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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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Cellular Traffic Prediction Models Using Convolutional Long Short-Term Memory Samson, A Sunil; Sumathi, N; 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.v6i1.472

Abstract

Precise cellular traffic modeling and prediction is essential to future big data-based cellular network management for providing autonomic control and user-satisfied stable mobile services. However, the traditional methods have difficulty learning the complex hidden patterns of the users’ traffic data from cross-domains because of their shallow learning characteristics. Deep learning (DL)-based methods could somewhat identify these hidden patterns by learning the underlying spatial and temporal features and their dependencies. Yet, they too have constraints in handling the noisy and sparse data, reducing the prediction accuracy with increased computation time and associated storage costs. Therefore, this paper presents an intelligent cellular traffic prediction model (ICTPM) using two improved deep learning algorithms to tackle the negative impacts of noisy and sparse traffic datasets. Firstly, the Enhanced Stacked Denoising Auto-Encoder (ESDAE) is introduced to eliminate the noise in the traffic data by an adaptive Morlet wavelet transform. Secondly, Multi-dimensional Spatiotemporal Sparse-representation Convolutional Long Short-Term Memory (MDSTS-CLSTM) is used to learn the hidden patterns by extracting the spatial-temporal dependencies and predict the cellular usage in the presence of data sparsity problem. This MDSTS-CLSTM is developed by combining the Long Short-Term Memory (LSTM) with the Convolutional Neural Networks (CNN) and improvising the multi-dimensional feature learning, spatial-temporal analysis, and sparse representation properties of the hybrid DL algorithm. Evaluated over real-world cellular traffic cross-domain datasets from Telecom Italia and Open-CellID, the proposed ICTPM outperforms the state-of-the-art methods with 5-10% better performance enhancements.
Evaluating Deep Learning Architectures for Potato Pest Identification: A Comparative Study of NasNetMobile, DenseNet, and Inception Models Hadianti, Sri; Riana, Dwiza; Sulistyowati, Daning Nur
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.v6i1.545

Abstract

Manual potato pest identification that is still applied today is often time-consuming and highly dependent on farmer skills in the field. This causes delays in taking action and inaccurate reporting, especially in pest emergencies. In addition, these limitations slow down the response to pest control which ultimately risks reducing crop yields and farmer income. This study aims to develop a more accurate, fast, and consistent deep learning-based approach to identify potato pests, in order to support practical solutions that farmers can implement independently. This study contributes by comparing three deep learning architecture models, namely NasNetMobile, DenseNet, and Inception which are designed to identify pest images. The potato pest image dataset used was collected from various sources equipped with an augmentation process to increase data diversity. The model was drilled using transfer learning techniques to utilize previously learned features on a large dataset. The evaluation model was carried out comprehensively based on accuracy, precision, and inference time efficiency. The results showed that the DenseNet model achieved the highest accuracy of 97% with an inference time of 11 seconds, and this model maintained a relatively stable performance and was superior several times compared to other models. Based on these results, DenseNet was chosen as the most effective and reliable model to be developed for practical applications in the field. This study provides practical implications in the form of providing a model that can be integrated into a mobile-based application that is easy to use by farmers, including in remote areas. This allows farmers to identify pests independently without requiring in-depth technical expertise. In addition, this study is a new benchmark for the development of artificial intelligence-based pest identification systems in other crops and opens up opportunities for integration with IoT-based technologies to support sustainable agricultural practices.
Developing a Parallel Network Slack-Based Measure Model in the Occurrence of Hybrid Integer-Valued Data and Uncontrollable Factors Dzulkarnain, Syarifah Nurfuaduz Zakiah Habib; Nawawi, Mohd Kamal Mohd; Kashim, Rosmaini
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.v5i4.407

Abstract

This study develops an alternative approach to the parallel network Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, offering a more accurate and informative assessment of performance within a network system. Traditional DEA models solely focus on the input utilization and the outputs produced when assessing efficiency, disregarding the operation of internal processes within a network system. In addition, these approaches do not assess the concurrent requirement of hybrid integer-valued data and uncontrollable factors on efficiency measures. To address these gaps, we propose a novel approach to parallel network SBM DEA model that integrates hybrid integer-valued data with uncontrollable factors, aiming for a more precise evaluation. Both requirements were initially integrating into the existing method. Subsequently, the optimal solution for the proposed method was achieved by converting its fractional form into a linear one. Therefore, the measures of the proposed approach can now deal directly with controllable hybrid integer- valued input and output slacks. We applied this model to a dataset of 26 faculties in a Malaysian public university, followed by a comparative analysis with existing models. Empirical findings indicate that four (4) faculties are found to be overall effective, as all of their internal processes are effective, while the other faculties are ineffective since not all of their internal processes are effective. The results from our model enable decision-makers to identify ineffectiveness within network processes, thereby facilitating targeted improvements in system performance. By concentrating on the appropriate processes, management can enhance their overall effectiveness and internal effectiveness.
Predicting Network Performance Degradation in Wireless and Ethernet Connections Using Gradient Boosting, Logistic Regression, and Multi-Layer Perceptron Models Widiawati, Chyntia Raras Ajeng; Sarmini, Sarmini; Yuliana, Dwi
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.v6i1.519

Abstract

This study explores predicting network performance degradation in wireless and Ethernet connections using three machine learning algorithms: XGBoost, Logistic Regression, and Multi-Layer Perceptron (MLP). Key metrics, including accuracy, precision, recall, F1-score, and AUC-ROC, were employed to evaluate model performance. The MLP classifier achieved the highest accuracy (98.7%) and AUC-ROC (0.9998), with a precision of 1.0000 and recall of 0.8622, resulting in an F1-score of 0.9260. Logistic Regression provided reasonable baseline performance, with an accuracy of 93.67%, AUC-ROC of 0.9565, and an F1-score of 0.5992, but struggled with non-linear dependencies. XGBoost showed limited utility in detecting degradation events, achieving an F1-score of 0 despite a perfect AUC-ROC (1.0), indicating sensitivity to imbalanced data. Through hyperparameter tuning, MLP demonstrated robustness in capturing complex patterns in network latency metrics (local_avg and remote_avg), with remote_avg emerging as the most predictive feature for identifying degradation across both network types. Visualizations of latency dynamics demonstrate the higher predictive relevance of remote latency (remote_avg) in both network types, where spikes in this metric are closely associated with degradation. The findings underscore the effectiveness of using latency metrics and machine learning to anticipate network issues, suggesting that MLP is particularly well-suited for real-time, predictive network monitoring. Integrating such models could enhance network reliability by enabling proactive intervention, crucial for sectors reliant on continuous connectivity. Future work could expand on feature sets, explore adaptive thresholding, and implement these predictive models in live network environments for real-time monitoring and automated response.
Applied Density-Based Clustering Techniques for Classifying High-Risk Customers: A Case Study of Commercial Banks in Vietnam Nhat, Nguyen Minh
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.v5i4.344

Abstract

Understanding and effectively engaging with customers is paramount in today's rapidly evolving business landscape. With rapid technological advances, banks have unprecedented opportunities to improve their approach to customer segmentation. This change is driven by integrating resource planning systems and digital tools, enabling a more comprehensive and data-driven understanding of customer behavior. Therefore, the study aims to evaluate the performance of various density-based clustering algorithms in classifying customers at risk of default. The algorithms analyzed include K-Means, DBSCAN, HDBSCAN, and Birch, each offering unique strengths in handling diverse data structures. Using a dataset of 77,272 customers from Vietnamese commercial banks spanning 2010 to 2022, the study rigorously assesses these models based on seven critical metrics: Davies-Bouldin Index, Silhouette Score, Adjusted Rand Index, Homogeneity, Completeness, V-Measure, and Accuracy. The results indicate that density-based methods, particularly DBSCAN and HDBSCAN, excel in identifying high-risk clusters despite challenges in cluster separation and alignment with accurate data distributions. Birch demonstrates superior cluster separation and compactness but requires further refinement for optimal accuracy. The findings underscore the potential of integrating clustering methods into credit risk management frameworks, enhancing financial institutions' predictive accuracy and operational efficiency. This research contributes to the ongoing discourse on practical credit risk assessment tools, providing valuable insights for practitioners in the banking sector. Finally, once segments are identified, banks can tailor marketing messages, product offerings, and customer experiences to better suit each group. This can lead to reduced risk, improved customer satisfaction, higher conversion rates, and ultimately increased revenue and customer segmentation in the context of technology trends is becoming an indispensable part of modern business strategy
Implementation of Stacking Technique Combining Machine Learning and Deep Learning Algorithms Using SMOTE to Improve Stock Market Prediction Accuracy Munthe, Ibnu Rasyid; Rambe, Bhakti Helvi; Hanum, Fauziah; Amanda, Ade Trya; Hutagaol, Anita Sri Rejeki; andrianto, Richi
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.v5i4.421

Abstract

This study introduces a stacking technique that integrates machine learning (ML) and deep learning (DL) algorithms to enhance the accuracy of stock market trend predictions. The stacking model utilizes XGBoost and Random Forest as base models from the ML domain, while Logistic Regression and LSTM (Long Short-Term Memory) function as meta models to optimize predictive accuracy. A significant challenge in stock market data is class imbalance, where certain trends, such as stock price drops, are underrepresented. To mitigate this, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic data for the minority class. This approach helps the model better capture patterns from the underrepresented data while preserving essential information from the majority class. The implementation of SMOTE, coupled with the stacking technique, yielded a substantial improvement in prediction accuracy. The results showed that the Random Forest algorithm achieved an accuracy of 85% with precision, recall, and F1-score all at 85%, while XGBoost and Logistic Regression achieved accuracies of 82% and 81% respectively. For the deep learning models, LSTM reached an accuracy of 83%, while the Stacking Meta Model with LSTM achieved an accuracy of 83% with slightly better precision and recall at 84%. The stacking model, with Logistic Regression as the meta model, ultimately achieved the highest accuracy of 86%, outperforming individual models such as SVM (Support Vector Machine), LSTM, Random Forest, and Logistic Regression (LR). These findings demonstrate the efficacy of combining SMOTE with stacking to address data imbalance and improve stock market predictions. The novelty of this study lies in the integration of advanced ML and DL models within a stacking framework to handle class imbalance in financial datasets. Future research will explore the deployment of this model in a real-time web-based application to support investor decision-making in stock market trend analysis.
Enhanced Fall Detection using Optimized Random Forest Classifier on Wearable Sensor Data Afuan, Lasmedi; Isnanto, R. Rizal
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.v6i1.498

Abstract

This study aims to enhance the performance of fall detection systems for elderly care using wearable sensors by optimizing the Random Forest (RF) algorithm. Falls among the elderly are a major health risk, and timely detection can mitigate serious injuries or fatalities. The primary contributions of this research include developing an optimized RF model specifically tailored for real-time fall detection on resource-constrained devices such as smartwatches. Our approach involves feature engineering, hyperparameter tuning using Grid Search and Randomized Search, and model evaluation to achieve optimal performance. Key findings indicate that the optimized RF model achieved an accuracy of 92%, precision of 91%, recall of 89%, and an F1-score of 90%, with an average processing time of 0.045 seconds per prediction. These metrics underscore the model's capability for real-time deployment, demonstrating improved computational efficiency and predictive accuracy compared to traditional machine learning algorithms and deep learning models. The novelty of this study lies in its targeted optimization of the RF model to balance accuracy with low computational demand, addressing the limitations of existing methods that are either computationally intensive or prone to misclassification. This research provides a scalable solution for continuous fall monitoring, with significant implications for wearable healthcare technology, improving both accessibility and response times in elderly care. 
Applied Data Science and Artificial Intelligence for Tourism and Hospitality Industry in Society 5.0: A Review Hartatik, Hartatik; Isnanto, R. Rizal; Warsito, Budi
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.v5i4.300

Abstract

The primary purpose of this research is to delve into the emerging trends of artificial intelligence and data science with a specific focus on the tourism and hospitality sectors. A comprehensive methodology used to conduct this research includes collecting article data, conducting analysis and then conducting a review study on data science and artificial intelligence trends. These articles were selected based on metadata sourced from web of science and Scopus metadata. In particular, the research scrutinized and assessed the evolving trends in data science and artificial intelligence   within the hotel and tourism category. This analysis drew data from two prominent databases, Web of Science and Scopus, obtained a total of 4155 articles identified using the software and generated 124 terms in the articles with at least ten co-occurrence relationships. The findings of this study explain the huge potential, namely the trend of data application of science and artificial intelligence   in the tourism sector which is categorized in five distinct areas: forecasting tourist demand, implementing customized service recommender systems for the tourism industry, classifying tourist behavior patterns in automation, analyzing and understanding tourist behavior, developing tourist destinations, and planning itineraries. Additionally, the research anticipates a heavy emphasis on future studies on predicting travel demand. Looking ahead, this research extends the foundations laid by previous review studies primarily focusing on knowledge and forecasting methodologies in the tourism sector. The conclusions drawn in this research are well-supported by the evolving landscape of knowledge in this field. Furthermore, contributions of this research it offers valuable insights into the future directions of apllied data science and artificial intelligent research are represents the pioneering effort to analyze of applying machine learning to advance artificial intelligence and big data within the hotel and travel industries. The authors propose several avenues for future research in this domain based on the data unearthed.Additionally, the research anticipates a heavy emphasis on future studies on predicting travel demand. Looking ahead, this research extends the foundations laid by previous review studies primarily focusing on knowledge and forecasting methodologies in the tourism sector. The conclusions drawn in this research are well-supported by the evolving landscape of knowledge in this field. Furthermore, it offers valuable insights into the future directions of sentiment analysis research. Notably, this paper represents the pioneering effort to comprehensively analyze the methodology of applying machine learning to advance AI and big data within the hotel and travel industries. The authors propose several avenues for future research in this domain based on the data unearthed.
Fuzzy SAW Based Decision Model for Determining the Priority Scale of ICT Handling in Public Sector Organizations Yulanda, Rissa; Utama, Ditdit Nugeraha
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.v5i4.419

Abstract

Determining the priority of handling information and communication technology (ICT) infrastructure in public sector organizations can help them take the right actions in maximizing limited budgets, to handle technical maintenance, improve human resource (HR) capabilities and governance of ICT infrastructure. The purpose of this research is to develop a decision-making model that is able to determine the priority of handling ICT, especially in public sector organizations. Decision support modeling (DSM) with Fuzzy Simple Additive Weighting (Fuzzy SAW) method is used to build a computer model that supports decision making in this case. The study consists of four stages, which are an integral part of the Fuzzy SAW-based DSM process. These stages include analyzing the case, determining parameters, collecting data and building the model. This study produces a Fuzzy SAW-based DSM consisting of 14 parameters, namely governance, number of internet users, number of ICT managers, work experience of ICT managers, bandwidth service capacity, router device age, educational background of ICT managers, network firewalls, network maintenance, server room availability, Network Attached Storage (NAS) storage devices, neatly organized cable devices, adequate electrical resources and internet connection backup networks, to determine the priority ranking of 34 existing alternatives. The final result of this research is a Fuzzy SAW-based DSM that is able to provide a priority score for handling ICT infrastructure in Public Sector Organizations. The findings in this model show that the parameter weights affect the final score of the model. Thus, the conclusion of this research is that the model has been successfully implemented, making a significant contribution in providing guidance on determining accurate ICT infrastructure handling for public sector organizations.
Convolutional Neural Network Based Deep Learning Model for Accurate Classification of Durian Types Diana, Diana; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Alqudah, Mashal Kasem; Alqudah, Musab Kasim; Zakari, Mohd Zaki; Fuad, Eyna Fahera Binti Eddie
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.v6i1.480

Abstract

Durian recognition is significant among fans of the durian community since many people tend to get confused, especially if they are not familiar with durian species, which can lead them to be involved in durian fraud. The development of this prototype can detect and classify durian fruits into three categories, including Musang King, Black Thorn, and D24, which can significantly benefit consumers. The prototype in this research involves training using a dataset of durian images, specifically in Musang King, Black Thorn, and D24 varieties. Preprocessing techniques such as resizing and scaling data are applied to enhance the quality and consistency of the dataset. The models chosen to develop this prototype include VGG-16 and Xception, and each model is compared according to its accuracy percentage. The accuracy outcomes of VGG-16 and Xception models are 56.64% and 92%, respectively. The models used a total of 1,372 images of durian with three classifications. Based on the findings, further enhancement of the CNN models for durian classification can be done by implementing different architectures, techniques, and methods. Moreover, future models can consider real-time image capture and processing capabilities to enhance the practicality of the system for durian consumers. The prototype developed in this study demonstrates the feasibility of using deep learning techniques for accurate and efficient durian classification, paving the way for future advancements in automated fruit grading and quality control systems in the durian industry.

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