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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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AI Prediction Model to Investigate the GovTech Maturity Index (GTMI) Indicators for Assessing Governments’ Readiness for Digital Transformation AlMurtadha, Yahya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.373

Abstract

Digital transformation helps governments improve their efforts to provide services to beneficiaries around the clock. However, governments must consider the potential disadvantages of unplanned digital transformation such as lack of attention to cybersecurity standards, which could put citizens' data at risk, or resistance to change and adoption of new technologies by government employees. The goal is for governments to take a comprehensive, well-planned approach to digital transformation that addresses people, processes and technology. Hence, governments should utilize digital maturity models to assess their current state and develop a plan for successful digital transformation. Governments especially are seeking for a smart transition to a mature digital transformed state. Therefore, this study proposes using the digital transformation maturity index as a systematic framework for governments to assess their digital transformation and plan a successful digital transformation. This study suggests using AI prediction algorithms to chart a path for a mature digital transformation.  Hence, this study builds a model that predicts government maturity level to one of four maturity classes (A, B, C, and D) using several AI prediction techniques on the World Bank GovTech dataset, which contains 48 important indicators used to measure the GovTech maturity index.  The results show that decision tree algorithm outperforms other approaches in terms of prediction accuracy. Government’s experts may thus utilize decision trees to determine the digital transformation maturity index success route starting at the root and working their way up to the leaf.  The results also highlight the need for a government to examine three essential indicators for a successful digital transformation with higher maturity class: universally accessible citizen-centric public services, a national strategy to connect all departments under one goal, and transparency. The study concludes that governments should embrace holistic and well-planned digital transformation while considering factors such as cultural and behavioral changes, future disruptions and emerging technologies.
Enhancing Apple Leaf Disease Detection with Deep Learning: From Model Training to Android App Integration Santoso, Cahyono Budy; Singadji, Marcello; Purnama, Denny Ganjar; Abdel, Saimam; Kharismawardani, Aqila
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.507

Abstract

This study presents an innovative approach to enhance apple leaf disease detection using deep learning by comparing three models: ReXNet-150, EfficientNet, and Conventional CNN (ResNet-18). The objective is to identify the most accurate and efficient model for real-world deployment in resource-constrained environments. Utilizing a dataset of 1,730 high-quality images, the models were trained using transfer learning, achieving significant results. ReXNet-150 outperformed other models with an F1-score of 0.988, precision of 0.989, and recall of 0.989. EfficientNet and ResNet-18 demonstrated competitive performances with F1-scores of 0.966 and 0.977, respectively. The integration of the ReXNet-150 model into a TensorFlow Lite-based Android application ensures real-time detection, enabling farmers and researchers to capture or upload images for immediate classification. The findings highlight ReXNet-150's robustness, achieving a test accuracy of 98.9% and minimal misclassification, making it ideal for practical agricultural applications. The novelty lies in bridging advanced deep learning with mobile deployment, addressing real-world constraints. Future work could extend this framework to multi-crop disease detection and real-time video analysis, providing scalable solutions for precision agriculture.
Novel Predictive Framework for Student Learning Styles Based on Felder-Silverman and Machine Learning Model Maulana Baihaqi, Wiga; Eko Saputro, Rujianto; Setyo Utomo, Fandy; Sarmini, Sarmini
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.408

Abstract

This study analyzes data from the Open University Learning Analytics Dataset to evaluate how students' interactions with Virtual Learning Environment (VLE) materials influence their final outcomes. This research aims to formulate and build a novel predictive framework based on the Felder-Silverman and Machine Learning Model for student learning styles. Based on these objectives, this research provides novelty and contributions since it enhances student data analysis, uses a learning model using Felder-Silverman Learning Style Model (FSLSM) to give a more comprehensive understanding of students' learning styles, and improves prediction accuracy by introducing Artificial Neural Network (ANN) and feature selection using Random Forest. The data used includes 3 main files: vle.csv, which contains information about the materials and activities in the VLE; studentVle.csv, which records students' interactions with the materials; and studentInfo.csv, which provides demographic information of students and their final outcomes. The analysis process involved data merging and processing, including handling of missing values, data type conversion, as well as mapping activity types to learning style features based on the FSLSM. We use the Random Forest feature selection method, as well as data imbalance handling techniques such as oversampling, to improve model performance. The applied classification models include Logistic Regression, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and ANN. The analysis results showed that after tuning, the Random Forest model achieved 97% accuracy, while SVM achieved 97% accuracy as well, with better performance than previous studies. This research highlights the importance of comprehensive data integration and appropriate processing techniques in improving the accuracy of student learning style prediction. Based on the increase in accuracy results, it can be beneficial for more effective personalized learning and improve our understanding of students' learning style preferences. The research advances knowledge and provides practical applications for educators to tailor their teaching strategies.
Quantitative Evaluation of Watercolor Brush Performance: A Comparative Study of User Satisfaction and Task Efficiency using 24 Innovative Brush Designs Chantanasut, Suraphan
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.462

Abstract

This study investigates the performance, user satisfaction, and durability of innovative watercolor brushes compared to traditional brushes, with a focus on quantifiable improvements. The innovative brushes, designed in collaboration with professional watercolorists, feature both roundhandled and flat-handled versions aimed at enhancing painting comfort, precision, and control. The researcher created an innovative watercolor brush with a total of 24 types, divided into 12 round-handled brushes and 12 flat-handled brushes. A sample of 24 artists, including both professionals and amateurs, completed three distinct painting tasks—still-life, large-area washes, and detailed line work. Quantitative data on task completion times, paint usage, and durability were collected, alongside user satisfaction ratings for comfort, ease of use, and stroke control. Statistical analysis revealed that the innovative brushes significantly outperformed traditional brushes across all metrics. On average, the innovative brushes reduced task completion times by 13-15%, with a mean of 13.88 minutes compared to 15.89 minutes for traditional brushes on the still-life task. Paint usage was also lower, with innovative brushes using approximately 2.44 grams on average for the still-life task, compared to 2.97 grams for traditional brushes, reflecting a 17.8% improvement in paint efficiency. User satisfaction ratings were consistently higher for the innovative brushes, scoring an average of 4.5 out of 5 for comfort, ease of use, and stroke control, in contrast to 3.5 for traditional brushes. Durability assessments further showed that innovative brushes maintained an average bristle condition rating of 4.6 versus 3.5 for traditional brushes after extended use, confirming superior longevity. These results highlight the impact of ergonomic handle design and advanced synthetic materials on brush performance. Recommendations for future brush designs include further refinement of handle shapes and enhanced bristle technologies to support the technical and artistic needs of watercolorists. While limitations such as the subjective nature of user ratings and sample size should be noted, this study lays the groundwork for continued research on performance metrics for art tools across various creative disciplines.
Deep Learning Incorporated with Augmented Reality Application for Watch Try-On Andri, Andri; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Alqudah, Mashal Kasem; Alqudah, Musab Kasim; Zakaria, Mohd Zaki; Hisham, Putri Aisha Athira binti
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.529

Abstract

In evaluating the dynamic landscape of online shopping, the integration of Augmented Reality (AR) technologies has emerged as a transformative force, redefining the way consumers engage with products in virtual environments. This research project investigates the intersection of deep learning and AR in the context of online shopping, with a particular focus on a Watch Try-On application. The experimentation involves the use of SSD MobileNet's models for real-time object detection aimed at enhancing the user experience during online watch shopping. Training both SSD MobileNet's V1 and V2 models through 50,000 iterations, the results reveal intriguing insights into their performance. SSD MobileNet's V1 demonstrated superior results, boasting a mean average precision (mAP) of 0.9725 and a significant reduction in total loss from 0.774 to 0.5405. However, the longer training time of 7 hours and 42 minutes prompted the selection of SSD MobileNet's V2 for real-time applications due to its faster inference capabilities. Extending beyond traditional online shopping experiences, the research explores the potential of AR technologies to revolutionize product visualization and interaction. The choice of the Vuforia model target for the Watch Try-On application showcases the synergy between deep learning and AR, allowing users to virtually try on watches and visualize them in their real-world environment. The application successfully detects users' hands with high accuracy, creating an immersive and visually enriching experience. In conclusion, this project contributes to the ongoing discourse on the fusion of deep learning and AR for online shopping. The exploration of SSD MobileNet's models, coupled with the integration of AR technologies, underscores the potential to elevate the online shopping experience by providing users with dynamic, interactive, and personalized ways to engage with products.
Performance Comparison of Whale and Harris Hawks Optimizers with Network Intrusion Prevention Systems Abualhaj, Mosleh M.; Al-Khatib, Sumaya N; Alsharaiah, Mohammad A; Hiari, Mohammad O
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.323

Abstract

Digital technology has permeated every aspect of our daily lives. Processing and evaluating information are highly demanding in all fields, including cybersecurity. Cybersecurity engineers widely use the Network Intrusion Prevention System (NIPS) to safeguard against cyberattacks. To avoid cyberattacks, the NIPS must deal with a large amount of data, which degrades its performance. This paper uses the whale optimization algorithm (WOA) and the Harris Hawks optimization method (HHO) to diminish the large amount of data that the NIPS needs to deal with. Subsequently, the Gradient Boosting Machine (GBM) is employed to determine the accuracy achieved when employing WOA and HHO. The GBM classifier is widely regarded as a sophisticated and straightforward classifier in data mining. Regardless of the premise of feature independence, it outperforms all other classification algorithms by delivering excellent performance. When using GBM, the findings indicate that the accuracy achieved with HHO is 89.81%, but the accuracy attained with WOA is 94.3%.
Empirical Study of the Correlation between Social Media Content and Health Issues among College Students Using Machine Learning Hemalatha, M.; Maidin, Siti Sarah; Sun, Jing
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.365

Abstract

This study analyzes the effect of social media content on college student addiction using data science techniques. It aims to examine the correlation between different types of social media content and addictive behavior in college students. The research methodology used is non-probability sampling with a sample size of 587 college students in Tamil Nadu, India. The study uses statistical tools such as correlation analysis, regression analysis, one-way ANOVA, and Friedman ranking test to analyze the data collected. The findings suggest that the factors influencing social media addiction are positively correlated with the health issues faced by college students. The study indicates that demographic variables such as age, gender, year in college, and place of living may play a role in shaping an individual's perception of social media addiction. The results of the study can inform the development of interventions and prevention strategies to reduce social media addiction among college students. The study recommends a multi-pronged approach to address the root causes of addiction and provide students with the tools and resources they need to manage their social media use and promote their physical and mental health.
Speech Enhancement using Sliding Window Empirical Mode Decomposition with Median Filtering Technique Selvaraj, Poovarasan; Maidin, Siti Sarah; Yang, Qingxue
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.470

Abstract

The Empirical Mode Decomposition is raising significant interest since its first introduction among the nineties. The attention in varied fields such as medical engineering, space analysis, hydrology, synthetic aperture measuring, speech enhancement, watermarking and etc. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by subsequently the least degraded IMF. Hereafter, in this article, SWEMD method is enhanced by using Sliding Window (SW) procedure. This research work has come SDG goals for health and well-being and also this research work concentrated on hearing aid application using noise level adjustment. In this SWEMDH method, the calculation of EMD is performed based on the small and sliding window along with the time axis. For each component, the total of sifting iterations is unwavering by decomposition of many signal windows by standard algorithm and calculating the average amount of sifting steps for each component. The median filter used for removed nonlinear components of this work. SWEMDH technique removed for low frequency Noisy Components. The speech quality was evaluation by the performance matrices of Mean Square Error, Perceptual evaluation of speech quality, signal to noise ratio, peak signal to noise ratio. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.
Optimizing Stunting Detection through SMOTE and Machine Learning: a Comparative Study of XGBoost, Random Forest, SVM, and k-NN Sugihartono, Tri; Wijaya, Benny; Marini, Marini; Alkayess, Ahmad Paqih; Anugerah, Hendra Agustian
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.494

Abstract

Stunting is a vital public health priority that affects millions of children from all over the world, especially in developing countries, where chronic malnutrition impairs their physical growth and cognitive development. Early detection of stunting is necessary for its timely intervention to reduce long-lasting effects. The following study deals with the application of higher-end machine learning techniques in order to detect stunting with more accuracy, using XGBoost, Random Forest, SVM, and k-NN algorithms. Using a dataset sourced from Kaggle, containing 10,000 samples of anthropometric and demographic features, we addressed the significant class imbalance of the data; the number of samples representing stunted children was only 15% of the total. We surmounted this limitation using SMOTE to generate synthetic data in order to balance the representation for this minority class. Further feature selection to improve the performance and interpretability of the model was done using backward elimination, where less impactful features like "Body Length" and "Breastfeeding" were systematically excluded, while putting more emphasis on more predictive variables such as weight, age, and socio-economic indicators. The evaluation of machine learning models showed significant improvements in performance with the integration of SMOTE and optimized feature selection, especially regarding recall and ROC-AUC metrics, which are critical in healthcare settings where the minimization of false negatives is of high importance. XGBoost was the best-performing model among those evaluated, yielding an accuracy of 0.8574, a recall of 0.8914, and an ROC-AUC of 0.9311, hence balancing precision and sensitivity more appropriately than other models. These results emphasize the efficiency of XGBoost in stunting detection while overcoming challenges arising from imbalanced datasets. It then illustrates the potential of merging machine learning techniques with synthetic data augmentation methodologies for the optimization of outcomes related to population health, and forms a basis for healthcare practitioners and policymakers by locating the at-risk children on time. The findings not only point to the importance of advanced data-driven approaches in stunting detection but also lay the ground for future research on machine learning applications in the fight against other malnutrition-related public health challenges, which could be crucial for improving child health and well-being across the world.
Osteoporosis Detection Using a Combination of Recursive Feature Elimination and Naive Bayes Classifier with Rule-Based Chatbot Testing Sela, Enny Itje; Rianto, Rianto; Anggara, Afwan; Utami, Wahyu Sri
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.409

Abstract

Osteoporosis is a condition characterized by reduced bone mass and density, increasing the risk of fractures. Early detection relies on patient awareness and proactive health management. Despite advances in technology, patient independence and awareness remain critical for early diagnosis. A rule-based chatbot tool can assist by helping patients screen their bone health. The chatbot provides automated recommendations, offering an alternative to traditional hospital visits. This study presents a rule-based chatbot designed to detect osteoporosis, using Recursive Feature Elimination (RFE) combined with the Naïve Bayes Classifier (NBC). Machine learning is integrated to enhance the chatbot's ability to identify early signs of osteoporosis. The model’s performance is compared to other feature selection methods, such as Principal Component Analysis (PCA), and machine learning algorithms like Deep Learning, Support Vector Machine (SVM), and Logistic Regression. The dataset used includes public data sets for training and validation, as well as data from the Yogyakarta Health Office for predictions. Research phases include normalization, data encoding, feature selection, training, validation, and prediction. The chatbot implements text preprocessing techniques, such as tokenization, stop word removal, and feature extraction, alongside normalization and encoding of numeric data. The prediction stage determines if the patient has a positive or negative osteoporosis status. Validation results show the RFE-NBC model is particularly effective for osteoporosis detection, offering a balanced performance in identifying both positive and negative cases. Additionally, this model served as the foundation for creating a rule-based chatbot designed to detect osteoporosis. Based on the set of testing metrics using chatbot, the model demonstrates strong overall performance, with a good balance between identifying positive and negative instances.

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