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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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Intangible Cultural Heritage Based on AR Technology Tong, Chaoran; Hee-Gyun, Kim
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.v3i3.64

Abstract

With the development of society and the improvement of people's material living standards, Internet technology has penetrated into all aspects of people's lives, and AR technology has gradually entered people's lives. At the same time, all sectors of society are paying more and more attention to the development and protection of ICH and other traditional cultures. Although many experts and scholars have conducted research and discussion in this area before and after, due to the late start time, shortage of technology and incomplete technical personnel and other issues, no good solution has been found, and little gain has been achieved. Based on this, this article adopts a new perspective, using the advantages of the modern big data society, through the current advanced AR technology and digital management, aiming to find the best plan for the development and protection of ICH, with a view to the domestic ICH Provide reference and reference for development and protection. This article uses data analysis, comparison and experiment methods. It first introduces the theoretical aspects of ICH and proposes specific measures for its digital development and protection, and then quotes the traditional cultural heritage of shadow play as as a specific example, 60 audiences were randomly surveyed by questionnaire survey, and divided into 3 groups according to age, and they were subjected to issues such as viewing attitude and effect of traditional shadow play based on AR technology and traditional shadow play according to the investigation, it is concluded that the traditional shadow play based on AR technology is more popular with the audience, and compared with the traditional shadow play, the viewing effect is better.
A Grid-search Method Approach for Hyperparameter Evaluation and Optimization on Teachable Machine Accuracy: A Case Study of Sample Size Variation Malahina, Edwin Ariesto Umbu; Iriane, Gregorius Rinduh; Belutowe, Yohanes Suban; Katemba, Petrus; Asmara, Jimi
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.v5i3.290

Abstract

This study aims to evaluate the effectiveness of the grid-search method in hyperparameter optimization on Teachable Machine (TM) using a varying number of image samples. The hyperparameters studied include epoch (e), batch size (b), and learning rate (l). A structured grid-search method approach will be applied to test 216 hyperparameter combinations across 6 categories of sample size per class, namely 10, 25, 50, 100, 250, and 500. The results showed that the optimal combination findings were obtained based on variations in the number of samples as follows: 10 samples using e:100, b:256, l:0.001 get an accuracy range of ≥ 90%; for 25 samples using e:500, b:16, l:0.001 get an accuracy range ≥ 97%; for 50 samples using e:100, b:512, l:0.001 get an accuracy range ≥ 88%; for 100 samples using e:500, b:32, l:0.001 get an accuracy range ≥ 88%; for 250 samples using e:50, b:16, l:0.001 get an accuracy range ≥ 92%, and finally 500 samples using e:500, b:256, l:0.001 get an accuracy range ≥ 96% and on average are able to achieve 100% accuracy from the detection test results of the best value performed for each sample variation of the image object. This research provides significant contributions or benefits in finding the optimal hyperparameter configuration, minimizing overfitting, and shortening the search time for TM accuracy in image classification, particularly in human face recognition. The findings support the development of more efficient and accurate TMs and provide practical guidance for finding better hyperparameter optimization using the grid-search method approach. The results of this study have implications for improving the effectiveness and accuracy of TM models and their development in mobile web applications
Optimizing Emergency Logistics Identification: Utilizing A Deep Learning Model in the Big Data Era Sumathi, V.; Shivakumar, B. L.; Maidin, Siti Sarah; Ge, Wu
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.v5i3.369

Abstract

This study investigates the dynamics of commodity flow across different facilities and settings, evaluating the performance of Simulation and Feed Forward Neural Network (FFNN) methods in optimizing these flows. Analyzing data from various configurations, the research reveals significant variations in commodity distribution patterns. At Facility_1 from the K1 disposer market, the flow of Commodity_1 increased from 770 units to 830 units, while Commodity_2 decreased from 192 units to 166 units. At Facility_2, Commodity_1's flow decreased from 851 units to 793 units, and Commodity_2's flow slightly increased from 139 units to 148 units. Similar trends are observed at facilities from the K2 disposer market, reflecting the complex impact of different settings on commodity flow. The comparative analysis of Simulation and FFNN methods demonstrates their relative strengths. In Setting I, the Simulation method achieved an objective value of 1,800,574.36 Rs with a computation time of 46.78 seconds, while the FFNN method yielded a slightly lower objective value of 1,800,352.24 Rs in 42.01 seconds. In Setting II, the Simulation method provided an objective value of 1,801,025.36 Rs with a computation time of 103.86 seconds, whereas FFNN achieved a comparable objective value of 1,800,847.27 Rs in 63.05 seconds. In Setting III, Simulation resulted in an objective value of 1,801,527.36 Rs with a computation time of 61.12 seconds, while FFNN produced a higher objective value of 1,806,997.32 Rs in 50.03 seconds. The results highlight the trade-offs between solution quality and computational efficiency. The Simulation method consistently delivers higher objective values but requires more time, making it suitable for applications where result accuracy is crucial. Conversely, the FFNN method offers faster computation with competitive or superior objective values, making it advantageous for scenarios where time constraints are significant. This study underscores the importance of selecting appropriate computational methods based on specific operational needs, optimizing both the efficiency and effectiveness of commodity flow management.
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.
To Retrench or Invest? Evaluating the Turnaround and Recovery Strategies of Indonesia MNEs through Data Science Approaches Abdillah, Willy; Nofiani, Delly; Adi, Maria Paramastri Hayuning; Marlina, Eka
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.v5i3.270

Abstract

This study aims to investigate the turnaround and recovery strategies employed by Ecolab International Indonesia MNEs after facing significant financial decline caused by the COVID-19 pandemic. This research analyzed internal factors, external opportunities, and threats to define effective strategies using a multi-method approach. Qualitative interviews were conducted to identify key themes, supported by examining company publications, especially annual financial reports from 2018 to 2022, to understand economic trends before, during, and after the pandemic. Thematic analysis was utilized to analyze the results, involving coding interview transcripts using ATLAS.ti and validating these themes through member checking to ensure reliability. Our findings show that Ecolab's turnaround strategy (cost reduction, enhancing value in sustainability, restructuring leadership, and organizational culture) was essential in addressing immediate and long-term challenges. The recovery strategy (operational and financial strategies, strategic focus area, regulatory and market dynamics) helped the company navigate the pandemic's impact and its position for sustainable growth. This study breaks new ground by integrating sustainability into strategic frameworks, aligning with global trends. Offering fresh perspectives enhances the relevance and value of MNEs' corporate strategy research in emerging markets. Additionally, our findings provide actionable insights for other companies to effectively incorporate sustainable practices into their turnaround and recovery strategies, ensuring long-term growth and regulatory compliance.
Sustainable Educational Data Mining Studies: Identifying Key Factors and Techniques for Predicting Student Academic Performance Murnawan, Murnawan; Lestari, Sri; Samihardjo, Rosalim; Dewi, Deshinta Arrova
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.v5i3.347

Abstract

This research paper presents a systematic literature review of sustainable educational data mining (EDM) studies published between 2017 and 2022 with the objective of identifying the primary factors that affect student academic performance. The purpose of this study is to provide a comprehensive analysis of sustainable EDM research and identify the most important factors that influence student performance while highlighting commonly used data mining techniques in the EDM field. The results suggest that student demographics, previous grades and class performance, social factors, and online learning activities are the most common and widely used factors for predicting student performance in educational institutions. Furthermore, Decision Trees, Naive Bayes, and Random Forests are the most frequently used categories of data mining algorithms in the studies included in the dataset. The methodology used in this study is a systematic literature review, which is a widely used technique for literature review that provides a reliable and unbiased process for reviewing data from diverse sources. The findings of this study provide valuable insights into the factors influencing student performance in educational institutions and can be used by researchers to inform future research and identify relevant factors to consider when predicting student performance.
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
Logistic Regression Analysis of Factors that Influence User Experience in Student Medical Report Applications Wahyuningrum, Tenia; Prasetyo, Novian Adi; Fitriana, Gita Fadila; Permadi, Dimas Fanny Hebrasianto; Setyawati, Rr.; Yuliansyah, Joewandewa; Sambath, Khoem
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.v5i3.285

Abstract

Monitoring student health efficiently requires collaboration between schools and government health services. Traditional methods often need more agility and user-friendliness, leading to delays and inaccuracies. This research aims to verify a fast and agile student medical report that we have previously developed using the Modified Agile User Experience (UX) method, with a focus on simplicity, usability, and accessibility. The system’s evaluation employs non-functional testing methods to identify factors influencing user satisfaction within the scope of the user experience. We measure task-level and overall user satisfaction using the Single Ease Questions (SEQ) questionnaire as the response variable. This study also investigates test-level satisfaction as predictor variables using Usability Metric for User Experience (UMUX) and UMUX-Lite questionnaire as predictor variables, as well as each student’s Interest in learning and learning motivation concerning test-level satisfaction. Binary Logistic Regression (BLR) analysis determined the relationship between test-level and task-level satisfaction, revealing significant correlations between these variables. Based on the results, the Interest to Learn variable is the most important factor that influences task-level satisfaction, but with a small probability value (42.9%). To ensure these accurate results, we changed the scale on SEQ from Easy and Hard to seven scales with normalized values. We compared the results using 4 algorithms: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting as the most effective model. For a test size of 0.2 and a random state of 40, Logistic Regression achieved an accuracy of 0.80 and a Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) score of 0.83. Random Forest also had an accuracy of 0.80 but a slightly lower ROC AUC score of 0.77. SVM also performed well, with accuracies of 0.83 and ROC AUC scores of 0.77. Gradient Boosting showed the lowest performance with an accuracy of 0.77 and a ROC AUC score of 0.73. These results indicate that Logistic Regression is the most robust model for predicting user satisfaction. Significant data correlations between SEQ, UMUX, and UMUX-Lite guide the development of user-centered applications, enhancing the effectiveness of educational tools by ensuring higher user satisfaction. Future research should consider more extensive, more diverse samples and additional factors influencing user experience to refine these models and their applications.
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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