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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 115 Documents
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A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications Lichung Jen; Yu-Hsiang Lin
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.38

Abstract

Many business applications rely on their history data to anticipate their company future. The marketing products process is one of the essential procedures for the firm. Customer needs supply a useful piece of information that helps to promote the suitable products at the proper moment. Moreover, services are recognized recently as products. The development of education and health services is reliant on historical data. For the more, lowering online social media networks problems and crimes need a big supply of information. Data analysts need to utilize an efficient categorization system to predict the future of such businesses. However, dealing with a vast quantity of data demands tremendous time to process. Data mining encompasses numerous valuable techniques that are used to anticipate statistical data in a number of business applications. The classification technique is one of the most extensively utilized with a range of algorithms. In this work, numerous categorization methods are revised in terms of accuracy in diverse domains of data mining applications. A complete analysis is done following delegated reading of 20 papers in the literature. This study intends to allow data analysts to identify the best suitable classification algorithm for numerous commercial applications including business in general, online social media networks, agriculture, health, and education. Results reveal FFBPN is the best accurate algorithm in the business arena. The Random Forest algorithm is the most accurate in categorizing online social networks (OSN) activity. Naïve Bayes method is the most accurate to classify agriculture datasets. OneR is the most accurate method to classify occurrences inside the health domain. The C4.5 Decision Tree method is the most accurate to classify students’ records to forecast degree completion time.
SHA-512 Algorithm on Json Web Token for Restful Web Service-Based Authentication Naufal Rasyada
Journal of Applied Data Sciences Vol 3, No 1: JANUARY 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i1.51

Abstract

The development of technology is getting faster and continues to grow so as to create various types of technology, architecture, to new programming languages. Surely this will be a new problem because of differences in technology, programming language, and architecture that must still be able to provide interconnected sources of information. So in order for the system to remain integrated, a Web Service (WS) is needed as a bridge in integrating between systems without differentiating the platform, programming language, or architecture used. One of the Web Service architectures that is widely used is REST (REpresentational State Transfer), but there will be problems in implementing REST Web Service because it does not have security standards in the authentication process. Then an authentication method is needed, namely JSON Web Token (JWT). In implementing JWT, a hash algorithm is needed, such as SHA-512. The results of this study indicate that the use of SHA-512 on the JWT has a good speed with an average data request speed of 512.8 milliseconds (ms) when compared to the SHA-256 algorithm which has an average data request speed of 515.55 MS. Meanwhile, in terms of data size, SHA-512 produces an average data request size of 0.75 kilobytes (kb) compared to SHA-256 which has an average data request size of 0.72 kb.
Implementation of ANN and GARCH for Stock Price Forecasting Hendra Mayatopani
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.
Knowledge Management Strategy by Means of Virtualization in Covid-19 Eissa Mohammed Ali Qhal
Journal of Applied Data Sciences Vol 1, No 2: DECEMBER 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v1i2.46

Abstract

In an era where companies, governmental programs, and the economy are knowledge-driven, they must understand the best ways to apply knowledge management to yield better results. Fortunately, technology is continuously evolving to better cope with knowledge management's arising challenges by innovating better problem-solving models. Cloud computing is one of the many technologies today that has revolutionized how different companies and economic sectors treat knowledge management by enabling this Knowledge's virtualization. The paper focuses on knowledge management transition to virtualization technology in supporting businesses during the COVID-19. Pandemic times despite many social restrictions being put in place to contain the virus. The text further presents the results of a study involving major firms in different sectors and how their applied virtualization technology is yielding results even during the toughest of times in any company or economy.
Improved Expectation-Maximization Algorithm for Unknown Reverberant Audio-Source Separation Shaher Slehat
Journal of Applied Data Sciences Vol 3, No 1: JANUARY 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i1.52

Abstract

The problem of undecided Separating reverberant audio sources is crucial for speech and audio processing. Numerous separation strategies have been developed to solve this problem; however, all of them estimate model parameters in the time–frequency domain, resulting in permutation ambiguity and poor separation performance. Additionally, one of the main challenges with existing expectation–maximization (EM) strategies is the time needed for each iterative step to update the model parameters. In this article, we offer an enhanced EM approach that combines nonnegative matrix factorization (NMF) with time differences of arrival (TDOA) estimations while eliminating time expenditure to the EM algorithm's starting values being appropriately selected. The suggested approach avoids permutation ambiguity by using the NMF source model, and acoustic localization is accomplished by converting the TDOA. Following that, model parameters are changed to improve separation outcomes. Finally, Wiener filters are used to separate the source signals. The experimental findings indicate that the suggested algorithm outperforms current blind separation approaches in terms of source separation.
Soil Infiltration Rate Impact on Water Quality Modeled Using Random Forest Regression Ajang Sopandi
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.
The Role of Methods and Applications of Artificial Intelligence Tools in the Field of Medicine to Diagnose and Discover Various Diseases Ahmed Hammad Al-Shoteri
Journal of Applied Data Sciences Vol 3, No 1: JANUARY 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i1.48

Abstract

The use of AI in healthcare has increased. It is now used in diagnosis, drug production, and improving hospital workflow between medical departments. The ability to examine large numbers of patients quickly is also a major use of artificial intelligence. Indeed, this field has made remarkable advances in early diagnosis and discovery of diseases through data, information, and radiograph analysis. The ability to predict disease outbreaks using AI analytics is dependent on data analysis and disease prediction. The current study aimed to assess the validity of previous research on artificial intelligence applications and their role in diagnosing and discovering diseases. This is to fill several gaps, such as the lack of recent studies in this field, especially Arab studies. The study also seeks to understand how artificial intelligence tools can help diagnose and discover diseases. The study yielded several findings. It is necessary to design systems and algorithms, as well as mechanisms and methods, to fully utilize artificial intelligence in this field. Neural networks, deep learning, fuzzy logic and others were addressed in previous studies, for their adoption and possible application because of their great impact according to the results of previous studies. Artificial intelligence can simultaneously monitor and process an unlimited number of inputs, revealing complex correlations that cannot be easily reduced. Finally, the researcher believes that artificial intelligence will increase efficiency, save time and effort, and reduce errors. Also, AI does not replace doctors because it lacks human qualities like empathy and compassion. The use of artificial intelligence in medicine will thus contribute to an approved and unprecedented scientific approach in this field to achieve the desired goals and objectives.
Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5 Qurrotul Aini; Jehad A H Hammad; Taslim Taher; Mohammed Ikhlayel
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.
Knuth Morris Pratt String Matching Algorithm in Searching for Zakat Information and Social Activities Fendi Riawan; Taqwa Hariguna
Journal of Applied Data Sciences Vol 3, No 1: JANUARY 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i1.49

Abstract

Algorithms are one of the components that need to be considered in the development of information systems. Determination of the algorithm is adjusted to the purpose of the system to be built. One algorithm that can be used is string matching. The string matching algorithm will play a role in searching for a string consisting of several characters (usually called a pattern). The method used in this research is string matching knuth morris pratt (KMP) which is used to search zakat information and social activities in the search engine system. KMP is a string matching algorithm with good performance. The results showed the performance of string matching using the KMP algorithm with 5 trials of input pattern on zakat information with execution times of 0.03 ms, 0.03 ms, 0.02 ms, 0.02 ms and 0.03 ms. And 5 times the input pattern experiment on social activities with execution time of 0.02 ms, 0.02 ms, 0.03 ms, 0.03 ms and 0.02 ms. Thus the average execution time of the KMP algorithm in string matching is 0.026 ms and 0.024 ms.
An Ensemble and Filtering-Based System for Predicting Educational Data Mining Andhika Rafi Hananto; Silvia Anggun Rahayu; Taqwa Hariguna
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.

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