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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 518 Documents
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The Design of IoT-based Business Process for SME Digital Transformation: a Case of Unofficial Car Service Workshop Widjaja, Albertus Hendrawan; Gunawan, Gunawan
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.247

Abstract

The Internet of Things (IoT) offers innovation processes that transform industry, finance, healthcare, agriculture, hospitality, and other sectors through process automation. Integrating IoT into business processes will transform an organization for better time, cost efficiency, and customer satisfaction. While the advantages of adopting IoT in the business process are widespread, the clear guidelines for implementing IoT for SMEs are limited. SMEs often do not recognize the potential of digital transformation and do not receive the necessary assistance to undertake critical development activities. This paper addresses this issue by focusing on IoT solutions for a car service workshop as an SME. This study aims to analyze the current business processes and design an IoT-based business process model for a car service workshop. The system development life cycle was adopted partially to design IoT-based business processes. The proposed business process model is designed with Business Process Modeling Notation (BPMN) to minimize time, effort, and cost inefficiencies. The concept and design of IoT systems were validated by the manager of five car service workshops. They perceived the transformation of car service using IoT as innovative, potentially increasing their business competitiveness. The respondents suggested that the implementation was executed gradually because of human resource readiness and investment costs. The design of IoT-based business process could become the guideline for car service workshops to transform the business into Industry 4.0.
Computer Simulation of Control of High-Order Nonlinear Systems using Feedback Bakhadirova, Gulnaz; Tasbolatuly, Nurbolat; Tanirbergenova, Alua; Dautova, Aigul; Akanova, Akerke; Ulikhina, Yuliya
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.275

Abstract

The relevance of the topic stated in this research is the need to develop and implement methods of computer modelling of these control systems. The purpose of this research is the search for opportunities for controlling nonlinear systems and the creation of a computer model for controlling nonlinear systems. The basis of the methodological approach in this research work is a combination of methods of theoretical and applied research of general principles of construction of computer models of control of high order nonlinear systems by means of feedback. In the course of the research work, the results were obtained, indicating the effectiveness of the development of an algorithm for finding the control of tracking a given reference signal of nonlinear systems and nonlinear systems with time delay. An algorithm has been developed to find a control that can effectively track the output signal of a nonlinear system behind a given reference signal. In addition, a scientific analysis of the tracking and stabilization errors of nonlinear systems and time-delayed nonlinear systems has been carried out depending on the control parameters, and graphical representations of a computer model of numerical experiments performed according to the control algorithms have been presented. It is established that the output control problem for a nonlinear system is to obtain a feedback control that forces the controlled output signal of the nonlinear system to asymptotically track the reference signal. The practical significance of the obtained results lies in the possibility of their use in the creation of computer models of process control with feedback.
Breast Cancer Prediction Using Metrics-Based Classification Armoogum, Sheeba; Dewi, Deshinta Arrova; Kezhilen, Motean; Trinawarman, Dedi
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.351

Abstract

Breast cancer remains the most prevalent form of cancer among women, with rising mortality rates worldwide. Early detection and accurate classification are crucial for improving patient outcomes, but manual detection methods are often time-consuming, complex, and prone to inaccuracies. This study aims to develop a machine learning (ML)-based desktop application to automate the detection and classification of breast cancer, thereby improving the efficiency and accuracy of diagnosis. Various ML algorithms, including Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, Gaussian Naive Bayes, and K-nearest Neighbors, were employed to build classification models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used, and pre-processing techniques such as data cleaning, over-sampling, and feature selection were applied to optimize model performance. Experimental results demonstrate that the Random Forest classifier outperformed the other models, achieving an accuracy of 95.54%, precision of 96.72%, recall (sensitivity) of 95.16%, specificity of 96%, and an F1-score of 95.93%. These results highlight the potential of ML techniques in enhancing breast cancer diagnosis by offering a more reliable and efficient classification process. Future work could focus on improving feature selection techniques and applying the model to more diverse datasets for broader applicability.
Cognition-Based Document Matching Within the Chatbot Modeling Framework Jatmika, Sunu; Patmanthara, Syaad; Wibawa, Aji Prasetya; Kurniawan, Fachrul
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.209

Abstract

The aim of the study is to examine cognitive methods for document matching in a chatbot modeling framework by utilizing Euclidean Distance, Cosine Similarity, and BERT methodologies. Five primary indications are used to carry out evaluation in testing: document matching accuracy, document matching execution time, document search efficiency, consistency of document matching results, and the quality of the document representation in the matrix. Document matching accuracy is evaluated by precision; document matching execution time is measured from the beginning to the end of the document matching process; document search efficiency is measured through evaluation of execution time and matching accuracy; the consistency of document matching results is assessed by comparing method results when tested against the same or similar queries and the quality of document representation is assessed based on the method's ability to represent documents in a matrix or vector. The test findings offer a comprehensive understanding of how well the three approaches operate and exhibit their capacity to address the unique requirements of chatbot users. These results may contribute to the advancement of language technology applications, making it possible for chatbots to deliver pertinent information more rapidly and precisely. There are 1,755 labeled question samples in the dataset, which were split up into two sets: 60% for training (1,053 pieces), and 40% for testing (702 samples) to evaluate the model's performance. The test results show the accuracy of the three methods based on five measured evaluation indications, namely Euclidean Distance 0,45%, Cosine similarity 0,59%, and BERT 0,91%.  By comprehending the benefits and drawbacks of each approach, this research strengthens contributions to the growth of chatbot systems to better serve user demands and opens the door for the creation of more complex human-machine interaction solutions.
Applying Structural Equation Modelling for Assessing Factors Influencing Innovation Capacity and Business Efficiency Nga, Lu Phi; Huy, Nguyen Quoc; Tam, Phan Thanh
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.295

Abstract

In the context of globalization and competition between businesses in an increasingly fierce international trade environment, participating in production networks and global value chains has become an inevitable requirement for developing each economy, in which business forces play a core and pioneering role. Creativity is critical to achieving significant business success in any business, large or small, in manufacturing, commerce, or service. Implementing innovation and creativity will have a profound and lasting impact on the enterprise's ecosystem. Conversely, companies that fail to innovate risk falling behind and becoming irrelevant in today's rapidly evolving business environment. Therefore, this study aims to analyze the key factors affecting innovation capacity and business efficiency, thereby providing solutions to promote this process. The study applied two methods: qualitative research, conducted through interviews, and focused on ten expert group discussions to adjust the content of observed variables to suit the characteristics of the business. Quantitative research was undertaken in 700 samples of representative managers representing 700 small and medium enterprises to test the model and research hypotheses. The findings show five factors affecting innovation capacity, with a significance of five percent, and innovation capacity affecting business efficiency. This result contributes to academic value and is a reference for research on innovation capacity in Vietnam in the coming years. There are five policy implications and contributions to promoting and building a creative culture in businesses, stimulating creativity and passion. After all, developing an innovative culture in businesses will create the role of individuals and groups with endless intelligence and creative passion for creating unique, different, and valuable products with high added value, significantly contributing to promoting growth in businesses.
Optimized Deep Learning method for Enhanced Medical Diagnostics of Polycystic Ovary Syndrome Detection Praneesh, M.; Nivetha, N.; Maidin, Siti Sarah; Ge, Wu
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.368

Abstract

This paper explores Polycystic Ovary Syndrome (PCOS), a common hormonal disorder caused by elevated androgen levels, which affects women's reproductive health. The primary objective is to enhance early detection and diagnosis of PCOS using advanced machine learning techniques. To achieve this, the study utilizes VGG19 Net, integrated with various machine learning algorithms, to classify ultrasound images of the ovaries. The research involves analyzing ultrasound scans to differentiate between benign and potentially cancerous cysts. The contribution of this study lies in its novel application of VGG19 Net, which achieved an accuracy rate of 96% compared to other techniques: Random Forest (94%), Logistic Regression (91%), Bayesian Classifier (81%), Support Vector Machine (92%), and Artificial Neural Network (90%). The findings indicate that VGG19 Net outperforms traditional methods in precision and accuracy, with a significant improvement in detecting early-stage PCOS. This approach not only provides a clearer diagnostic image but also enables timely intervention, thus addressing the challenge of distinguishing between benign and malignant cysts more effectively. The results underscore the potential of VGG19 Net in revolutionizing PCOS diagnosis through enhanced image classification, offering a valuable tool for medical practitioners.
Forms and Field Trials of a Digital Evaluation Tool: Integrating F-S Model, WP Method, and Balinese Local Wisdom for Effective E-Learning Ariawan, I Putu Wisna; Sugandini, Wayan; Ardana, I Made; Sugiharni, Gusti Ayu Dessy; Gama, Adie Wahyudi Oktavia; Divayana, Dewa Gede Hendra
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.201

Abstract

This study purposed to show the tool display and the results of field trials on the digital evaluation tool. This tool is an evaluation tool in digital format which was from a combination of the concept of the educational evaluation model “F-S (Formative-Summative)”, the decision support system method “WP (Weighted Product)”, and Balinese local wisdom “TP (Tri Pramana)”. The importance of combining these concepts and methods is it makes it easier to obtain accurate calculation results following the needs of evaluation tools to determine the dominant aspects determining the effectiveness of e-learning. This research approach was development research. The development model was Borg and Gall, which focused on the field trial and field trial revision stages. The reason for focusing on those two stages was that we wanted to know how effective the evaluation tool was in getting the dominant aspects determining the effectiveness of e-learning. The research location was at several health colleges in Bali. Field trials data collection was using a measuring instrument in the form of a questionnaire. The respondents who were involved in conducting field trials were 54 people. Data analysis on the results of field trials was comparing the results of field trials with the standard effectiveness of five’s scale. The results of this study show that the appearance of the digital evaluation tool and the percentage of its effectiveness through field trials was 81.73%, so the tool was categorized as good. The impact of this research on informatics observers/informatics experts is that they will know an innovative evaluation tool used to determine the dominant aspect determining the effectiveness of e-learning based on decision support system methods and Balinese local wisdom.
Volatility Analysis of Cryptocurrencies using Statistical Approach and GARCH Model a Case Study on Daily Percentage Change Sarmini, Sarmini; Widiawati, Chyntia Raras Ajeng; Febrianti, Diah Ratna; Yuliana, Dwi
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.261

Abstract

Cryptocurrency has become a significant subject in the global financial market, attracting investors and traders with its high volatility and profit potential. This study analyzes the daily volatility and GARCH volatility of six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), USD Coin (USDC), Tether (USDT), and Ripple (XRP). Daily percentage change data and GARCH volatility are analyzed over specific time periods. The analysis reveals that Bitcoin (BTC) has an average daily percentage change of 0.366%, while Ethereum (ETH) has 0.376%. Litecoin (LTC) shows a daily percentage change of 0.166%, whereas USD Coin (USDC) and Tether (USDT) have very low daily percentage changes, nearly approaching zero. In terms of GARCH volatility, Ethereum (ETH) stands out with a volatility of 0.198, followed by Bitcoin (BTC) with a volatility of 0.121. The study's results indicate that cryptocurrencies are vulnerable to extreme price fluctuations, evidenced by their asymmetry distribution and kurtosis. Volatility correlation analysis reveals significant relationships, important for risk management and portfolio diversification. These findings contribute to understanding cryptocurrency volatility characteristics and aid stakeholders in making informed investment decisions.
Implementation of Enhanced Axis Aligned Bounding Box for Object Collision Detection in Distributed Virtual Environment Elfizar, Elfizar; Herawan, Tutut; Sukamto, Sukamto
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.226

Abstract

Axis Aligned Bounding Box (AABB) is a popular method for object collision detection in distributed virtual environment (DVE). Actually, besides AABB several researchers have found better methods but those methods are not used in DVE. The simplicity of AABB is one of reasons. Another side, researchers have found several methods to make a scalable DVE to accommodate huge users. Object-based simulators architecture is one of them. It uses a simulator for an object in DVE. This paper aims to determine the comprehensive effects of implementing AABB in DVE and to enhance AABB implementation in DVE using object-based simulators architecture in order to be able to use other object collision detection methods in DVE. This research had four stages. First stage, we developed DVE application. The second stage was running AABB in DVE. In this stage, we also determined characteristics of AABB implemented in DVE. Third, we implemented AABB in object-based simulators architecture. This architecture used more simulators to manage objects. Finally, we analyzed and compared the results to the common existing DVE. There were three parameters measured in this research: runtime, frame rate and CPU usage of simulation application. The experiment results showed that computation complexity was the most important thing to be considered in DVE because DVE always updates its environment. Adding a few workloads into AABB degraded strictly performance of DVE. The results also showed that the enhanced AABB implementation in DVE using the object-based simulators architecture could rich the user experiences. The average runtime, frame rates and CPU usages using this architecture are 0.001 second, 239 fps and 20.94%, respectively. These achievements are better than common DVE. This novel approach could be used to implement other better collision detection algorithms in DVE such as oriented bounding box (OBB).
An Unsupervised Learning and EDA Approach for Specialized High School Admissions Paramita, Adi Suryaputra; Ramadhan, Arief
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.178

Abstract

This research investigates disparities in access and representation within specialized high school admissions processes, focusing on public middle schools in New York City. Leveraging a dataset by a non-profit organization dedicated to increasing diversity in specialized high school admissions, the study employs exploratory data analysis and unsupervised learning techniques to identify schools with high levels of underrepresentation and academic potential. The analysis reveals significant disparities in access to specialized high schools, with certain demographic groups and schools facing barriers to entry. Through k-means clustering, schools are categorized based on their academic performance and demographic composition, enabling targeted intervention strategies to address disparities in access and representation. The research proposes general use towards education, including on-campus interventions, awareness campaigns, and regional information sessions, aimed at fostering equitable access to specialized high school programs. This study contributes to the broader discourse on educational equity and offers valuable insights for policymakers, educators, and researchers seeking to promote diversity and inclusion within educational systems.

Page 19 of 52 | Total Record : 518

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