 99.66%, with the Random Forest method having an accuracy of 97.02%. Meanwhile, the CNN method shows a higher accuracy rate, which is between 98.75% - 100%. Thus, this research confirms the superiority of CNN in this classification process, and shows potential for further use in early detection of heart disease
E-learning Academy Untuk Meningkatkan Kapasitas SDM Di Lingkungan Perusahaan Transportasi X
No full text Services in the field of land transportation services are still a sector needed by the community for mobility and economic growth. The main problem faced by Transportation Company X in developing human resources (HR) is the uneven skills and knowledge between generations in the organizational structure. Gen X dominates with 57.36%, Gen Y (38.34%) and Gen Z (4.29%). This has an impact on the ability to adapt to the demands of the modern transportation industry. This research aims to develop and implement an e-Learning Academy, to increase the capacity of X Transportation Company's human resources. This research methodology uses SCRUM framework in learning system development, with agile approach that allows adaptation to changes quickly and efficiently. E-Learning Academy features video-based learning and interactive elements that allow employees to learn independently, thus maximizing knowledge transfer and improving skills in various fields. Survey results after testing by users through user acceptance test activities show that on the Ease of Navigation aspect, 55% of respondents stated “strongly agree” the application is easy to use”. The aspect of Confidence in Application Capabilities, the results are 55% of respondents “strongly agree” this application believes it can improve HR skills and abilities. For the Quality of Main Features, 36% of respondents stated “strongly agree” the main features in this application are easy to use and the remaining 64% stated “agree”. On the aspect of Impact on HR Improvement, 46% of respondents “strongly agree” this application has a positive impact and the remaining 54% of respondents stated “agree”. Finally, on the aspect of Benefit for the Company, 36% of respondents “strongly agree” that this application is useful and the remaining 64% stated “agree”. This platform can be accessed across all business sectors so that it becomes a strategic tool that helps Transportation Company X achieve its goals and improve its public transportation services
Systematic Literature Review: Predicted Color Output in UI/UX Design Using Machine Learning
Full text linkAn attractive user interface (UI) design is greatly influenced by the selection of appropriate colors, but the selection process tends to be subjective. To address this challenge, this study was conducted to identify commonly used machine learning techniques and evaluate their effectiveness in recommending colors based on RGB and HSL features. The method used was a Systematic Literature Review (SLR) of 39 articles published between 2020 and 2025. The study was conducted through three main stages, namely planning, implementation, and reporting. The review results show that approaches such as K-Means are widely used in the dominant color extraction stage, while classification algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest are applied for color prediction and recommendation. Random Forest is one of the models that shows superior performance, especially in terms of prediction stability and the ability to handle large numbers of variables. The model development process usually begins with color quantization, followed by data labeling and model training. Based on these findings, it can be concluded that Random Forest is a reliable model in color recommendation systems, especially when supported by good data preprocessing stages and proper parameter tuning
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Analisa Performa Algoritma Random Forest & Logistic Regression Dalam Sistem Credit Scoring
Full text linkThe rapid advancement of technology, particularly in the field of Artificial Intelligence (AI), has had a significant impact across various industries. One increasingly popular implementation is ChatGPT, enabling more intuitive human-computer interactions. Moreover, AI has transformed the landscape of the financial sector, particularly in Credit Scoring. Using Supervised Machine Learning, algorithms like Random Forest and Logistic Regression are employed to enhance accuracy and efficiency in the Credit Scoring process. However, comparing the accuracy between these two algorithms remains a question. Therefore, this research aims to compare the accuracy levels of Random Forest and Logistic Regression in the context of Credit Scoring. From the research that have been conducted got result Random Forest given better AUC score on 0.90 than Logistic Regression which only got 0.89.Perkembangan teknologi yang pesat, terutama dalam bidang Artificial Intelligence (AI), telah membawa dampak besar pada berbagai sektor industri. Salah satu implementasi yang semakin populer adalah ChatGPT, yang memungkinkan interaksi manusia dengan komputer secara lebih intuitif. Selain itu, AI juga telah mengubah lanskap sektor keuangan, terutama dalam hal Credit Scoring. Dengan menggunakan Machine Learning Supervised, algoritma seperti Random Forest dan Logistic Regression digunakan untuk meningkatkan akurasi dan efisiensi dalam proses Credit Scoring. Namun, perbandingan antara akurasi kedua algoritma tersebut masih menjadi pertanyaan. Oleh karena itu, penelitian ini bertujuan untuk membandingkan tingkat akurasi antara Random Forest dan Logistic Regression dalam konteks Credit Scoring. Dari hasil penelitian kali ini didapatkan bahwa untuk dataset yang digunakan dalam penelitian Random Forest menghasilkan nilai AUC yang lebih baik yaitu sebesar 0.90 dibandingkan Logistic Regression pada angka 0.89
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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