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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
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.
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.
The Success Factors of E-Philanthropy are Determined Based on Perceived Trust, Perceived Usefulness, Subjective Norms, Enjoyment and Religiosity: A Case Study on a Charity Site Sukmana, Husni Teja; Nanang, Herlino; Agustin, Fenty Eka Muzayyana; Aristoi, Zidny Fiqha; Azizah, Khansa
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.v5i3.310

Abstract

The rapid development of information technology and social media has significantly influenced people's behaviors and preferences in various activities, including philanthropy. Traditionally, philanthropic activities necessitated direct interpersonal interactions. However, the advent of ephilanthropy has enabled more practical and accessible ways to engage in charitable activities anytime and anywhere using electronic technology. This study examines the perceived role of e-philanthropy users in Indonesia and their intention to make actual donations through crowdfunding for humanitarian purposes. The research integrates the Technology Acceptance Model (TAM) and the IS success model, supplemented by additional variables like trust, usefulness, subjective norms, and religiosity. Data were collected from 231 respondents across Indonesia using online questionnaires and analyzed using the PLS-SEM method. The findings indicate significant relationships between perceived quality and trust (t-value = 7.156, path coefficient = 0.681), trust and perceived usefulness (t-value = 31.724, path coefficient = 0.886), and religiosity and intention to use (t-value = 3.206, path coefficient = 0.360). However, perceived enjoyment (t-value = 1.100, path coefficient = 0.140), subjective norms (t-value = 1.448, path coefficient = 0.162), and perceived trust (t-value = 1.023, path coefficient = 0.128) did not significantly influence the intention to use e-philanthropy platforms. These insights can inform strategies to enhance user participation and trust in e-philanthropy initiatives in Indonesia.
Utilizing Support Vector Machine and Dimensionality Reduction to Identify Student Learning Styles within the Felder-Silverman Model Hananto, Andhika Rafi; Musdholifah, Aina; Wardoyo, Retantyo
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.v5i3.337

Abstract

This research explores the impact of questionnaire structure on the accuracy of learning style classification, focusing on the optimization of the Felder-Silverman Learning Style Model (FSLSM) using advanced machine learning techniques. By employing Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, the study identifies and retains the most informative variables from the original 44-question FSLSM instrument. These refined features are then processed through a Support Vector Machine (SVM) algorithm to evaluate classification performance across various core-to-secondary item ratios. Results indicate that the most optimal configuration—produced through the combined PCA-t-SNE reduction—achieved a peak accuracy of 89.54%, surpassing other configurations and highlighting the effectiveness of selective question modeling. This approach not only enhances prediction accuracy but also introduces a more efficient and streamlined FSLSM formula, reducing redundancy without compromising diagnostic precision. The study contributes to educational data mining by presenting a data-driven strategy for learning style assessment and offers practical implications for the development of adaptive, personalized learning systems grounded in statistically validated models.
Implementation of Scale-Invariant Feature Transform Convolutional Neural Network for Detecting Distracted Driver Fhadilla, Nahdatul; Sulandari, Winita; Susanto, Irwan; Slamet, Isnandar; Sugiyanto, Sugiyanto; Subanti, Sri; Zukhronah, Etik; Pardede, Hilman Ferdinandus; Kadar, Jimmy Abdel
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.v5i3.222

Abstract

A distraction while driving a vehicle may result in fatal consequences, namely accidents that may leave road users seriously injured or even dead. In order to mitigate this risk, it is imperative to establish a distracted driver detection system that is both precise and real-time. This research focuses on the application of artificial intelligence, with a particular emphasis on deep learning, which is achieved through the utilization of the Convolutional Neural Network (CNN) model. In order to enhance the detection of inattentive drivers and produce a more precise model, a scaleinvariant feature transform (SIFT)-CNN combination is proposed. The activities of the driver while operating a vehicle are categorized into ten categories in this study. One of these categories is considered a normal condition, while the remaining nine are classified as inattentive behaviors. This study implemented Adam optimization with 64 batches, a learning rate of 0.001, and epochs of 20, 25, 50, and 100. The proposed CNNSIFT model is capable of achieving superior performance in comparison to the solitary CNN model, as evidenced by the experimental results. The CNN-SIFT model has achieved 99% accuracy and a 0.05 loss when the hyperparameter configuration is optimized for 50 epochs. The analysis indicates that the accuracy of the features obtained from CNN-SIFT can be improved by approximately 1% compared with CNN to classify the type of driver distraction behavior. The model's reliability was further enhanced by its evaluation on test data, which resulted in high accuracy, precision, recall, and F1-score values. The model's ability to accurately identify driver behavior with a high degree of reliability is demonstrated by these results, which are a positive contribution to the improvement of road safety.
Deep Wiener Deconvolution Denoising Sparse Autoencoder Model for Pre-processing High-resolution Satellite Images Kiruthika, S.; Priscilla, G. Maria; Vijendran, Anna Saro; 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.v5i3.357

Abstract

The detection of geospatial objects in surveillance applications faces significant challenges due to the misclassification of object boundaries in noisy and blurry satellite images, which complicates the detection model's computational complexity, uncertainty, and bias. To address these issues and improve object detection accuracy, this paper introduces the Deep Wiener Deconvolution Denoising Sparse Autoencoder (DWDDSAE) model, a novel hybrid approach that integrates deep learning with Wiener deconvolution and Denoising Sparse Autoencoder (DSAE) techniques. The DWDDSAE model enhances image quality by extracting deep features and mitigating adversarial noise, ultimately leading to improved detection outcomes. Evaluations conducted on the NWPU VHR-10 and DOTA datasets demonstrate the effectiveness of the DWDDSAE model, achieving notable performance metrics: 96.32% accuracy, 86.88 edge similarity, 75.47 BRISQUE, 28.05 IQI, 38.08 PSNR (dB), 0.883 SSIM, 98.25 MSE, and 0.099 RMSE. The proposed model outperforms existing methods, offering superior noise and blur removal capabilities and contributing to Sustainable Development Goals (SDGs) such as SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). This research highlights the model's potential for inclusive innovation in object detection applications, showcasing its contributions and novel approach to addressing existing limitations.
Applied Regression Modelling to Recommend Green Business Development in Vietnam Nga, Lu Phi; Huy, Nguyen Quoc; Tam, Phan Thanh
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.v5i3.294

Abstract

The green businesses engaged in environmentally friendly business activities, minimizing negative impacted on the ecosystem and used resources effectively. Green businesses contributed to the country's sustainable development goals and brought practical benefits to the businesses themselves. Therefore, the article aimed to identify the key factors affecting green business development and proposed policy implications for green business development. Based on the study goal, the authors surveyed 400 managers of green enterprises in Vietnam and applied simulation modelling based on the quantitative research method. The findings explored five critical factors affecting green business development: (1) financial incentives, (2) corporate strategy and culture, (3) technological innovation, (4) mechanisms and policies, and (5) regulatory environment. The novelty of this study is that it increased the level of reputation and trust of customers, partners, and the community. Green businesses built a positive brand image to affirm their social responsibility and pioneering role in environmental protection. Green businesses could also attract and retain talent when employees feel proud and attached to the business's mission. A significant benefit of green businesses was their contribution to protecting the living environment for humans and animals. The article’s contributions helped to reduce greenhouse gas emissions, air, water, and soil pollution, reduce waste, and increase energy and resource efficiency. Green business development can also support nature conservation activities, habitat restoration, and biodiversity maintenance. The article's contributions proposed recommendations to management agencies in developing and implementing mechanisms and policies to support and promoted businesses to build in a green direction.
Machine Learning Algorithm Optimization using Stacking Technique for Graduation Prediction Herianto, Herianto; Kurniawan, Bambang; Hartomi, Zupri Henra; Irawan, Yuda; Anam, M Khairul
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.v5i3.316

Abstract

Graduating on time is crucial for academic success, impacting time, costs, and education quality. Hang Tuah University Pekanbaru (UHTP) is currently struggling to meet its goal of achieving a 75% on-time graduation rate. This study introduces an innovative approach using machine learning techniques, particularly ensemble learning with Stacking Machine Learning Optuna SMOTE (SMLOS), to address this issue. Our primary objective is to enhance data classification accuracy to predict student graduation timelines effectively. We employ algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (C4.5), Random Forest (RF), and Naive Bayes (NB). These were combined with meta-models, including Logistic Regression (LR), Adaboost, XGBoost, LR+Adaboost, and LR+XGBoost, to create a robust prediction model. To address class imbalance, we applied the Synthetic Minority Over-sampling Technique (SMOTE) and utilized Optuna for hyperparameter tuning. The findings reveal that SMLOS with the Adaboost meta-model achieved the highest accuracy of 95.50%, surpassing previous models' performances, which averaged around 85%. This contribution demonstrates the effectiveness of using SMOTE for class imbalance and Optuna for hyperparameter optimization. Integrating this model into UHTP's academic information system facilitates real-time monitoring and analysis of student data, offering a novel solution for promoting a Smart Campus through more accurate student performance predictions. This technique is not only beneficial for predicting student graduation but can also be applied to various machine learning tasks to improve data classification accuracy and stability.
Lower Limb Prosthetics using Optimized Deep Learning Model – a Pathway Towards SDG Good Health and Well Being S, Prakash; M, Jeyasudha; S, Priya; Batumalay, M
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.v5i3.239

Abstract

This research article aimed at revolutionizing prosthetic leg technologies to enhance accessibility, affordability, and environmental sustainability. With a focus on addressing the diverse needs of amputees globally, the program integrates principles of eco-design, community engagement, technological innovation, and policy advocacy to foster inclusive and resilient societies which leads to the attainment of Sustainable Development Goal (SDG) Good Health and Well Being. Lower Limb Prosthetics of Activity Recognition is an innovative field combining prosthetic technology and activity recognition systems. The challenge of activity recognition in lower limb prosthetics to optimize the performance and responsiveness of mock limbs. In this work, the problem is overcome by using the Optimized deep learning technique, which improves activity recognition in lower limb prosthetics. The proposed methodology consists of (1) Pre-processing (2) Feature extraction (3) Feature classification. The collected images are pre-processed via improved wavelet demonizing and Empirical mode decomposition. From pre-processed data, the features are extracted using an improved sliding window method. The obtained extracted features are moved on to the Feature classification process. The classification process is done by the Optimized Long short- term memory. They are designed to better capture dependencies and patterns in sequential data, which makes them highly effective for tasks involving time series, natural language processing and other sequential data problems. Optimization can be done by proper data preprocessing and tuning the data from data extraction. The weight of the LSTM model is optimized to improve the performance of this model by the improved Black Window Optimization Algorithm. The main contributions of the paper are to obtain the best classification accuracy, an optimized LSTM model is introduced in this paper, and the weight of the LSTM model is enhanced by the improved Black Window Optimization algorithm. It improves the performance of the proposed system.

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