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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 16 Documents
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Search results for , issue "Vol 4, No 3: SEPTEMBER 2023" : 16 Documents clear
Data Analytics of Online Lessons in Social Studies and Buddhism: Enhancing Dhamma Teaching and Tripitaka Understanding Among Teachers and Students Luaensutthi, Aammuay; Sangsawang, Thosporn
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.125

Abstract

The objectives were to (1) determine the effectiveness of online lessons of Social Studies and Buddhism on Dhamma’s teaching regarding Tripitaka for teachers; (2) compare the pre-test and post-test achievements of teachers and primary school 6 (Grade 6) students; 3) examine the satisfaction of teachers and students using online lessons of Social Studies and Buddhism on Dharma’s teachings according to the Tripitaka. The samples were 12 teachers, and 30 students studying primary school 6 (Grade 6) at Wat Proifon School. The instruments were online lessons of the Social Studies and Buddhism course on Buddha's Teaching Tripitaka, pre-test and post-test, and the questionnaire of teachers’ and students’ satisfaction towards studying the online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding the Tripitaka.Statistics used were percentage, mean, standard deviation, and t-test for dependent samples. The findings revealed that the efficiency of online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding Tripitaka was 81.92/80.83 on average based on the criteria. The teachers’ learning achievements after using online lessons in the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test 11.40, SD.=1.51, while the average score of the post-test was 18.17, SD.=1.10, and the t-test between   the pre-test and post-tests was 6.77, which were significantly distinctive at the level of .05., and the students’ learning achievements after using online lessons on the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test: 10.40, SD.=1.61, while the average score of the post-test was 16.17, SD.=1.11, and the t-test between the pre-test and post-tests was 5.77, which were significantly distinctive at the level of .05. Teachers' satisfaction was at high level with an average of 4.47, SD.=.55, and the students’ satisfaction gained a very high level with an average of 4.50, SD.=.44.
Ensemble learning techniques to improve the accuracy of predictive model performance in the scholarship selection process Buslim, Nurhayati; Zulfiandri, Zulfiandri; KyungOh, Lee
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.112

Abstract

Ensemble Learning is an algorithm that searches for the best prediction result based on several classifier solutions which are come from different algorithms. Ensemble learning has better accuracy and performance compared to other algorithms because this method uses several learning algorithms to achieve better predictive solutions. There are a lot of data that the scholarship organizer receives and manages. This makes the process take a lot of time to do checking process and makes it inefficient. Accelerating the process while also maintaining the accuracy of the scholarship selection process can certainly improve the selection performance. In this study, we process student data from UIN Jakarta University as a sample. The model will have 2 base classifiers, namely Support Vector Machine (SVM) and Key Nearest Neighbor (KNN). Each of these algorithms already has quite a good accuracy. Using Ensemble Learning improves the model performance because it has the ability to overcome errors that occur in each data prediction. We can exploit the classification application created using "Streamlit" and will determine whether a student is accepted or rejected in the scholarship selection process. Our model and application can be used by academics as a Decision Support System (DSS) for determining scholarship recipients. This model can also be used as a development for institutions in the academic field.
A Comparative Study of Feature Selection Techniques in Machine Learning for Predicting Stock Market Trends Paramita, Adi Suryaputra; Winata, Shalomeira Valencia
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.99

Abstract

This study aims to compare the effectiveness of three feature selection techniques, namely Principal Component Analysis (PCA), Information Gain (IG), and Recursive Feature Elimination (RFE), in predicting stock market conditions. This research uses three distinct Kaggle datasets that contain data for predicting stock market values. The results show that RFE performs better than PCA and IG in predicting market value with fairly precise accuracy. By using the RFE technique, this study was able to identify the most influential features in prediction, reduce the dimensionality of the data, and improve the performance of the prediction model. These provide significant benefits in the world of stocks, including improved investment decisions, reduced investment risk, improved trading strategy performance, and identification of promising investment opportunities. For future research, further comparative studies between other feature selection techniques can be conducted. This research has novelty in several aspects. First, it applies different feature selection techniques, namely Principal Component Analysis (PCA), Information Gain (IG), and Recursive Feature Elimination (RFE), in the context of stock market prediction. Utilizing these techniques to select the most relevant features in predicting stock market conditions provides a deeper understanding of the influence of these features on stock price movements. Furthermore, this research utilizes different datasets from Kaggle, which represent various stock market value predictions. The utilization of diverse datasets provides variation in the data and allows this research to examine the performance of feature selection techniques in multiple stock market contexts. In conclusion, this research provides insight into the effectiveness of feature selection techniques in stock market value prediction. It also provides actionable guidance for market participants to improve investment decisions and trading performance in the stock market.
Assessing Factors and Simulating Innovation: A Study of Innovative Capacities Among Data Science Professionals in China Zhang, Yongfeng; Sangsawang, Thosporn; Vipahasna, Piyanan Pannim
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.123

Abstract

This study aims to analyze the multifaceted factors influencing the innovative capabilities of data science professionals in China and assess the impact of simulations on their innovative skills. The sample comprises seventeen experts who actively participated in discussions and provided 36 perspectives on the factors affecting their innovation abilities. The research methodology utilized the Delphi method, involving four rounds of questionnaires distributed to 363 data science professionals to evaluate the factors affecting their innovation capacity. The data was rigorously analyzed using mathematical statistics and SPSS, with a strong emphasis on questionnaire validity and reliability. In the reliability analysis, Cronbach's α was found to be 0.98, indicating a high level of internal consistency. The research results yielded an average score of 4.79, SD = 0.39, IQR = 1, reflecting a strong consensus among experts in agreement with the research findings. Exploratory factor analysis was employed for validity assessment, revealing that the 12th factor accounted for a cumulative variance explanation rate of 76.54%, exceeding the threshold of 60%, signifying the robust structural validity of the questionnaire data. The study also utilized AMOS software to simulate sample data and assess the influence coefficients of individual, organizational, and family characteristics on innovation capacity, resulting in values of 0.53, 0.39, and 0.22, respectively, all greater than 0, indicating favorable influence relationships. Building upon these findings, a comprehensive model of creativity abilities among Chinese data science professionals is proposed. This research critically examines the innovation potential of data science professionals in Chinese academia, with the overarching goal of enhancing their creative skills and competitiveness within the data science field. Additionally, it lays the theoretical groundwork for fostering innovation within the university setting.
Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework Paramita, Adi Suryaputra; Prabowo, Harjanto; Ramadhan, Arief; Sensuse, Dana Indra
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.117

Abstract

Information systems research for non-profit organizations is an opportunity to make a contribution to the field of information systems, the adoption of information systems in this field is relatively tedious and there are few studies that examine this area; consequently, there are several research gaps in the domain of non-profit organizations that need to be solved. This research will focus on the development of data warehouse architecture and business intelligence for non-profit organizations. In this study, the Soft Systems Methodology (SSM) technique will be employed to develop a data warehouse architecture and business intelligence. This research will interview twenty individuals to collect primary data, review organizational policy documents, and conduct an open-ended survey. The obtained data will then be qualitatively analyzed, resulting in the formation of rich picture diagrams, CATWOE analysis, and conceptual models, which will ultimately form a data warehouse architecture and business intelligence. This research has produced a microservices-enhanced data warehouse architecture and business intelligence for non-profit organizations.
Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal Wibawa, Aji Prasetya; Utama, Agung Bella Putra; Lestari, Widya; Saputra, Irzan Tri; Izdihar, Zahra Nabila; Pujianto, Utomo; Haviluddin, Haviluddin; Nafalski, Andrew
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.97

Abstract

Sessions or unique visitors is the number of visitors from one IP who accessed a journal portal for the first time in a certain period of time. The large number of unique daily average subscriber visits to electronic journal pages indicates that this scientific periodical is in high demand. Hence, the number of unique visitors is an important indicator of the accomplishment of an electronic journal as a measure of the dissemination in accelerating the journal accreditation system. Numerous methods can be used for forecasting, one of which is the backpropagation neural network (BPNN). Data quality is very important in building a good BPNN model, because the success of modeling at BPNN is very dependent on input data. One way that can be carried out to improve data quality is by smoothing the data. In this study, the forecasting method for predicting time series data for unique visitors to electronic journals employed three models, respectively BPNN, BPNN with mean smoothing, and BPNN with median smoothing. Based on the findings, the results of the smallest error were obtained by the BPNN model with a mean smoothing with MSE 0.00129 and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture which can be used as a forecast for unique visitors of electronic journals.

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