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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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The Mediating Role of Perceived Value in the Relationship Between Brand Image and Repurchase Intention: A Case Study of the Chinese Tea Market Luo, Rui; Sriboonlue, Umawasee; Onputtha, Suraporn
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.v5i2.227

Abstract

This study is dedicated to exploring how brand image influences repurchase intention through perceived value in the Chinese tea market. Five randomly selected tea brands among the top twenty tea brands in Sichuan Province were chosen as the study sample. Corporate image, product image and user image were used to measure brand image; meanwhile, functional value, emotional value, social value and price value were used to assess perceived value; and repurchase intention was directly measured through questionnaires. Six hundred valid questionnaires from consumers of these five brands were collected through the Questionnaire Star platform and analyzed by structural equation modeling using SMARTPLS 4.0 software. The results show that brand image has a significant positive effect on perceived value and repurchase intention, and perceived value plays a significant mediating role between brand image and repurchase intention. These findings not only enrich the theoretical framework, but also provide practical strategic recommendations for brand management in the Chinese tea market, emphasizing the need to pay attention to the impact of brand image on consumer repurchase intention through perceived value in the process of brand image construction and management, so as to enhance consumer loyalty and promote sustained purchase behavior.
Implementation of the K-Nearest Neighbor Algorithm for the Classification of Student Thesis Subjects Paramita, Adi Suryaputra; Maryati, Indra; Tjahjono, Laura Mahendratta
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.v3i3.66

Abstract

Students who have studied for a considerable amount of time and will complete a lecture process must complete the necessary final steps. One of them is writing a thesis, a requirement for all students who wish to graduate from college. Each student's choice of topic or specialization will be enhanced if it not only corresponds to their interests but also to their skills. K-Nearest Neighbor is one of the classification techniques used. K-Nearest Neighbor (KNN) operates by determining the shortest distance between the data to be evaluated and the K-Nearest (neighbor) from the training data. K-Nearest Neighbor is utilized to classify new objects based on the learning data closest to the new object. Therefore, KNN is ideally suited for classifying data to predict student thesis topics. This research concludes that optimizing the k value using k-fold cross-validation yields an accuracy rate of 79.37% using k-fold cross-validation = 2 and the K-5 value. Based on the K-Nearest Neighbor Algorithm classification results, 45 students are interested in computational theory thesis (RPL) topics, 32 students are interested in artificial intelligence (AI) thesis topics, and 21 students are interested in software development topics.
Synchronization Patterns for Digital Twin Systems Alghamdi, Wael; Albassam, Emad
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.v5i3.267

Abstract

In the area of digital twin technologies, ensuring operational accuracy and efficiency requires information alignment between the physical objects and their corresponding digital twins. This study identifies synchronization patterns that can be incorporated into the design of digital twin systems to help improve data quality, system efficiency, and alignment in digital twin systems. To identify these patterns, we conducted a comprehensive literature review and performed an analysis of synchronization techniques. With this approach, it is possible to study the advantages and limitations of various synchronization patterns, such as time-driven, event–driven, and hybrid patterns, as well as determine their applicability in different operational settings. Identified patterns are evaluated by means of simulations of several component-based software architectures. The results of the study indicate that it is crucial to identify and incorporate appropriate synchronization patterns in the design of digital systems to maximize the benefit of this technology. While some patterns work well in industrial settings, other patterns are more applicable in other domains such as health systems and smart cities. The findings offer valuable directions for future innovations and uses in various industries thereby significantly raising the field of digital twin technology.
Clustering the Unlabeled Data Using a Modified Cat Swarm Optimization Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Zakaria, Mohd Zaki; Armoogum, Sheeba
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.v5i3.349

Abstract

This paper presents a modified version of the Cat Swarm Optimization (CSO) algorithm aimed at addressing the limitations of traditional clustering methods in handling complex, high-dimensional datasets. The primary objective of this research is to improve clustering accuracy and stability by eliminating the mixture ratio (MR), setting the counts of dimensions to change (CDC) to 100%, and incorporating a new search equation in the tracing mode of the CSO algorithm. To evaluate the performance of the modified algorithm, five classic datasets from the UCI Machine Learning Repository—namely Iris, Cancer, Glass, Wine, and Contraceptive Method Choice (CMC)—were used. The proposed algorithm was compared against K-Means and the original CSO. Performance metrics such as intra-cluster distance, standard deviation, and F- measure were used to assess the quality of clustering. The results demonstrated that the modified CSO consistently outperformed the competing algorithms. For example, on the Iris dataset, the modified CSO achieved a best intra-cluster distance of 96.78 and an F-measure of 0.786, compared to 97.12 and 0.781 for K-Means. Similarly, for the Wine dataset, the modified CSO reached a best intra-cluster distance of 16399, surpassing K-Means which recorded 16768. In conclusion, the modifications introduced to the CSO algorithm significantly enhance its clustering performance across diverse datasets, producing tighter and more accurate clusters with improved stability. These findings suggest that the modified CSO is a robust and effective tool for data clustering tasks, particularly in high-dimensional spaces. Future work will focus on dynamic parameter tuning and testing the scalability of the algorithm on larger and more complex datasets.
Predicting Financial Failure in Algerian Public Insurance Companies Using the Kida Model El Bachir, Morkane Mohamed; Mili, Khaled; Bengana, Ismail; Benaouali, Imane
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.v5i2.212

Abstract

This study evaluates the effectiveness of financial ratios in predicting the financial health of Algerian public insurance companies using the Kida model, a robust tool for identifying potential financial failure and bankruptcy risks. The primary objective is to assess the predictive power of the Kida model for early detection of financial failure. The research employs a case study approach, analyzing financial statements from three major public insurance companies in Algeria (SAA, CAAR, CAAT) over the period from 2015 to 2021. Key contributions include a comprehensive analysis of financial ratios such as profitability, solvency, liquidity, and management efficiency, and their integration into the Kida model. The methodology involves a detailed examination of financial data, application of the Kida model, and interpretation of the financial failure index. Our findings reveal that the Kida model accurately predicts financial failure risks, with all values of the financial failure index being negative, indicating potential vulnerability. The study underscores the importance of early detection systems and proactive financial management to ensure stability and sustainability in the insurance sector. The results have significant implications for policymakers and stakeholders, emphasizing the need for tailored financial failure prediction models for the Algerian insurance industry. Future research could expand on this work by incorporating real-world data and exploring other predictive models to enhance accuracy and reliability.
Enhancing Spam Detection Using Hybrid of Harris Hawks and Firefly Optimization Algorithms Abualhaj, Mosleh M.; Shambour, Qusai Y.; Alsaaidah, Adeeb; Abu-Shareha, Ahmad; Al-Khatib, Sumaya; Hiari, Mohammad O.
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.v5i3.279

Abstract

The emergence of the modern Internet has presented numerous opportunities for attackers to profit illegally by distributing spam mail. Spam refers to irrelevant or inappropriate messages that are sent on the Internet to numerous recipients. Many researchers use many classification methods in machine learning to filter spam messages. However, more research is still needed to assess using metaheuristic optimization algorithms to classify spam emails in feature selection. In this paper, we endorse fighting spam emails by employing a union of Firefly Optimization Algorithm (FOA) and Harris Hawks Optimization (HHO) algorithms to classify spam emails, along with one of the most well-known and efficient methods in this area, the Random Forest (RF) classifier. In this process, the experimental studies on the ISCX-URL2016 spam dataset yield promising results. For instance, the union of HHO and FOA, along with using an RF classifier, achieved an accuracy of 99.83% in detecting spam emails.
Ethno-Flipped Learning of Mathematics Lessons Based on Internalizing Data from Tri Mandala Concept on Schoology Platform Ardana, I Made; Sugiarta, I Made; Sudatha, I Gde Wawan; Andayani, Made Susi Lissia; Divayana, Dewa Gede Hendra
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.v5i3.361

Abstract

In the digital era like today, maximum Mathematics learning outcomes in the affective and psychomotor domains are difficult to obtain through full online learning. This difficulty is influenced by environmental and cultural factors inherent in students’ lives. Therefore, an idea in the form of innovative learning model is needed to internalize cultural values and local wisdom in online learning. Based on the problems and needs of their solutions, the innovative model that emerged in this study is called Ethno-Flipped learning of Mathematics lessons based on internalizing data from Tri Mandala concept on the Schoology platform. The aim of this research is to demonstrate an Ethno-Flipped Learning design based on internalizing data from the Tri Mandala concept on the Schoology platform. This research approach is development with a focus on development at several stages, including: research and field data collection, planning, design development, initial trials, and revision of initial trial results. The number of personnel involved in the initial trial was 104 respondents. The data collection tool for the initial trial results was a questionnaire consisting of 10 questions related to the model design. The analysis was carried out by comparing the percentage of model design quality with the five scale categorization standards. The findings or research results show that the Ethno-Flipped learning design especially for Mathematics lessons based on internalizing data from the Tri Mandala concept on the Schoology platform is classified as good quality by a quality percentage of 87.54%. The contribution of this research is that it provides new knowledge about an innovative learning model based on Balinese culture and local wisdom combined with an online learning platform. The novelty of this research is in the form of an innovative learning model design that combines the Flipped Classroom learning model, the Ethno-Mathematics approach, and the Tri Mandala concept.
Applied Regression Modelling to Recommend Sustainable Tourism Development Policies: A Case Study of Danang City in Vietnam Tien, Nguyen Duc
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.v5i2.188

Abstract

Sustainable tourism development is one of the top focuses not only in Vietnam but also around the world. Especially when Covid-19 began to break out in early 2020, causing most tourism activities to stop and causing many communities that considered tourism to be completely independent from other industries to have time to look back. Thus, the article aims to identify the main factors contributing to sustainable tourism development in Danang City, Vietnam. Accordingly, some contents need to clarify factors affecting sustainable tourism development, measuring the characteristics and the role of each factor impacting sustainable tourism development. Finally, policy implications for sustainable tourism development in the future are proposed. Based on the goal, the author surveyed 600 tourists and used the regression modeling method from data processed using SPSS 20.0. In addition, the results showed six factors affecting sustainable tourism development with a significance of 0.01. This situation requires more practical recommendations from state management agencies and tourism enterprises. The contributions of this study suggest sustainable tourism development policies to balance economic development, social stability, and environmental protection. The article evaluates sustainability, considering the cohesion and balance of sustainable development aspects, such as economic, social, and environmental, thereby going deeper and identifying groups of activities. The main drivers to achieve sustainable tourism development include economics, society, environment, tourism resources, tourism products and services, and infrastructure. The research novelty discusses proposed six recommendations, which include (1) infrastructure development, (2) developing technical facilities for the tourism industry, (3) developing human resource training, (4) level of organization and management of the tourism industry, (5) quality of tourism services, and (6) community participation for sustainable tourism development in Danang City, Vietnam.
Machine Learning Techniques for Diabetes Prediction: A Comparative Analysis Abdelhafez, Hoda A.; Amer, Abeer 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.v5i2.219

Abstract

Diabetes mellitus, characterized by chronic hyperglycemia, presents significant challenges due to its associated complications and increasing morbidity rates. This study examines a range of machine learning algorithms such as Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, Neural Network, Support Vector Machine, LogitBoost, and Voting classifier to develop accurate predictive models for diabetes. The data used in this research is drawn from a comprehensive dataset available on mendeley.com, sourced from the laboratory of Medical City Hospital in Iraq. The focus of the study is on feature selection and evaluation metrics to effectively gauge model performance. Eight classification techniques are employed and compared, including Decision Trees (DT), Random Forests (RF), and LogitBoost. The study's findings highlight DT and RF as the top-performing algorithms, demonstrating comparable predictive abilities, with LogitBoost also showing promising results. Conversely, Support Vector Machine (SVM) shows reduced performance due to its sensitivity to outliers. These insights enable healthcare practitioners to adopt appropriate machine learning methods to improve diabetes prediction, thus enabling timely interventions and enhancing patient outcomes.
Anomaly Detection in Sales Transactions for FMCG (Fast Moving Consumer Goods) Distribution Tanuwijaya, Eggy; Mauritsius, Tuga
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.v5i3.228

Abstract

In today’s era, companies operating in the FMCG industry played an important role in society, especially regarding the distribution of goods used in daily life, which were distributed directly from factories or principals. Despite rapid technological advancements, many distribution companies in Indonesia still relied on human labor and conducted distribution processes manually. Concerns about inaccuracies in employee actions and other detrimental activities such as embezzlement, fraud, and so on, drove companies to undertake digital transformation processes. To reduce these risks, some FMCG companies had already implemented systems to monitor distribution activities and customer payment processes. However, another issue arose due to the limited number of employees available to conduct professional audits, resulting in suboptimal monitoring processes and increased risks of integrity issues or fraud committed by employees. To address this, the implementation of an Autoencoder system was utilized to help companies detect fraudulent activities, particularly in the sales domain. Referring to this study, it showed that the implementation of machine learning technology, such as Autoencoders, yielded positive results and was considered effective in detecting suspicious activities, especially in large transaction datasets. The Autoencoder system utilized in this research was developed using TensorFlow, showing promising results in detecting fraudulent transactions in the company. Additionally, the model was able to train on 80% of the data and was tested on the remaining 20%. According to the outcome, approximately 6.664% of transactions were predicted to be fraudulent. Based on the results, this research showed that the implementation of the AutoEncoder system had proven to be effective in helping the organization prevent and protect against potential non-compliant activities. This proof could be used as a learning opportunity for other organizations facing similar challenges. 

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