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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 543 Documents
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Gold Prices Time-Series Forecasting: Comparison of Statistical Techniques Maryati, Indra; Christian, Christian; Paramita, Adi Suryaputra
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.135

Abstract

The fluctuation of gold prices throughout the year makes it difficult for both investors and regular individuals to predict the future value. The goal of this research is to utilize various statistical techniques, such as linear regression, naive bayes, and various types of smoothing algorithms, to predict the price of gold. The data used in this study was obtained from Kaggle and is from a 70-year time period. The results showed that using a single exponential smoothing method had the highest accuracy and precision, with a good MAPE score of 7.12%. This study is unique in that it compares multiple algorithms using data over a long time period, and it can be useful for investors and traders in making decisions related to gold prices. Additionally, it can also serve as a reference for future research studies.
A Comprehensive Data-Driven Analysis of Talent Supply using Delphi Method in Higher Vocational Education and Ethnic Minority Regions Huang, Lihua; Boonsong, Sutthiporn; Siramaneerat, Issara; Sangsawang, Thosporn; Sawetmethikul, Pakornkiat; Chen, Rui
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.171

Abstract

This study delves into the principles of structural reforms on the supply side of talent in higher vocational education, specifically focusing on the context of Guangxi, China, and extending its applicability to diverse ethnic regions. Embracing a data science approach, the research aims to develop a model grounded in theoretical foundations and policy considerations, offering insights to enhance the higher vocational education system and facilitate a high-quality talent supply. The research sample comprises 28 experts who contributed 182 perspectives on the constituent elements of higher vocational education reform in ethnic minority areas. Leveraging the Delphi method, the study employs qualitative evaluation methods through anonymous questionnaire surveys to ensure reliable feedback. A comprehensive survey includes 391 participants representing various stakeholders, such as the education department, teachers, industry experts, and students. Utilizing mathematical statistics and SPSS AU22.0 for data analysis, the study confirms that adaptation indicators meet established standards, aligning the theoretical model with measured data. Descriptive analysis and correlation testing of model variables reveal moderate to high average values, indicating a significant positive correlation between the scales. The study explores the layout of universities, major settings, curriculum systems, and talent cultivation as independent variables, with a focus on their influence on vocational talent cultivation. Additionally, it covers the demand side of talents, incorporating perspectives from the government, society, students, and parents. The analysis assesses the satisfaction of the supply side of higher vocational education, exploring specific manifestations of the contradiction between talent supply and demand. Through attribution analysis, the study concludes by proposing considerations for the supply-side structural reform of higher vocational education talents in Guangxi and similar ethnic regions. This research, rooted in data science methodologies, provides valuable insights for educational policymakers and practitioners. It sets the stage for further exploration into the dynamic interplay between data-driven decision-making and structural reforms in the higher vocational education landscape.
A Lexicon-Based Long Short-Term Memory (LSTM) Model for Sentiment Analysis to Classify Halodoc Application Reviews on Google Playstore Refianti, Rina; Mutiara, Achmad Benny; Putra, Ryan Arya
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.160

Abstract

The development of information and communication technology is developing very quickly, has made many new breakthroughs. One of these technological advances is in the health sector, the creation of telemedicine applications. During the Covid-19 pandemic, it is difficult for people to get access to health. Therefore, telemedicine applications are needed. Halodoc is one of the telemedicine applications that has successfully become the top health application on the Google PlayStore. The application has been used by more than ten million users throughout Indonesia and received a rating of 4.6. To be able to see ratings and satisfaction from the public, user reviews are needed. The very large number of reviews often contain errors, making them difficult to decipher. Based on this, this research aims to create a web application, which can classify user reviews of the Halodoc application, using a proposed lexicon-based Long Short-Term Memory (LSTM) Model. Application is built using the Flask framework and the Python programming language. Models are created and trained using the TensorFlow library. The results of the model evaluation get an accuracy of 85.3% with an average precision value of 85.3%, a recall value of 85.6% and an f1-score of 85.3%. The proposed LSTM model can be used to classify Halodoc review sentiment classes.
Information Security Measurement using INDEX KAMI at Metro City Savitri, Ratna; Firmansyah, Firmansyah; Dworo, Dworo; Hasibuan, Muhammad Said
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.152

Abstract

Information security is a crucial issue that affects the overall business process, therefore it must be protected and secured. This research was conducted to assess the information security risks at Metro City Communication and Information Office in a structured manner towards information assets in identifying efforts to reduce risks as part of the information security management program. The research method begins with defining the scope, collecting data and supporting documents, evaluating the Information Security Index (KAMI), determining scores in 7 security areas, where strengths/maturity and weaknesses/deficiencies will be identified in each security area. Finally, after obtaining the evaluation results, recommendations will be made. The Information Security Index (KAMI) is a computer-based tool in excel format that can assess and evaluate the completeness and maturity level of information security implementation based on the SNI ISO/IEC 27001 criteria that describe the readiness of the information security framework. The data obtained by the researcher is based on interview results, examination of the availability of Information Security Management System (SMKI) documents, and evidence of SMKI implementation records/archives. The dashboard evaluation results for electronic system category score 17, which is in the high category, governance score is 69, risk management score is 29, framework score is 33, information asset management score is 69, technology score is 81 and supplement score is 0%. Based on verification of the results of the KAMI Index version 4.2 assessment file, a score of 275 was obtained, indicating that information security
Automated Class Attendance Management System using Face Recognition: An Application of Viola-Jones Method Widjaja, Andree E; Harjono, Nathanael Joshua; Hery, Hery; Mitra, Aditya Rama; Haryani, Calandra Alencia
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.133

Abstract

Over the past few years, face recognition has been widely used to help human activities in various sectors, including the education sector. By using facial recognition, the class attendance system at universities can be significantly improved. For example, students are no longer asked to sign attendance sheets manually, but attendance can be taken, recorded, and managed automatically through an integrated class attendance management system using facial recognition. The recorded data can then be further analysed to produce useful information for instructors and administrators. In turn, this arrangement will assist them in making decisions about matters relating to student attendance. The main objective of this research is to develop an automatic class attendance management system using facial recognition. In particular, the system we propose was developed using a prototyping software development approach and was modelled using UML version 2.0. As a choice of methods and tools, we used the Viola-Jones method as a face detection algorithm, Python and PHP as programming languages, OpenCV as the computer vision library, and MySQL as the DBMS. The results obtained from a number of black box tests carried out were a fully functional automatic class attendance system prototype using facial recognition.
User Interface Design for DIVAYANA Evaluation Application Based on Positive-Negative Discrepancy Divayana, Dewa Gede Hendra; Suyasa, P. Wayan Arta; Ariawan, I Putu Wisna; Mariani, Ni Wayan Rena; Sugiharni, Gusti Ayu Dessy; Gama, Adie Wahyudi Oktavia
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.136

Abstract

This study aims to show the user interface design form of the DIVAYANA evaluation application based on Positive-Negative Discrepancy. The method in this research is a development method that uses the Borg and Gall model. The development refers to the design stage, initial design trials, and revisions to initial design trials. Tests on user interface design involved 104 respondents. The instrument was a questionnaire consisting of 15 questions. Analysis of the trial data used a quantitative descriptive technique. The results of the study show that the quality of the user interface design is quite good. The impact of the results of this research on educational evaluators is that there is new knowledge about the existence of a user interface design that is important to know to support the realization of physical quality evaluation applications.
Statistical Analysis the Influence of Internal and External Factors on Entrepreneurial Intentions Wen, Tingbin; Boonsong, Sutthiporn; Siramaneerat, Issara; Sangsawang, Thosporn; Sawetmethikul, Pakornkiat
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.167

Abstract

This study aimed to explore and analyze the internal and external factors influencing statistical analysis the influence of internal and external factors on entrepreneurial intentions. The specific focus was on conducting an in-depth analysis of how these factors manifest within the data science demographic. The study involved a sample group of 432 university students, employing an anonymous questionnaire to gather reliable feedback and achieving a commendable response rate of 93%. Through an established random sampling scheme, 402 valid responses were obtained for data analysis. The data processing and analysis were conducted utilizing SPSS software, incorporating descriptive statistics, hypothesis testing, and multiple regression analysis to uncover insights within the data science context. The study yielded significant results: 1) Gender emerged as a robust variable with a significant t-value=3.28 and a low p-value = .001, indicating a notable gender-based disparity in entrepreneurial intention among students in the data science domain. Work experience also exhibited noteworthy t and p-values (t = -2.45, p = .015), emphasizing the influential role of prior work experience on students' entrepreneurial inclination within the data science field; 2) A comprehensive examination of data related to determinants of university students' entrepreneurial intention revealed distinct differences in the realm of individual traits (personality: ????̅ = 3.94, SD. = .74; values: ????̅ = 4.01, SD. = .70; motivation: mean = 3.87, SD. = .74), social-cultural influences (????̅ = 3.89, SD. = .70), family (????̅ = 3.78, SD. = .86), peers (????̅ = 3.77, SD. = .72), mentors (????̅ = 3.72, SD. = .89), dimensions related to data science entrepreneurship education (innovation education: ????̅ = 3.80, SD. = .87; training: ????̅ = 3.76, SD. = 0.94; courses: ????̅ = 3.71, SD. = .93), and economic environmental factors (financial pressures: ????̅ = 3.93, SD. = .77; financing: ????̅ = 3.89, SD. = .72; market opportunities: mean = 3.83, SD. = .80) exhibited pronounced trends towards convergence within the data science sector. These findings highlight the necessity of comprehensively considering multiple interconnected factors specific to data science in fostering entrepreneurial spirit among university students; 3) All secondary indicators of the four hypothesized factors - individual traits, social support, data science entrepreneurship education, and economic environment - were significant at the .01 level (p .01), affirming positive correlations between all hypothesized factors and the dependent variable of entrepreneurial intention within the data science context.
Deciphering Digital Social Dynamics: A Comparative Study of Logistic Regression and Random Forest in Predicting E-Commerce Customer Behavior Sunarya, Po Abas; Rahardja, Untung; Chen, Shih Chih; Lic, Yung-Ming; Hardini, Marviola
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.155

Abstract

This study compares Logistic Regression and Random Forest in predicting e-commerce customer churn. Utilizing the E-commerce Customer dataset, it navigates the complexities of customer interactions and behaviors, offering a rich context for analysis. The methodology focuses on meticulous data preprocessing to ensure data integrity, setting the stage for applying and evaluating Logistic Regression and Random Forest. Both models were assessed using accuracy, precision, recall, F1-Score, and AUC-ROC. Logistic Regression showed an accuracy of 90%, precision of 91% for class 0 and 82% for class 1, recall of 98% for class 0 and 50% for class 1, F1-Score of 94% for class 0 and 62% for class 1, and AUC-ROC of 0.88. Random Forest, with its ability to handle complex patterns, demonstrated higher overall performance with an accuracy of 95%, precision of 95% for class 0 and 93% for class 1, recall of 99% for class 0 and 74% for class 1, F1-Score of 97% for class 0 and 82% for class 1, and an AUC-ROC of 0.97. This comparative analysis offers insights into each model's strengths and suitability for predicting customer churn. The findings contribute to a deeper understanding of machine learning applications in e-commerce, guiding stakeholders in enhancing customer retention strategies. This research provides a foundation for further exploration into the digital social dynamics that shape customer behavior in the evolving digital marketplace.
Studying Electricity Load Forecasting and Optimizing User Benefits with Smart Metering Jumaa, Shereen Sadeq
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.147

Abstract

Accurate energy projections and optimal utilization of resources require the consideration of real-time variations in demand-side response components. Innovative ultra-short-term power load forecasting approaches such as CNN-BiLSTM-Attention, CNN-LSTM, and GRU models are used to assess the load level and predict daily raw load curve. The study shows that by incorporating predicted raw loads and two types of customer reactions influenced by average reduction rate under different energy efficient classes, wholesale market price fluctuations can be minimized through retail-to-wholesale market connection using demand-side responses. This helps diminish both frequency and amplitude of sudden changes in prices for wholesalers while taking into account an average overall usage pattern based on user class resource consumption rates.
Implementation of PageRank Algorithm for Visualization and Weighting of Keyword Networks in Scientific Papers Lubis, Adyanata; Prasiwiningrum, Elyandri
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.138

Abstract

Papers are written works that contain thoughts about a particular problem or topic that are written systematically accompanied by logical analysis. Scientific papers are often found on the internet or in libraries for various titles of scientific papers, citations or references can be found in every scientific paper and can be obtained easily, but to display all citations in scientific papers in the form of visualization cannot be done easily. Visualizing the citation network of scientific papers in the form of a graph, with nodes representing research papers and edges representing the relationship between researchers' scientific papers and other scientific papers based on scientific paper citations. This research uses the pagerank algorithm to create a keyword network that has a high relationship and potential relevance in a data library. This research is the first research in using the pagerank algorithm and testing its accuracy by comparing with KNN and linear clustering. The presentation displays the citation of scientific papers based on the size of the node by showing the number of citations of the scientific paper. It can be concluded that all processes in the system have run according to design, and functionally the visualization system and weighting of the scientific paper citation network are in accordance with the design. The results obtained from 51 articles, this algorithm produces a visual user interest of 81.60%, compared to the accuracy of the data suitability produced by the linear clustering and KNN algorithms in the form of 71.22% and 61.34%, helping to facilitate the search for scientific papers in large quantities.

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