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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 52 Documents
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Search results for , issue "Vol 5, No 3: SEPTEMBER 2024" : 52 Documents clear
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.
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.
Efficient Web Mining on MyAnimeList: A Concurrency-Driven Approach Using the Go Programming Language Putra, Muhammad Daffa Arviano; Dewi, Deshinta Arrova; Putri, Wahyuningdiah Trisari Harsanti; Achsan, Harry Tursulistyono Yani
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.v5i3.352

Abstract

Anime is a globally popular form of entertainment, with the industry experiencing rapid growth in recent years. Despite the wealth of anime data available on MyAnimeList, the largest community-driven platform for anime enthusiasts, existing publicly available datasets are often outdated and incomplete. This presents a challenge for data science research, as the increasing volume of anime information requires more efficient data extraction methods. This research aims to address this challenge by developing a concurrent web mining program using the Go programming language. Leveraging Go's concurrency capabilities, our program efficiently extracted anime data from MyAnimeList, iterating through anime pages from ID 1 to 52,991. To overcome potential issues like rate limits and server timeouts, we implemented a two-phase execution strategy. As a result, the program successfully gathered 23,105 anime records within 8.5 hours. The extracted data has been transformed into a comprehensive dataset and made publicly available in CSV format. This research demonstrates the effectiveness of concurrent web mining for large-scale data extraction and offers a valuable resource for future data-driven research in the anime industry.
Perceived Risk as a Mediator Between Brand Trust, Perceived Fit, and Brand Extension Success: Case Study of China Time-honored Brand Lu, Change; Pulpetch, Thitima; Li, Liou-Yuan
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.v5i3.317

Abstract

This study aimed to explore the causal relationship and impact of perceived risk on brand extension success, with a particular focus on insights drawn from China Time-honored brand. The primary research question addressed in this study is: Does perceived risk mediate the effects of brand trust and perceived fit on brand extension success? Based on perceived risk theory, categorization theory, this study constructs a research model by adopting China time-honored brand as the research subject. The study collected 605 valid survey responses using a self-filled questionnaire, employing a combination of purposive and random sampling methods in the location of the parent brands. Quantitative analysis was conducted using partial least squares structural equation modeling (PLS-SEM) to test 4 research hypotheses. The findings indicate that: In the brand extension process of China time-honored brand, 1) perceived risk transmits effects of brand trust to brand extension success; 2) perceived risk transmits effects of perceived fit to brand extension success. These discoveries underscore the importance of considering consumers' perceived risk in the formulation and implementation of brand extension strategies. This study contributes to understanding the causal relationships and impacts of perceived risk in the brand extension of time-honored brands. The empirical evidence provided can serve as a reference for the development of extension strategies and marketing management for China time-honored brands and other heritage brands.
Combined Fire Fly – Support Vector Machine Digital Radiography Classification (FF-SVM-DRC) Model for Inferior Alveolar Nerve Injury (IANI) Identification Manikandaprabhu, P.; Thirumoorthi, C.; Batumalay, M.; Xu, Zhengrui
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.v5i3.356

Abstract

Inferior Alveolar Nerve Injury (IANI) is a severe complication in oral surgery that can significantly affect a patient's quality of life. Accurate diagnosis is crucial for effective management, and digital radiography has become an essential tool in this regard. This study proposes a novel feature selection-based classification algorithm to enhance the diagnostic precision of digital radiographs (DRs) for IANI detection. The objective is to improve classification accuracy by selecting the most relevant features using a Firefly algorithm-based method. Our approach identifies optimal features that preserve critical information from the dataset, enabling more accurate predictions by machine learning models. The proposed method was tested using a dataset of 140 DRs and achieved a classification accuracy of 97.4%, with a sensitivity of 80.9% and a specificity of 94.8%. These results demonstrate that the Firefly algorithm-based feature selection significantly outperforms traditional methods in diagnosing IANI. The novelty of this research lies in its integration of advanced feature selection techniques with support vector machines, offering a robust tool for improving diagnostic accuracy in dental imaging. This work contributes to enhanced clinical decision-making and could be valuable for broader applications in healthcare systems.
Data Management as a Critical Component of Protecting Corporate Devices Melikov, Agassi; Gasimov, Vagif; Ahmadov, Samur
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.v5i3.283

Abstract

The relevance of the problem under study lies in the growing threat of cyberattacks and unauthorized access to corporate data. The need for effective data management at the moment is due to the increased importance of securing corporate devices, which requires in-depth analysis and understanding of the role of data management in this context. The aim of the study is to comprehensively analyze the role of information governance in securing organizational technology. The used methods were: experiment, systematization, comparison, analysis, synthesis. The main findings of the study emphasize the importance of information management in securing enterprise technology. The study involves the development of a C++ program designed to simulate different scenarios of using data management strategies. This program is designed to demonstrate the effectiveness of different information security techniques in organizational technologies. In addition, a comparative analysis of data control techniques designed to protect organizational devices has been carried out. The results of this analysis are presented in the form of a table that discusses the various aspects of information management in this context. And the developed structural diagram of information management in organizations presents the main components and processes required to secure organizational technology. The paper also provides examples of practical applications of data control techniques in large corporations, emphasizing their importance in protecting sensitive information. This research makes a practical contribution by providing organizations not only with theoretical foundations but also with concrete data governance strategies to enhance the security of corporate devices, which is essential for today’s companies in the face of growing cyber threats. Limitations of the study include biases, simulated situations, and an inability to adequately address issues that arise in the actual world, such as organizational culture and cyber threats.
Retinopathy Classification using Convolutional Neural Network Method with Adaptive Momentum Optimization and Applied Batch Normalization Slamet, Isnandar; Susilotomoa, Dhestahendra Citra; Zukhronah, Etik; Subanti, Sri; Susanto, Irwan; Sulandari, Winita; Sugiyanto, Sugiyanto; Susanti, Yuliana
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.v5i3.309

Abstract

Retinopathy is a common eye disease in Indonesia, ranking fourth after cataracts, glaucoma, and refractive errors. It can be overcome by early diagnosis with optical coherence tomography (OCT), but this imaging technique takes much time. In this research, retinal imaging was carried out using an expert system. The expert system in this study was formed using the convolutional neural network (CNN or ConvNet) method. CNN is an algorithm of deep learning that uses convolution operations to process two-dimensional data, such as images and sounds. This research consisted of 4 stages: data collection, preprocessing, model design, and model testing. A CNN model was formed with three arrangements, consisting of two convolutional layers and one pooling layer. The ReLU activation function, zero padding, and batch normalization were used in all three formats. Then, the flattening process was carried out, and the Softmax activation function was used at the end of the architecture. The model was built using eight epochs, and optimization of Adaptive Momentum resulted in a 98.96% test data accuracy value. The result was considered good so that CNN could be used as an alternative in retinopathy diagnosis. Further research is suggested to use other optimizations or other model architectures.
Asynchronous Programming based on Services with Application of Neural Networks as a Method of Taking Legitimate Measures at DDoS Attacks Tokpayev, Kairat; Bedelbayev, Agyn
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.v5i3.276

Abstract

The relevance of this study is conditioned by the growing threat of various attacks in the modern information space. The purpose of this study was to analyze and evaluate the effectiveness of applying asynchronous programming and neural networks to combat availability attacks. A rudimentary C# programme was created to simulate a DDoS attack detection system, and a comparative table was generated to assess different DDoS attack countermeasure services. The results illustrate the pragmatic importance of utilizing neural networks and asynchronous programming in detecting DDoS attacks, emphasizing their capacity to enhance the effectiveness, precision, and flexibility of detection systems. Such methods allow for a quick and effective response to attacks and ensure the stability of information systems, reducing the risk of loss of availability and financial losses. The study also highlights the importance of evaluating the scalability and performance of these methods in actual network environments. The practical significance of this study is that it provides new ways and tools to protect information resources from attacks, contributes to the advancement of scientific knowledge and provides certain solutions to combat information threats.
Entrepreneurial Orientation and MSME’s Tourism Performance: The Mediating Role of Social Media Capability Parlyna, Ryna; Susanto, Perengki; Abror, Abror; Marsal, Arif
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.v5i3.377

Abstract

This study explores the mediating role of social media capability on entrepreneurial orientation (EO) and MSME’s tourism performance in DKI Jakarta Province, Indonesia (MP). MSMEs contributes largely to Indonesia's economy. However, these enterprises often struggle with limited resources leading to weaker performance. The research problem centers on identifying factors that can enhance the MP, mainly through EO and SMC. The objective is to investigate both EO's direct impact on MP and SMC's mediating role. Quantitative research was used, utilizing a survey-based method with data collected from 300 MSME owners and managers in the tourism sector. Structural Equation Modeling (SEM) was used to examine the relationships between EO, SMC and MP. The results reveal a favorable positive relationship between EO and MP, with a path coefficient (β) of 0.425 and a p-value of 0.000. It underscores the importance of EO in intensifying the MP. It also found that EO significantly affects SMC, with a path coefficient (β) of 0.353 and a p-value of 0.000. This suggests that MSMEs with a strong EO are expected to enhance SMC. SMC was also found to have a positive and favorable effect on MP, with a path coefficient (β) of 0.179 and a p-value of 0.001. This suggest that SMC is crucial for improving MP. It also confirmed the mediating role of SMC in the relationship between EO and MP, with an indirect path coefficient (β) of 0.063 and a p-value of 0.006. It proposed that EO directly enhanced MP through SMC. However, the study is narrowed by its focal point on a specific geographic area and sector, which may impact the relevancy of the results. Forthcoming study could address these limitations by exploring different contexts. This study contributes to broaden the literature on the mediating role of SMC in the EO- MP in DKI Jakarta, Indonesia.
Assessing Leadership Styles, Self-Efficacy, Job Satisfaction, and Organizational Commitment in Rural Administrative Officials Through Mixed-Methods and Quantitative Data Analysis Aheruddin, Aheruddin; Eryanto, Henry; Sariwulan, Tuty
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.v5i3.315

Abstract

This study explores the relationship between leadership styles, self-efficacy, job satisfaction, and organizational commitment among rural administrative officials on Sumbawa Island. Utilizing a mixed-methods approach, the research combines quantitative data from surveys and qualitative insights from interviews across four districts: Bima, Dompu, Sumbawa, and Sumbawa Barat. The findings indicate that transformational leadership significantly enhances organizational commitment, with Bima exhibiting the highest commitment scores (mean = 4.18), followed by Sumbawa (mean = 3.92). In contrast, transactional leadership, more common in Dompu, correlates with lower commitment (mean = 3.09). Self-efficacy also plays a crucial role, with higher scores associated with increased job satisfaction and commitment, particularly in Sumbawa (mean = 3.75) and Bima (mean = 3.38). Job satisfaction mediates the relationship between leadership styles and organizational commitment, underscoring its importance in fostering a dedicated workforce. The study reveals significant contextual differences, emphasizing the need for tailored leadership development programs that address specific socio-cultural and demographic factors. Key contributions include empirical evidence supporting transformational leadership in rural administration and the integration of self-efficacy into leadership theories. Recommendations for future research include longitudinal studies and multi-source assessments, while practical applications suggest focusing on transformational leadership training, enhancing self-efficacy, and improving job satisfaction through better working conditions and career advancement opportunities. By addressing these factors, rural administrations can enhance organizational commitment and improve governance outcomes, ultimately contributing to sustainable rural development.

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