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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 5 Documents
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Search results for , issue "Vol 2, No 4: DECEMBER 2021" : 5 Documents clear
Implementation of ANN and GARCH for Stock Price Forecasting Mayatopani, Hendra
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.41

Abstract

For simulating intricate goalfunctions, neural networks are a technology that is employed in artificial intelligence. The usage of artificial neural networks is becoming more popular.(ANNs) to certain sorts of tasks, for example learning to comprehend complicated sensor data collected in the real world, is one of the most effective methods of learning approaches available. The usage of time series models in financial time series prediction has grown significantly over the past decade, and their relevance in this area continues to expand. To be more specific, the goal of this research is to determine whether neural networks have the ability to predict financial time series in general, or, more specifically, whether they have the ability to predict future patterns i The stock market in the United States is characterized by the European Union, and Brazil, among other things. They are compared to a well-known forecasting approach, generalized autoregressive conditional heteroskedasticity, in this research, and their accuracy is shown to be superior (GARCH). Aside from that, the optimal network design for each data sample is developed for each data sample. According to this article, ANNs are capable of forecasting the stock markets under examination, and their resilience may be increased by varying the network topology utilized to construct them. Aside from that, the results of this research demonstrate that ANNs outperform GARCH models in terms of efficiency of statistical performance.
Soil Infiltration Rate Impact on Water Quality Modeled Using Random Forest Regression Sopandi, Ajang
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.42

Abstract

In this paper, Infiltration rate of the soil is investigated by using predictive models of Random forest regression and their performance were compared with Artificial neural network (ANN) and M5P model tree techniques. We utilized 132 field measurements comprising this dataset. 88 models were trained using observations, while the remaining 44 were used to validate it. The cumulative time (Tf), the impurity type (It), the impurity concentration (Ci), and the moisture content (Wc) were utilized as input variables, and the rate of infiltration was employed as the output. To evaluate the efficiency of the two modeling methodologies, correlation coefficients we estimated root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative square error are all terms that may be used to describe errors (RRSE). The random forest regression approach outperforms the other two models when compared to evolutionary data (ANN and M5P model tree). Using a random forest as a model, regression can properly estimate the infiltration rate within a 25% error range. According to the results of the sensitivity research, cumulative time plays an important influence in determining the soil's penetration rate.
Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5 Aini, Qurrotul; Hammad, Jehad A H; Taher, Taslim; Ikhlayel, Mohammed
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.43

Abstract

Congestion seems to be a daily occurrence in the Indonesian city of Jakarta. As a consequence, the rider has access to essential information regarding traffic conditions at all times, which is beneficial. Through social media platforms such as Twitter, this information is readily available to the public. On the other hand, the information offered on Twitter is still uncategorized text. DKI Jakarta, as a consequence, developed a congestion classification system that included data mining techniques, a classification approach based on the decision tree technique, and C4.5 as a component. This C4.5 method transforms a large amount of information into a decision tree that shows the rules. Geocoding will be utilized to illustrate the locations that have been gathered, and a data split with a confusion matrix will be used to assess how well the categorization process has worked. According to the study's results, the average accuracy rate is 99.08 percent, the average precision rate is 99.46 percent, and the average recall rate is 97.99 percent.
An Ensemble and Filtering-Based System for Predicting Educational Data Mining Hananto, Andhika Rafi; Rahayu, Silvia Anggun; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.44

Abstract

When developing a prediction paradigm, an ensemble technique such as boosting is used. It is built on a heuristic framework. Generally speaking, engineering ensemble learning is more accurate than individual classifiers when it comes to making predictions. Consequently, numerous ensemble strategies have been presented in this work, particularly to provide a more complete understanding of the essential methods in general. Researchers have experimented with boosting methods to forecast student performance as part of a variety of ensemble techniques. The researchers employed improvement approaches to construct an accurate predictive educational model, which was based on a key phenomena seen in categorization and prediction operations. In light of the uniqueness and originality of the suggested strategy in educational data mining, the researchers used augmentation strategies in order to construct an accurate predictive pedagogical model. Tenfold cross-validation was performed to evaluate the effectiveness of the basic classifiers, which included the random tree, the j48, the knn, and the Naive Bayes. The random tree was found to be the most effective classifier. Several additional screening techniques, including oversampling (SMOTE) and undersampling (Spread subsampling), were utilized to analyze any statistically significant variations in results between the meta and base classifiers that had been identified between the meta and base classifiers. The use of ensemble and screening strategies, as compared to the use of standard classifiers, has demonstrated considerable gains in predicting student performance, as has the use of either strategy alone. Furthermore, after the completion of a performance research on each approach, two new prediction models have been established on the basis of the improved results gained thus far.
An Exemination Of The Effects Of Service Quality And Satisfaction On Customers Behavior Intentions In E-Shoping: An Empirical Study With Comparision Of Taiwan And Vietnam Tran, Van-Dat; Nguyen, Minh Dung; Linh Vo, Thi Ngoc; Dinh, Thu Quynh
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.45

Abstract

The purpose of this paper to examine the relationship among e-service quality and e-satisfaction and behavioral intention, to reveal any difference across Taiwan and Vietnam. Data from a survey of 891 online consumers, 409 respondents in Taiwan and 482 respondents in Vietnam were used to test the research model. Confirmatory factor analysis was conducted to examine the reliability and validity of the measurement model, and the structural equation modeling technique was used to test the research model. Data analysis involved the comparison of two models using structural equations modeling. The prevailed model reveals that e-service quality has a positive effect on e-satisfaction in both Taiwan and Vietnam, explored the influence of customer e-satisfaction on behavioral intention in Vietnam and Taiwan. E-service quality played a stronger positive role for online shoppers in Taiwan as compared to their counterparts in Vietnam. Such differences in determinants of customer satisfaction may due to the market contexts in different parts of the world.

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