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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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Statistical Approach to Evaluating the Efficacy of Career Guidance Programs on University Graduate Employability in China Guo, Li; Sangsawang, Thosporn; Vipahasna, Piyanan Pannim; Pigultong, Matee; Punyayodhin, Sulaganya; Darboth, Kanokwan
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; Wahid, Arif Mu’amar; Fortuna, Dewi; Saputra, Alfin Nur Aziz; Bagaskoro, Galih
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 Aripiyanto, Saepul; Khairani, Dewi; Hartono, Ambran
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.
Text Mining Application With K-Means Clustering to Identify Sentiments and Popular Topics: A Case Study of The Three Largest Online Marketplaces in Indonesia Widjaja, Andree E; Fransisko, Andy; Haryani, Calandra Alencia; Hery, Hery
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.v4i4.134

Abstract

The number of internets and social media users, which continues to increase at a very fast rate, has resulted in the emergence of new business opportunities in Indonesia. One of those indications is the emergence of marketplace companies in Indonesia. The presence of these online marketplaces provides people with more online marketplace choices according to their preferences. One of the factors that became the basis for this election was reading comments or reviews from consumers on the marketplace posted on social media. This research was conducted using text mining method with k-means clustering algorithm to systematically identify the sentiments and topics that are widely discussed by online marketplace consumers in Indonesia. The data was processed by the k-means algorithm in the form of comments or reviews from three online marketplaces (Tokopedia, Shopee and Bukalapak) on Twitter. The amount of data for each marketplace referred to was 1500 data tweets. The results showed that the three online marketplaces were associated to different topics, even though they are in the same industry. These differences arise due to the fact that most consumers discuss the topics of programs held by their respective online marketplaces. The main topics related to Tokopedia are “belanja” (“shopping”) and “terimakasih” (“thank you”); while for Shopee “pilih” (“choose”) and “jongho”, and for Bukalapak “pra-kerja” (“pre-employment”). In addition, the sentiment analysis carried out shows that the sentiment of the three online marketplaces is predominantly neutral.
Applied Regression Modelling to Recommend Microfinance Development Policies Huy, Nguyen Quoc; Nga, Lu Phi; Tam, Phan Thanh
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.v4i4.139

Abstract

Microfinance plays an essential role in the socio-economic development of each country through support for poverty reduction. In Vietnam, hunger eradication and poverty reduction under the National Target Program have received attention and implementation in recent years. However, during 2020-2021, Vietnam had several difficulties and hurdles for microfinance organizations, exacerbated by the Covid-19 outbreak, which was hurting the country and all sectors of social life. Microfinance is an excellent instrument for long-term poverty reduction since it teaches the poor how to do business and save and provides essential information. However, microfinance has not yet reached its full potential in our nation. One of the suggested reasons is the legal framework impediment. Thus, the research examines the State's policies for microfinance operations using a survey of 260 staffs related to microfinance activities from 30 microfinance organizations and 30 commercial banks in Vietnam, with data processed using SPSS 20.0. Finally, the study's value suggests ideas for removing barriers to continued microfinance activity development in Vietnam.
Unveiling Entrepreneurial Development in Data Science Using CCIP-PF Model and Statistical Analysis Zhong, Junhua; 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.v5i1.168

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 Liebenlito, Muhaza; Inayah, Nur; Choerunnisa, Esti; Sutanto, Taufik Edy; Inna, Suma
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.
Applying Quantitative and Data Analysis using Structural Equation Modeling for Accessing Factors Influencing Employee Loyalty Tam, Nguyen Thanh; Truc, Ngan Mai Thi; Thanh, Ha Le Thi
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.v5i1.151

Abstract

This study aims to measure the impact of factors on the loyalty of employees at small and medium enterprises (SMEs) in Ho Chi Minh City through interviews with 640 surveys with a non-probability sampling method, a convenient approach. Quantitative analysis techniques used in the study include reliability analysis of the scale through Cronbah's Alpha index, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and Structural Equation Modeling (SEM).From the results of SEM analysis of the whole model, it is shown that the fit of the tested model is relatively high, satisfying the conditions that are CMIN = 1.975 3; RMSEA = 0.043 0.05; CFI = 0.924 0.9; GFI = 0.823 0.8, Pclose = 1.00 0.05; and Pvalue = 0.000 0.05. Besides, the SEM analysis results also show that independent factors(1) Opportunities for training and resource development; (2) Direct employee involvement; (3) Support from superiors and colleagues; (4) Environment and working conditions; (5) Salary, allowances, and benefits; (6) Fair rewards; (7) Alignment with organizational goals; (8) Level of empowerment;  have a positive impact on employee job satisfaction at 1% significance level. At the same time, the job satisfaction factor in the employee's work positively impacts employees' loyalty to the organization at the 1% significance level. Accordingly, independent factors explain 72.10% of the change in job satisfaction in the employee's work; the remaining 37.90% of the change is explained by other factors. In addition, job satisfaction in employees' work explained 63.1% of the shift in employee loyalty to the organization, and 36.9% of the change was explained by factors other than the model. From the research results, the author proposes managerial implications to increase the loyalty of employees in SMEs.The study has some limitations: The study only surveyed 640 employees representing SMEs in HCMC, and the sample representativeness is not high. The governance implications are still qualitative, and obstacles to implementing the above impacts have not been assessed. The research sample uses a non-probability sampling method, so the representativeness of the study population is not high.
Multiple Choice Question Difficulty Level Classification with Multi Class Confusion Matrix in the Online Question Bank of Education Gallery Siregar, Pariang Sonang; Hatika, Rindi Genesa; Hayadi, B. Herawan
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.v4i4.132

Abstract

The importance of test question planning as a critical element in improving the quality of education is undeniable as it helps teachers evaluate student understanding. The creation of questions must consider the level of difficulty, which is often divided into three categories: easy, medium, and difficult. Predicting the difficulty level of questions has great importance as it helps teachers create test questions that match students' abilities. In this study, we view the identification of item difficulty as a classification problem. The data used includes questions from elementary and junior high school, with various machine learning methods applied to perform classification. We tested Random Forest, Logistic Regression, SVM, Gaussian, and Dense NN, considering embedding, lexical, and syntactic features. The evaluation results show that the best method in identifying the difficulty level of questions in subjects is using Random Forest, resulting in an accuracy of 84%. Meanwhile, in other cases, the best method is also Random Forest, with an accuracy of 80%. Our research shows that the use of feature embedding and TF-IDF has a significant positive impact on the accuracy of the resulting model.
Quantitative Analysis of Educational Techniques for Psychological Development in Vocational Students in China Li, Shuang; Sangsawang, Thosporn; Thepnuan, Narumom; Pigultong, Matee; Punyayodhin, Sulaganya; Darboth, Kanokwan
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.v5i1.173

Abstract

The research of objective were to: 1) examines environment, educational system, teacher-student relationship, self-awareness, and other aspects affecting Chinese vocational school students' psychological quality., and 2) development Psychological Quality for Vocational School Students in China Model for address unique psychological challenges and foster personal development in vocational education. Populations and sampling group were stents tests 7,000 Zigong, Rong County, and Dujiangyan vocational and technical students. The questionnaires used a percentage- based scoring standard, with a score below 50 indicating “strongly disagree,” 51 to 70 indicating “neutral,” 71 to 90 indicating “moderately agree,” and 91 to 100 indicating “strongly agree.” Data processing affects Zigong, Rong County, and Dujiangyan Chinese vocational school students' mental health. Statistical percentage of students picking each option. Guttman half coefficient was .802 after Split-Half Method testing of the data, indicating good split-half reliability and internal consistency. The questionnaire reveals how survey questions, sample size, and data processing affect Chinese vocational school students' mental health. The questions asked Zigong, Rong County, and Dujiangyan vocational and technical school students about mental health. 4,768 people completed 6,458 surveys. After deleting 97 low-reliability questionnaires with similar answers to seven consecutive items, 4,671 were valid. The Countermeasure Developing Model in China enhances the psychological quality of vocational school students by implementing multi-level therapy, methodical mental health education, and a supportive learning environment.

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