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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 518 Documents
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Structural Equation Modeling on Women’s Perceptions of Halal Cosmetics Based on The Development of TPB Framework Using Religiosity, Social Influence, Knowledge, and Brand Value Utami, Meinarini Catur; Fetrina, Elvi
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.v5i1.164

Abstract

Nowadays cosmetics play an important role for women to maintain their beauty. The statistics show that in 2018, there was an increase of 5.5 % in the cosmetics market globally compared to the previous year whereas the skincare product accounted for about 39% of the worldwide market. In Indonesia, it shows quite the same condition and it also shows that the trend of halal cosmetics has a quite big demand with which 58.3% of Indonesian women choose halal cosmetics. This study was continuous research from the previous one conducted by the authors in 2018 to find out whether there was an influence of religiosity on Indonesian Muslim women intentions and behavior in buying Korean cosmetics based on Halal issues. Based on previous research and other similar research, the researchers developed The Theory of Planned Behavioral (TPB) as the model with some additional variables including religiosity, social influence, knowledge, and brand value. In continuation, this paper determined whether women regardless of these variables are interested in halal cosmetics that showed through intention variable in TPB. The model with 7 proposed hypotheses was analyzed by using the Structural Equation Modeling (SEM) method and SmartPLS as a tool. The results based on the 600 women of college students (muslim and non-muslim) in Java showed that 6 hypotheses have significant influence toward the intention to use halal cosmetics.
An Empirical Analysis of Bank Capital Adequacy Ratio in Vietnam: A Data Science Approach Using System Generalized Method of Moments Huy, Nguyen Quoc; Nga, Lu Phi; Tam, Phan Thanh
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.v5i1.156

Abstract

Commercial banks and the financial industry face considerable hurdles in light of the fourth Covid-19 outbreak. Commercial banks continuously put capital adequacy measures in place to fulfill Basel regulations. One of the main ways they do this is by issuing bonds, which boost tier 2 capital sources. This helps mobilize capital and assure capital safety for the market's borrowing requirements in the long run. As a result, considering both external and internal variables, this research seeks to investigate what influences the capital adequacy ratio of Vietnam's joint-stock commercial banks. Between 2011 and 2022, the authors combed through data from 25 different Vietnamese joint-stock commercial banks. The authors employed the system generalized method of moments model and other conventional techniques for panel data analysis. The authors derived key findings: Fourteen components are statistically significant at the 1% level, affecting the capital adequacy ratio. Therefore, it is evident that the equity capital of Vietnamese commercial banks has successfully met the required safety standards for assets with credit risk as per legislation. As a result, this assists Vietnamese commercial banks in managing potential losses from credit activities, thus assuring the security of banking operations and protecting depositors. However, the issue suggests policy implications for enhancing Vietnamese commercial banks' future capital adequacy ratio coefficient.
Predictive and Analytics using Data Mining and Machine Learning for Customer Churn Prediction Lukita, Chandra; Bakti, Lalu Darmawan; Rusilowati, Umi; Sutarman, Asep; Rahardja, Untung
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.v4i4.131

Abstract

This research aims to predict and analyze customer churn using Data Mining and Machine Learning methods. The background of this research is based on the importance of understanding the factors that influence customer decisions to churn, as well as improving the effectiveness of customer retention strategies in a business context. The method used in this research involves the use of a customer bank dataset that includes information about customers who left in the past month, services registered by customers, customer account information, and demographic info about customers. The factors most influential to churn were identified through heatmap analysis, including MonthlyCharges, PaperlessBilling, SeniorCitizen, PaymentMethod, MultipleLines, and PhoneService. This research compares the performance of several machine learning algorithms, including Random Forest, Logistic Regression, Adaboost, and Extreme Gradient Boosting (XGBoost), to predict customer churn. Accuracy metrics and confusion matrix results are used to evaluate the performance of these algorithms. The results showed that XGBoost proved to be the best algorithm in predicting customer churn with high accuracy. The factors that have been correctly identified do not provide missed precision, showing a significant influence on customer churn decisions. The novelty and uniqueness of this research lies in focusing on the factors that have the most influence on customer churn and comparing the performance of machine learning algorithms. This research provides more specific and relevant insights for companies in developing effective customer retention strategies. However, this research has some limitations. One of them is the use of a dataset limited to a customer bank, so the generalizability of the findings of this research may be limited to that business context. In addition, other factors that are not the focus of this research may also contribute to the prediction of customer churn.
Applying Structural Equation Modeling for Accessing Mobile Banking Service Quality and Customer Satisfaction: A Case Study in Vietnam 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.v4i4.137

Abstract

Mobile Banking allows customers to use mobile devices and smartphones to conduct banking transactions anytime, anywhere. On the other hand, Mobile banking is a service product that brings high business efficiency, does not cost much, creates initiative for users, reduces pressure on over-the-counter transactions and has little risk, so developing developing mobile banking services brings great benefits to banks. Therefore, using scientific and technological achievements, particularly information technology, electronics, and telecommunications, has had a significant impact on daily life, the economy, and society, changing people's awareness and production and business methods in a wide range of fields and industries, including financial-banking services. In order to address the aforementioned analytical concerns, the authors performed a survey of 650 individual consumers who use mobile banking services at ten commercial banks in Vietnam. The authors employed structural equation modeling and data processing tools SPSS 20.0, Amos. Customer satisfaction is influenced by five elements, according to the findings: dependability, responsiveness, empathy, competence, and tangibles. The findings of the article had a significant reliability influence on individual customer satisfaction, with a significance level of sig 0.01. Finally, the study uniqueness validates ideas regarding customer satisfaction and service quality drivers, as well as the need of flexibly implementing customer satisfaction research policies.
Data-Driven Analysis of Teaching Quality Impact on Graduate Employment in Higher Vocational Colleges of Hefei Wang, Ning; Pasawano, Tiamyod; Sangsawang, Thosporn; Pigultong, Matee
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.v5i1.169

Abstract

The objectives were to identify the influence of teaching quality in higher vocational colleges on the employment quality of graduates, and to develop instructional design through both theoretical and empirical analysis, to synthesize the relationships among teaching quality, human capital, and employment quality. In collaboration with 17 experts, they were selected through purposive sampling and involving 100 instructors within higher vocational colleges in China. The instruments using the Delphi Technique through a round questionnaire of vocational colleges' teaching quality positively influenced both graduates' human capital and employment quality. The findings revealed that vocational colleges' teaching quality positively influenced both graduates' human capital and employment quality. Vocational education has a favorable effect on employment quality, with human capital playing a crucial role in enhancing teaching quality. This paper distributed 600 questionnaires in total and collected 527 valid questionnaires, with an effective recovery rate of 87.83%. Data processing and analysis were carried out on the valid questionnaires. However, the relationship between teaching quality and employment quality is mediated by professional cognition and growth ability. These results offer important insights for vocational colleges, pointing to the crucial significance of human capital and educational quality in improving employment quality. In higher vocational colleges, the study investigates the connection between human capital, employment quality, and instructional quality. The teaching quality positively affects graduates' human capital and employment quality, according to data from Hefei grads. The link between teaching and learning is moderated by human capital. The research uses AMOS software to analyze vocational teaching variables, revealing a direct effect of higher colleges' teaching quality on graduates' employment quality and human capital. The significance level of these effects is .001, indicating a strong capacity for explanatory reasoning.
Harnessing the Power of Prophet Algorithm for Advanced Predictive Modeling of Grab Holdings Stock Prices Hery, Hery; Haryani, Calandra A.; Widjaja, Andree E.; Mitra, Aditya Rama
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.181

Abstract

This study investigates the effectiveness of the Prophet algorithm in predicting Grab Holdings' stock prices dataset from Kaggle. By meticulously analyzing historical closing, high, low, and volume data, the research aims to uncover market patterns and gain insights into investor sentiment based on short-term forecasting. The findings reveal a dynamic trajectory for Grab Holdings' stock, characterized by significant fluctuations and evolving investor confidence. The stock reached a peak of $14 in early 2022, indicating optimism, but subsequently experienced a decline to $4 by late 2023, reflecting a shift in sentiment. Notably, 2023 witnessed heightened volatility compared to 2022, evident in more significant price swings and increased trading volume. The Prophet algorithm demonstrated promising potential for prediction better than traditional methods, which overlook the presence of seasonality or fail to adapt to evolving market conditions, leading to less accurate forecasts. The excellent performance of Prophet is indicated by a Mean Absolute Percentage Error (MAPE) of 10.45511%, a Mean Absolute Error (MAE) of 3.112026, and a Root Mean Squared Error (RMSE) of 3.516969. Compared to the traditional ARIMA, MAE and RMSE resulting from Prophet are much lower than their counterparts, which are 14.49675 and 16.079898, respectively. These widely used metrics suggest moderate accuracy in predicting future stock prices. This research offers valuable insights for investors that they can use to understand the trend of Grab Holdings' stock price and make more informed investment decisions regarding buying or selling opportunities. However, it is crucial to acknowledge the inherent limitations of such models and interpret results cautiously, considering the ever-changing dynamics of the financial market.
Assessing Drought Risk in Forest Zones Near Coal Mines with TVDI Taati, La; Sunardi, Sunardi; Syauqiah, Isna; Jauhari, A
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.220

Abstract

This study is quantitative research employing survey techniques and spatial modeling. In the research area, particularly in forested areas, the NDVI values range from 0.25 to 0.55 with LST values of 29°C to 37°C. Cooler temperatures below 34°C were observed in the southwest (outside the mining area). The LST values indicate high temperatures above 37°C in the coal mining area, with effects extending up to 6 km. The linear regression equation between NDVI and LST in the coal mining area, with a regression equation of y = -20.888x + 40.458; R^2 = 0.83; r = -0.91, shows an inverse relationship between NDVI increase and ground surface temperature, indicating a good model fit with the data and a strong negative linear relationship between the two variables. The Urban Heat Island (UHI) effect in the mining area, especially at the mining center, shows a UHI with a temperature difference of more than 0.6 degrees Celsius compared to the cooler surrounding area. At the center of the coal mining area, the TVDI value is 0.6-0.8 (high-very high), but in the eastern part of the mine in forested areas with a certain soil type, the TVDI value is 0.2-0.6 (moderately dry - dry), while in other parts of the forested area with a different soil type, the NDVI value is 0.2 (moist). There is a difference in response to different soil types. Drought increases in the forested areas around the mining site, affecting ecosystem productivity and soil moisture.
Early Stopping on CNN-LSTM Development to Improve Classification Performance Anam, M. Khairul; Defit, Sarjon; Haviluddin, Haviluddin; Efrizoni, Lusiana; Firdaus, Muhammad Bambang
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.312

Abstract

Currently, CNN-LSTM has been widely developed through changes in its architecture and other modifications to improve the performance of this hybrid model. However, some studies pay less attention to overfitting, even though overfitting must be prevented as it can provide good accuracy initially but leads to classification errors when new data is added. Therefore, extra prevention measures are necessary to avoid overfitting. This research uses dropout with early stopping to prevent overfitting. The dataset used for testing is sourced from Twitter; this research also develops architectures using activation functions within each architecture. The developed architecture consists of CNN, MaxPooling1D, Dropout, LSTM, Dense, Dropout, Dense, and SoftMax as the output. Architecture A uses default activations such as ReLU for CNN and Tanh for LSTM. In Architecture B, all activations are replaced by Tanh, and in Architecture C, they are entirely replaced by ReLU. This research also performed hyperparameter tuning such as the number of layers, batch size, and learning rate. This study found that dropout and early stopping can increase accuracy to 85% and prevent overfitting. The best architecture entirely uses ReLU activation as it demonstrates advantages in computational efficiency, convergence speed, the ability to capture relevant patterns, and resistance to noise.
The Intelligent kWh Export-Import Utilizing Classification Models for Efficiency in Hybrid PLTS Baso, Muchlis; Manjang, Salama; Suyuti, Ansar
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.244

Abstract

Electricity demand is integral to the stability of the community's economic condition, where currently electricity is predominantly sourced from fossil fuels, posing limitations. One effort to maintain this stability is through the utilization of renewable energy, particularly solar energy. The abundance of solar energy in Indonesia presents an opportunity to maximize its potential. This study develops an intelligent kWh export-import system based on the Internet of Things (IoT) and integrated with machine learning. Users can access real-time conditions via mobile based on three parameters: "current," "power," and "voltage." Machine learning is employed to classify conditions as "efficient" or "less efficient" by analyzing and comparing five different models: AdaBoost Classifier, DecisionTree Classifier, support vector machine (SVM), naïve Bayes classifier, and extra tree classifier. Model evaluation using accuracy percentage and F1-score indicates that the AdaBoost classifier exhibits high accuracy and F1-score values of 94.5% and 0.937, respectively.
Improved Hybrid Machine and Deep Learning Model for Optimization of Smart Egg Incubator Febriani, Anita; Wahyuni, Refni; Mardeni, Mardeni; Irawan, Yuda; Melyanti, Rika
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.304

Abstract

This research develops a Smart Egg Incubator that integrates IoT technology, fuzzy logic, and the YOLOv9-S Deep Learning model to enhance the efficiency and accuracy of hatching chicken eggs. The system automatically regulates temperature and humidity, maintaining temperature between 34.3°C and 39.5°C and humidity between 57% and 68% with a fuzzy logic success rate of 90%. The YOLOv9-S model enables realtime chick detection and classification with mAP50 of 93.7% and mAP50:95 of 71.3%. Efficiency improvements are measured through the success rate of fuzzy logic and improved detection and classification accuracy. This research also uses CNN for high-accuracy object classification, with model optimization performed using SGD to accelerate convergence and improve accuracy. The results indicate significant potential in improving the egg hatching process. The high accuracy and robustness of the YOLOv9-S model enhance real-time monitoring and decision-making in hatcheries, leading to higher hatching success rates, reduced chick mortality, and increased operational efficiency. Future designs can leverage these technologies to create more intelligent, automated systems requiring minimal human intervention, enhancing productivity and scalability. Additionally, IoT and deep learning integration can extend to other poultry farming areas, such as broiler production and disease monitoring, providing a comprehensive approach to farm management. Future research could focus on integrating the YOLOv10 model for even higher accuracy and efficiency, exploring diverse data augmentation techniques, optimizing fuzzy logic algorithms, and integrating additional sensors like CO2 and advanced humidity sensors to improve environmental regulation. These advancements would benefit not only smart incubator applications but also broader poultry farming areas.

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