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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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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the largest community-driven platform for anime enthusiasts, existing publicly available datasets are often outdated and incomplete.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.
Teachable Machine: Optimization of Herbal Plant Image Classification Based on Epoch Value, Batch Size and Learning Rate Malahina, Edwin Ariesto Umbu; Saitakela, Mardhalia; Bulan, Semlinda Juszandri; Lamabelawa, Marinus Ignasius Jawawuan; Belutowe, Yohanes Suban
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.v5i2.206

Abstract

Herbal plants are a source of natural materials used in alternative medicine and traditional therapies to maintain health. The purpose of this research is to develop an intelligent system application that is able to assist people in independently detecting herbal plants around them, provide education, and most importantly, find the optimal value based on certain parameters. This research uses several values for the parameters studied, namely the epoch value which varies between 10, 50, 100, 250, 750, and 1000; the batch size value which varies between 16, 32, 64, 128, 256, and 512; and the learning rate value which varies between 0.00001, 0.0001, 0.001, 0.01, 0.1, and 1. A total of 10,000 training data samples (1,000 samples in 10 classes) were used in Teachable Machine. The method used is to utilize the TensorFlow framework in the Teachable Machine service to train image data. This framework provides Convolutional Neural Networks (CNN) algorithms that can perform image classification with a high degree of accuracy. The test results for more than three months showed that the highest optimal value was achieved at the 50th epoch value, with a learning rate of 0.00001, and a batch size of 32, which resulted in an accuracy rate between 98% and 100%. Based on these results, a mobile web-based intelligent system application service was developed using the TensorFlow framework in Teachable Machine. This application is expected to be widely implemented for the benefit of the community. However, the challenges and limitations in training this test data are the large number of data classes that will be very good so that machine learning can learn to recognize objects but will take hours to train, then the training image object data has a clean background from other objects so that when tested it is not detected and influenced as another object or can result in a decrease in the percentage value.
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.
Unsupervised Learning for MNIST with Exploratory Data Analysis for Digit Recognition Hery, Hery; Haryani, Calandra A.; Widjaja, Andree E.; Tarigan, Riswan E.; Aribowo, Arnold
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.v5i2.184

Abstract

This research investigates the application of unsupervised learning techniques for digit recognition using the MNIST dataset. Through a comparative analysis, various dimensionality reduction methods, including ISOmap, PCA, and tSNE, were evaluated for their effectiveness in visualizing and processing the MNIST data. The findings reveal that tSNE consistently outperforms ISOmap and PCA in terms of accuracy, F1- score, precision, and recall, showcasing its superior capability in preserving relevant information within the dataset. Utilizing tSNE for visualizing and clustering digits provides valuable insights into the underlying structure of the dataset, uncovering complex patterns in digit relationships. These results contribute to the advancement of digit recognition systems, offering potential improvements in classification accuracy and model reliability. The success of tSNE highlights the importance of nonlinear dimensionality reduction techniques in handling complex datasets, such as MNIST. This research underscores the significance of unsupervised learning approaches, particularly tSNE, in enhancing digit recognition systems' performance, with implications extending across various application domains. Continued research is recommended to explore further applications and potentials of unsupervised learning techniques and to deepen our understanding of the MNIST dataset's structure and complexity.
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.
Enhancing Aspect-based Sentiment Analysis in Visitor Review using Semantic Similarity Iswari, Ni Made Satvika; Afriliana, Nunik; Dharma, Eddy Muntina; Yuniari, Ni Putu Widya
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.v5i2.249

Abstract

The global economy greatly depends on the tourism industry, which fosters job opportunities and stimulates economic development. With the growing reliance of tourists on online platforms for guidance, evaluations of tourist destinations have gained heightened significance. These assessments, frequently expressed through user-generated content, offer valuable perspectives on customer experiences, viewpoints, and levels of satisfaction. Nevertheless, analyzing and interpreting these reviews can pose difficulties because of the unstructured or semi-structured nature of user-generated content. Conventional sentiment analysis methods might not adequately grasp the intricacies and particular aspects of tourism encounters that users convey in their reviews. The efficacy of sentiment analysis can be augmented by integrating semantic similarity. This study explores methods to enhance aspect-based sentiment analysis within tourism reviews by utilizing semantic similarity approaches. Five aspects have been curated, representing keywords frequently reviewed by visitors to the tourist attraction. These aspects encompass scenery, dusk, surf, amenities, and sanitation. Based on the data analysis, F-Measure values with Semantic Similarity tend to increase for the scenery and dusk aspects. This is because in the sample data used, visitor reviews for the scenery and dusk categories may use other words that are semantically similar. The sample data used for these categories is also quite extensive, resulting in a better classification model for both categories. While it is valuable to analyze user-generated content data from visitor reviews, it's important to consider the limitations and potential biases associated with this data. The classification results per aspect need to be further reviewed in more depth. What aspects lead visitors to give positive reviews will certainly be maintained and even improved by stakeholders. Similarly, for negative review outcomes, it is necessary to investigate more deeply the factors contributing to visitor dissatisfaction so that they can be addressed by stakeholders.
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.
Modeling and Control of a Based Extreme Learning Machine as Distributed Setpoint for the HEPP Cascade System in a Nickel Processing Plant Sarira, Yayan Iscahyadi; Syafaruddin, Syafaruddin; Gunadin, Indar Chaerah; Utamidewi, Dianti
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.v5i2.211

Abstract

The aim of this research is to model the cascade system of hydropower plants in order to predict the set point power value of each generator. The model simulates several input data variables to obtain an accurate prediction of the set point value. Various historical data are used in this study to evaluate the relationship between input and output variables. This paper presents an Extreme Learning Machine (ELM) method for modeling system models and generating set point values for each generator in a hydroelectric power plant (HEPP) cascade system in a nickel processing plant (NPP). The issue of coordination time between the production and utility departments is addressed. The research aims to use the ELM method to auto-generate setpoint values.  The MATLAB application serves as a simulator for generating the expected Extreme Learning Machine (ELM) model.  As a result, this allows for automatic changes to the set point of each generator in the cascade system. The ELM method yields a MAPE value of 13.94%, indicating accurate predictions.
Data Analysis of Student Attitude Survey Based on Internet Analysis Technology Cao, Yongcheng; Liu, Quanguo; Chen, Huajie
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.63

Abstract

Language attitude is people’s understanding and evaluation of languages, which has important effect on language learning. Based upon investigation into 605 tibetan college students from 5 colleges in Tibet areas, and combined with network data, this paper mainly analyses their attitude towards Tibetan, Chinese and English from four dimensions: recognizing, instrumental, integrative and transferring attitude. This paper also discusses the relationship between students’ language attitude and their gender and grade.

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