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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 25 Documents
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Search results for , issue "Vol 5, No 1: JANUARY 2024" : 25 Documents clear
A Comprehensive Data-Driven Analysis of Talent Supply using Delphi Method in Higher Vocational Education and Ethnic Minority Regions Lihua Huang; Sutthiporn Boonsong; Issara Siramaneerat; Thosporn Sangsawang; Pakornkiat Sawetmethikul; Rui Chen
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 Rina Refianti; Achmad Benny Mutiara; Ryan Arya Putra
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 Ratna Savitri; Firmansyah Firmansyah; Dworo Dworo; Muhammad Said Hasibuan
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
Statistical Analysis the Influence of Internal and External Factors on Entrepreneurial Intentions Tingbin Wen; Sutthiporn Boonsong; Issara Siramaneerat; Thosporn Sangsawang; Pakornkiat Sawetmethikul
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 Po Abas Sunarya; Untung Rahardja; Shih Chih Chen; Yung-Ming Lic; Marviola Hardini
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.
Statistical Approach to Evaluating the Efficacy of Career Guidance Programs on University Graduate Employability in China Li Guo; Thosporn Sangsawang; Piyanan Pannim Vipahasna; Matee Pigultong; Sulaganya Punyayodhin; Kanokwan Darboth
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.v5i1.172

Abstract

This study aimed to develop a career guidance model for improving employment ability among Chinese undergraduate students and assess the impact of this model on students’ employment ability. The research involved 17 Chinese experts and 100 instructors from 10 universities in Sichuan, China. The Delphi technique was employed to gather expert perspectives, while data on employment ability were collected using the College Student Employment Ability Questionnaire. The Cronbach's coefficient of the questionnaire is .869, and Cronbach's α .80 indicates excellent internal consistency, affirming the authenticity and credibility of the data in this study. Based on the statistical criteria defined from the results of the fourth-round inquiries, each Course needs to meet any two of the following conditions: arithmetic x ̅ 3.5964, Full Score Rate .1020, and Cronbach's α .3883 to be preliminarily retained. The results of the third-round expert inquiries show that the course offerings meet the Arithmetic x ̅ 3.3548 criteria, Full Score Rate .1987, and Cronbach's α .5590. The study found a significant improvement in students’ employment ability after participating in the model, with the average score increasing from 16.11 to 20.33. These results underscore the effectiveness of targeted career guidance in enhancing undergraduate students’ employment prospects. Most experts have passed all courses and course content by this round, with viable ideas identified. Career Education and Orientation received the highest response percentage (90.67%), followed by self-assessment (89.50%), industry-oriented skill development (87.50%), mentor support and networking (85.50%), industry insights and trend analysis (89.50%), job search and application assistance (90.80%), continuous review and improvement (87.50%), and follow-up counseling and support (89.50%).
Exploring the Impact of Discount Strategies on Consumer Ratings: An Analytical Study of Amazon Product Reviews Berlilana Berlilana; Arif Mu’amar Wahid; Dewi Fortuna; Alfin Nur Aziz Saputra; Galih Bagaskoro
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.v5i1.163

Abstract

This research delves into the influence of discount strategies on consumer ratings within the e-commerce landscape, particularly on Amazon. A logistic regression model assessed how discount percentages and product categories affect consumer ratings. The study followed a rigorous methodology, beginning with comprehensive data collection across diverse product categories on Amazon. This was succeeded by a detailed exploratory data analysis (EDA), data preprocessing, and subsequent model building. The model was then subjected to an extensive evaluation process, encompassing accuracy, precision, recall, F1-score, and ROC-AUC metrics. The evaluation revealed that the model achieved an accuracy of 74.94%, a precision of 72.69%, and a recall of 74.94%. The F1 score was calculated at 69.26%, and the ROC-AUC score was notably 78.24%. These metrics underscore the model’s capability to accurately predict consumer ratings influenced by discount strategies. Key findings highlighted the significant predictive power of discount percentages and specific product categories, particularly 'Home Kitchen', suggesting a complex relationship between discounts, product types, and consumer responses. Theoretically, the study enriches the understanding of consumer behavior in e-commerce, highlighting the nuanced impact of discount strategies on consumer satisfaction, especially in online retail contexts. For e-commerce businesses and marketers, the findings underscore the importance of strategically employing discount strategies and tailoring marketing approaches to specific product categories. This study emphasizes managing customer expectations and maintaining product quality alongside discounts. This research provides valuable insights for optimizing e-commerce strategies and paves the way for future investigations. It opens up avenues for further exploration into factors like product quality, brand reputation, shipping times, and the potential of consumer segmentation and sentiment analysis in enhancing marketing effectiveness. The study marks a significant contribution to the field by linking discount strategies with consumer ratings, using advanced data analytics to inform e-commerce practices in the digital age.
CO2 Emission Forecasting in Indonesia Until 2030: Evaluation of ETS Smoothing Prediction Models and Their Implications for Global Climate Change Mitigation Saepul Aripiyanto; Dewi Khairani; Ambran Hartono
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.v5i1.154

Abstract

The objective of this study is to predict CO2 emissions in Indonesia until 2030 utilizing the ETS smoothing prediction model in line with the pressing demand for viable climate change mitigation approaches. Through an assessment of the model's efficacy, several fundamental evaluation metrics have been identified. The research findings reveal that the Mean Absolute Error (MAE) stands at 146,154.40, presenting an overview of the average absolute disparity between the projected and actual CO2 emission values. The Mean Squared Error (MSE) of 21,838,251,772.37 characterizes the mean of the squared variances between projections and actual values, gauging the variability of predictive errors. The Root Mean Squared Error (RMSE) at 147,777.71, derived from the square root of MSE, reflects the degree of uncertainty in CO2 emission predictions. Simultaneously, the Mean Absolute Percentage Error (MAPE) of 7.24% provides an overview of the average percentage of absolute discrepancies between projections and actual values. Projections suggest that CO2 emissions could potentially reach 1 million tons in 2030. This evaluation furnishes an in-depth comprehension of the precision of the ETS smoothing model in the context of substantial emission escalation. The implications on the challenges of climate change mitigation become increasingly crucial, underscoring the immediacy of preemptive measures and sustainable policies. While the model delineates emission trends, it is imperative to acknowledge that these forecasts are subject to various influences, such as policy and technological shifts. Consequently, this study underscores the necessity for heightened awareness and the formulation of more efficacious policies to address the potential surge in CO2 emissions in the forthcoming years.
Unveiling Entrepreneurial Development in Data Science Using CCIP-PF Model and Statistical Analysis Junhua Zhong; Sutthiporn Boonsong; Issara Siramaneerat; Thosporn Sangsawang; Pakornkiat Sawetmethikul
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.v5i1.168

Abstract

This study aims to explore the intricacies of entrepreneurial development within the realm of data science, shedding light on both internal and external factors that play pivotal roles in shaping the entrepreneurial landscape. Through the lens of the CCIP-PF model and employing rigorous statistical analysis, this research endeavors to provide insights crucial for fostering entrepreneurial growth in this dynamic field. The objectives of this study are: 1)To develop the CCIP-PF model and establish an assessment index system for mental health literacy training in junior high schools; and 2)To apply the evaluation index system to junior high school mental health literacy training, thereby promoting the enhancement of educational quality. The sample group consisted of 17 experts who participated in discussions and generated 162 viewpoints on the constituent elements of evaluation for junior high school mental health literacy training. The methodology employed the Delphi method; the instrument utilized a qualitative assessment approach, employing questionnaires to ensure anonymity and provide reliable feedback. A questionnaire survey was conducted among 422 participants in Sichuan Province's relevant educational administrative authorities, middle school mental health education teachers, university lecturers and professors in mental health education, and psychological counselors. The response rate reached 96.2%. The study analyzed the data using mathematical statistics and SPSSAU22.0, focusing on the reliability of the entire questionnaire and its dimensions. The findings of this study are as follows:1)primary indicators at mean 4.794, SD = 0.473, IQR = 0.125; secondary indicators at mean 4.823, SD = 0.379, IQR = 0.25; tertiary indicators at mean 4.790, SD = 0.424, IQR = 0.302. A factor contribution rate of 74.175% demonstrates efficacy. 2)Empirical research was conducted in various districts of Zigong City, yielding outcomes that align with reality and meet the anticipated objectives.
Active learning on Indonesian Twitter sentiment analysis using uncertainty sampling Muhaza Liebenlito; Nur Inayah; Esti Choerunnisa; Taufik Edy Sutanto; Suma Inna
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.v5i1.144

Abstract

Nowadays, sentiment analysis research in social media is rapidly developing. Sentiment analysis typically falls under supervised learning, which requires annotating data. However, the annotation process for sentiment analysis tasks is notoriously time-consuming. Fortunately, an effective strategy to overcome this challenge has emerged, known as active learning. Active learning involves labeling only a small subset of the dataset, leaving the rest for annotation through sampling strategies. This study focuses on comparing two active learning strategies: random sampling and boundary sampling. These strategies are applied to machine learning models such as logistic regression and random forests. In addition, we present an evaluation of the model performance and data savings achieved by implementing these strategies in the context of traditional machine learning for sentiment analysis on Twitter. The dataset considered consists of two labels: positive and negative sentiments. The results of our investigation show that active learning can significantly reduce the amount of training data required, saving up to 65% of the total training data required to achieve peak model accuracy. The most successful model identified uses a random forest with a margin sampling strategy, yielding an accuracy of 81.12% and an F1 score of 88.60%. This research highlights the effectiveness of active learning strategies in sentiment analysis, demonstrating their potential to improve model performance and resource efficiency. The results underscore the viability of employing active learning methods, particularly the combination of random forest models with margin sampling, for more efficient sentiment analysis in social media.

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