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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 38 Documents
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Search results for , issue "Vol 5, No 2: MAY 2024" : 38 Documents clear
Research Gaps in Radio Frequency Identification Technology Implementation in Warehouses Haswika, Haswika; Qurtubi, Qurtubi; Setiawan, Danang; Supriyadi, Supriyadi
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.v5i2.245

Abstract

Warehouses are important in enabling and supporting the efficient execution of logistical activities. Integrating radio frequency identification (RFID) technology can catalyze innovation in warehouse operations by reducing the tracking and management of items. This study aims to identify novel research prospects for integrating RFID technology within warehouse operations. The researchers conducted a comprehensive literature evaluation by examining articles indexed by Scopus. This study has comprehensively analyzed the critical elements of each paper reviewed, including the research aims, findings, outputs, and gaps. These characteristics contribute to identifying innovative research directions in the context of RFID technology and its application in warehouse environments. This study provides a significant result, as no recent literature review on a similar topic exists. Furthermore, this literature review aims to address the existing theoretical void by building upon prior research and offering scientific contributions by presenting a complete and comprehensive literature review.
Knowledge Mapping of Digital Leadership and Research Agenda: The Open Knowledge Maps Perspective Zam, Efvy Zamidra; Amin, Shofia; Johannes, Johannes; Rosita, Sry
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.v5i2.207

Abstract

In today's technological era, digital leadership is necessary for organizational success in facing environmental and technological alteration. This study aims to map digital leadership research using the Open Knowledge Maps platform to find and build cluster visualizations. Moreover, using Open Knowledge Maps as a research analysis tool is rare. The data used is a research paper from 2015-2024 with high metadata quality. This study found 15 clusters related to digital leadership, and most research on digital leadership is carried out in the education field. In addition, this digital leadership study also searches for its effect on employee performance. This study implies that it can find research gaps that can be helpful for future research as the basis for further research.
Improving Publishing: Extracting Keywords and Clustering Topics Soekamto, Yosua Setyawan; Maryati, Indra; Christian, Christian; Kurniawan, Edwin
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.v5i2.199

Abstract

Humans, by nature, are inclined to share knowledge across various platforms, such as educational institutions, media outlets, and specialized research publications like journals and conferences. The consistent oversight and evaluation of these publications by ranking bodies serve to maintain the integrity and quality of scholarly discourse on a global scale. However, there has been a decline in the proliferation of such publications in recent times, partly attributed to ethical misconduct within specific segments of the scholarly community. Despite implementing systems such as the Open Journal System (OJS), publishers grapple with the formidable task of managing editorial and review processes. Compounding the multifaceted nature of scholarly content, manual review procedures often lead to considerable time investment. Thus, a pressing need exists for advanced technological solutions to streamline the article selection process, empowering publishers to prioritize articles for review based on topical relevance. This study advocates adopting a comprehensive framework integrating advanced text analysis techniques such as keyword extraction, topic clustering, and summarization algorithms. These tools can be implemented and integrated by connecting with the database of the existing system. By leveraging these tools with the expertise of editorial and review teams, publishers can significantly expedite the initial assessment of submitted articles. Given the rapid technological advancements, publishers must embrace robust systems that enhance efficiency and effectiveness, particularly in reviewer assignments and article prioritization. This research employs the neural network approach of BERT and K-Means clustering to perform keyword extraction and topic clustering. Furthermore, using BERT facilitates accurate semantic understanding and context-aware representation of textual data. Additionally, BERT's pre-trained models enable its fine-tuning capability to allow customization to specific domains or tasks. By harnessing the power of BERT, publishers can gain deeper insights into the content of scholarly articles, leading to more informed decision-making and improved publication outcomes.
The Determinant Factors For The Issuance Of Central Bank Digital Currency (CBDC) In Malaysia Using Machine Learning Framework Awang Abu Bakar, Normi Sham
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.v5i2.176

Abstract

In order to identify the factors influencing the establishment of the Centre Bank Digital Currency (CBDC) in Malaysia, this study leverages the machine-learning technique to determine the most critical factors leading to CBDC issuance in Malaysia. The overall Central Bank Digital Currency Project Index (CBDCPI) was selected as a target variable,while two machine learning algorithms, Random Forest and XGBoost were used to identify the determining variables. The accuracy obtained through the Random Forest is 83% and subsequently, 80% in XGBoost. This study explored a new research frontier by creating two machine-learning models that treated retail and wholesale CBDCPI as target variables. The data used in the process are gathered from various official sources such as the Bank for International Settlements (BIS), the International Monetary Fund (IMF), and the World Bank. The Circulation of Cash, Prevalence of Cryptocurrencies, Effect of CBDC on International Trade, the Search Interest, Financial Development Index, Innovation Value, and Trade Openness are some of the most critical factors determining whether CBDC will be issued in Malaysia. Generally, are identified as important factors determining whether CBDC will be issued in Malaysia. Eventually, the factors identified will be used to develop a framework for the implementation of CBDC in Malaysia.
Multi-Algorithm to Measure the Accuracy Level of Diabetes Status Prediction Zulkifli, Zulkifli; Makkiyah, Feda Anisah; Antoni, Darius; Fitriana, Fitriana; Jamaan, Taufik; Taufik, Ahmad
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.v5i2.250

Abstract

Poor management of diabetes leads to damage in organs and body tissues, impacting crucial organs like the heart, kidneys, eyes, and nerves. Although there is no permanent cure for diabetes, early detection enables effective disease management, which researchers and medical professionals agree enhances recovery prospects. The rapid progress in information technology has facilitated early prediction and diagnosis of diseases through Machine Learning (ML), a subset of Artificial Intelligence (AI) comprising various algorithms such as Neural Network, Support Vector Machine (SVM), kNN, Random Forest, and Naïve Bayes. These algorithms serve as effective tools in handling predictive data. Early prediction of diabetes holds the potential to control the disease and save lives. Therefore, the focus of this research is to develop a predictive model for diabetes status by utilizing various algorithms, but the level of validation of this model still needs to be tested. The dataset utilized consists of information from several diabetic patients, including eight input variables (pregnancies, glucose levels, blood pressure, skin thickness, insulin levels, BMI, age, and diabetes pedigree function) and one output variable (diabetes status). Research findings indicate that the SVM algorithm exhibits superior accuracy (84%) in predicting diabetes status compared to other algorithms such as neural network, Random Forest, Naïve Bayes, and kNN.
Analyzing Factors that Influence Student Performance in Academic Hidayani, Nieta; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
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.v5i2.221

Abstract

Student performance analysis is a complex and popular study area in educational data mining. Multiple factors affect performance in nonlinear ways, making this topic more appealing to academics. The broad availability of educational datasets adds to this interest, particularly in online learning. Although previous studies have focused on analyzing and predicting students' performance based on their classroom activities, this study did not take into account student's outside conditions, such as sleep hours, extracurricular activities, and a sample of question papers that they had practiced.  These three variables are included among others in our study. In this paper, we describe an analysis of 10,000 student records, with each record containing information on numerous predictors and a performance index. The dataset intends to shed light on the relationship between predictor variables and the performance indicator. To create the correlation variable heatmap, we use both univariate and bivariate studies to produce a linear equation. Following that, we perform data preprocessing and modeling to facilitate predictive analysis. Finally, we showed the outcomes of actual and expected student performance using the model we constructed. The findings demonstrate that our prediction model was 98% accurate, with a mean absolute error of 1.62. 
Implementation of Blended Learning System in Higher Education to Explore the Interaction of Technology, Organization, Environment, and Technology Acceptance Model Hidayah, Nur Aeni; Aini, Qurrotul; Ghania, Putri
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.v5i2.204

Abstract

A thorough examination of the deployment of blended learning after the COVID-19 pandemic is necessary to ensure its efficacy in enhancing the educational quality in higher education. The objective of this study is to identify the key elements that strongly impact the adoption of a blended learning system. It will be achieved by applying both the technology-organization-environment framework and the technology acceptance model. The study formulated eight hypotheses and administered online surveys on social media platforms to gather data from a total of 249 participants from four Islamic state universities. Analyzed data with the PLS methodology. The findings indicated that 92% of participants concurred that blended learning enhanced the quality of education. In addition, seven assumptions have been accepted, with the relationship between the technology context and the perceived ease of use in the blended learning system being the most relevant component. On the other hand, the PLS prediction results demonstrate that the suggested model possesses moderate predictive capability, as evidenced by its lower RMSE and MAE values in comparison to the linear regression model. Subsequent investigations should focus on analyzing the four blended learning models while taking into account factors such as teacher competence, educational systems, and social impacts.
An Extensive Exploration into the Multifaceted Sentiments Expressed by Users of the myIM3 Mobile Application, Unveiling Complex Emotional Landscapes and Insights Hayadi, B Herawan; Henderi, Henderi; Budiarto, Mukti; Sofiana, Sofa; Padeli, Padeli; Setiyadi, Didik; Swastika, Rulin; Arifin, Rita Wahyu
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.v5i2.187

Abstract

This study investigates user sentiment towards the myIM3 application, an application used for telecommunication service management in Indonesia. Using text analysis and machine learning methods, we analyzed user reviews to identify dominant sentiment patterns and evaluate different classification models. Word cloud analysis, sentiment distribution, and donut plots were utilized to gain deeper insights into user preferences and issues. Results indicate that the majority of user reviews are neutral (52.2%), with 37% positive reviews and 33.4% negative reviews. Users consistently pay attention to aspects such as internet connection (Neutral: 92%, Positive: 95%, Negative: 87%) and pricing (Neutral: 92%, Positive: 92%, Negative: 93%) in their reviews. Evaluation of classification models like Decision Tree Classifier, Support Vector Machine (SVM), and Random Forest shows that the SVM model performs the best with an accuracy of 93%, high precision (Negative: 93%, Neutral: 92%, Positive: 95%), recall (Negative: 93%, Neutral: 95%, Positive: 91%), and F1-score (Negative: 93%, Neutral: 94%, Positive: 93%). These findings can serve as a basis for service improvement and better product development in the future, while also affirming the capability of text analysis and machine learning techniques in providing valuable insights for telecommunication service providers.
Research on the Influencing Factors of College Students' Deep Meaningful Learning in Blended Learning Mode Li, Shu; Pasawano, Tiamyod; Sangsawang, Thosporn
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.v5i2.254

Abstract

This study examines the factors that impact deep and meaningful learning in blended learning environments and their connections. The sample included 397 college students from a university in Sichuan Province, selected through random sampling. Data was collected using a questionnaire based on Bandura's ternary interaction theory, encompassing learners, helpers, environment, and interaction dimensions. The following text should be remembered: "Hypotheses were developed based on existing literature, and a survey with established scales was created. Quantitative analysis was conducted using SPSS and AMOS software. The mean, standard deviation, Variance, skewness, and kurtosis values were within reasonable ranges. The model's latent variables showed strong convergent validity, with standardized factor loadings (SFL) ranging from 0.807 to 0.965, average Variance extracted (AVE) from 0.697 to 0.946, and composite reliability (C.R.) from 0.919 to 0.946. Model fit indices indicated acceptable fit (CMIN/DF: 2.303, NFI: 0.966, CFI: 0.980, RMSEA: 0.058, RMR: 0.008, PNFI: 0.789). The study optimized the model through path analysis, culminating in the final structural equation model (SEM)." Findings indicate (1) Learner, environmental, and interaction factors positively influence deep meaningful learning, while helper factors show a negative correlation; (2) learner, interaction, and helper factors mediate the environment's impact on deep, meaningful learning; and (3) environmental factors hold the most significant sway over helper factors, followed by interaction and learner factors. Helpers wield significant influence over learners, enhancing deep understanding. These insights guide effective, deep, meaningful learning strategies in blended learning
Mapping of Warehouse Radio Frequency Identification Research: A Bibliometric Analysis Auliana, Windi; Qurtubi, Qurtubi; Setiawan, Danang; Elquthb, Jundi Nourfateha
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.v5i2.246

Abstract

Warehouses play a vital role as an intermediary between entities in supply chains, connecting upstream and downstream entities. Implementing Radio Frequency Identification (RFID) technology as a warehouse management system enables data collection with more accuracy, speed, and reliability. This research was motivated by the limited bibliometric perspective and visualization of research on warehouse RFID. The use of bibliometric methods aimed to find basic patterns and an overview of the direction of research related to warehouse RFID. This research utilized the Publish or Perish and VOSviewer tools for analyzing purposes. This study comprised 172 Scopus journals that provide an extensive overview of the developmental progress over 2003-2023 period. Bibliometric visualization was conducted to investigate the outcomes from later publications connected to warehouse RFID. The visualization displayed the leading publishers, yearly patterns, prominent publication titles, top authors, most referenced papers, distribution of keywords, most influential journals, and areas of research that require more investigation.

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