cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Husni Teja Sukmana
Contact Email
husni@bright-journal.org
Phone
+62895422720524
Journal Mail Official
jads@bright-journal.org
Editorial Address
Gedung FST UIN Jakarta, Jl. Lkr. Kampus UIN, Cemp. Putih, Kec. Ciputat Tim., Kota Tangerang Selatan, Banten 15412
Location
Kota adm. jakarta pusat,
Dki jakarta
INDONESIA
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 54 Documents
 2   3   4   5   6 
Search results for , issue "Vol 6, No 2: MAY 2025" : 54 Documents clear
Design of multiband antenna for full screen smartphone using ANSYS HFSS R., Karthick Manoj; K., Suresh Kumar; T., Ananth kumar; R., Nishanth; Joseph, Abin John; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.718

Abstract

The current scenario and the global trend are rapidly growing in terms of technology and connected through communication. The term communication is an extensive link and has an enormous number of inventions and technologies booming in this field. In mobile communication, numerous antennas are worked and fabricated in mobile phones. Here, in this paper, we focus on communication through mobile phones. It deals with simulating a multi-band slotted microstrip patch antenna for mobile phones with 6 GHz as the operating frequency using FR4 substrate material. This proposed antenna has resonating frequencies such as 2.732 GHz, 3.311 GHz,4.792 GHz,5.373 GHz, 6.462 GHz, 7.476 GHz, and 9.156 GHz. The parameters to be performed are VSWR, gain, directivity, return loss, and radiation pattern with the inputs as dimen-sion and frequency. The FR4 material might be used as the base substrate because flexi-bility is better, easily used for thin substrates and patch antennas, and the cost is meagre. The simulation is performed with the help of the ANSYS HFSS tool. When implemented in real-time in smartphones, the simulated output will be compatible in means of low power consumption and utilizes low area when fabricated. It is very efficient and has good performance. The antenna, thus simulated in this paper, will be proficient in a great way for the fifth-generation telecommunication field when designed further for process innovation
Intelligent Web Search Recommender System: An Application of Ensemble of Convolution Neural Network for Deep Semantic Content Analysis of Web Documents Chawla, Suruchi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.571

Abstract

Web Information retrieval is widely used for retrieving web documents relevant to the user search query. Search engines retrieve huge collection of web documents for a given search query and an information overload problem arises for the web user.  Web page recommender systems are widely used to deal with the information overload problem. Quality of the web page recommendations for a given search query depends heavily on the document feature representation. In this research a novel method is explained for Intelligent web search based on deep semantic content analysis of clicked web documents using an ensemble of convolution neural network. Deep learning model Convolution neural network has been used in the research for feature generation and it effectively represents the text characterization for classification. The optimized web document feature vector is generated using the ensemble of CNN is finally averaged at the output layer for clustering. The resulting clusters of optimal web documents optimized feature vector therefore groups semantic similar web documents in a given cluster for web page recommendations during web search. Experiment results confirm the improvement in average precision to 93% across all selected domains that shows the relevant web documents are increased in the recommendations based on clusters of web document optimal feature vectors generated using ensemble of CNN. Thus, the proposed system performs the Intelligent web search recommendations based on the deep semantic deep content analysis of web documents using an ensemble of CNN.
Kodein-Penetration: Recommendations of Customer Personalization Level in A CRM using Deep Learning Sudianto, Sudianto; Usman, Muhammad Lulu Latif; Prabowo, Dedy Agung; Gustalika, Muhamad Azrino; Marsally, Silvia Van; Akhmad, Fajar Kamaludin; Rakhma, Nazwa Aulia; Muna, Bunga Laelatul; Wicaksono, Apri Pandu; Rachman, Ari
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.597

Abstract

This study aims to develop a personalization-level recommendation model implemented in the Customer Relationship Management (CRM) system at PT Kodegiri, called KodeinPenetration. Personalization in CRM aims to improve customer interaction by providing more relevant recommendations based on their needs and preferences. To achieve this goal, this study tested several classification models using historical customer interaction data as the basis for analysis. The classification models tested included decision tree-based methods such as Random Forest, Gradient Boosting, and AdaBoosting, as well as deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). In addition, two main feature extraction techniques were applied to process text data, namely TF-IDF (Term Frequency-Inverse Document Frequency) and Tokenizer Padding. TF-IDF is used to represent words as numeric vectors based on their frequency of occurrence. In contrast, Tokenizer Padding is used in deep learning models to convert text into a numeric format that neural networks can process. The test results showed that the decision tree-based method using the TF-IDF feature produced the best accuracy of up to 82%. On the other hand, the deep learning model with GRU architecture utilizing Tokenizer Padding achieved the highest accuracy of 88.23%. This shows that the deep learning model has greater potential in handling sequential data and providing more accurate results compared to traditional methods. This study provides an important contribution to the development of deep learning-based personalized recommendation systems in CRM. By leveraging historical customer interactions, this system can improve user experience by offering more relevant and targeted services.
Towards Developing an AI Random Forest Model Approach Adopted for Sustainable Food Supply Chain under Big Data Miralam, Maram Saleh
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.680

Abstract

Big data presents a transformative solution for addressing operational challenges and emerging risks in the food industry while unlocking new opportunities. It enables the analysis and integration of complex, large-scale datasets that often suffer from poor quality and unstructured formats. Although big data is a well-established technique in supply chain management, several areas remain unexplored, particularly in the global food supply chain, which faces significant limitations such as environmental impact, resource wastage, and operational inefficiencies. Achieving sustainability requires enhancing food supply chain operations through data-driven methods. The integration of big data with artificial intelligence models, such as Random Forest, offers a more efficient and sustainable approach to optimizing resource utilization, minimizing waste, and improving overall efficiency. This study develops and implements an artificial intelligence-based Random Forest model, demonstrating its effectiveness in improving sustainability in the food supply chain. The model achieves an accuracy of 96%, outperforming traditional Linear Regression, which records 91% accuracy. Additionally, the F1-score for Random Forest is 0.89, compared to 0.84 for Linear Regression, highlighting its superior balance between precision and recall. The model also improves waste reduction by 17% and optimizes resource utilization by 22%, contributing to more efficient food supply chain operations. These findings underscore the potential of integrating big data analytics and AI-driven approaches to enhance sustainability and decision-making in global food supply chains.
A Proposed Model for Detecting Learning Styles Based on the Felder-Silverman Model Using KNN and LR with Electroencephalography (EEG) Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Yeh, Ming-Lang; Wijaya, Adi
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.659

Abstract

The identification of learning styles plays a crucial role in enhancing personalized education and optimizing learning outcomes. This research proposes a model for detecting learning styles based on the Felder-Silverman model using two machine learning algorithms: K-Nearest Neighbors (KNN) and Linear Regression (LR). Electroencephalography (EEG) data, known for its ability to capture cognitive and neural activity, serves as the primary dataset for this study. The proposed model was tested on a dataset comprising EEG signals collected during various learning tasks. Feature extraction and preprocessing techniques were employed to ensure high-quality input for the learning algorithms. The experimental results revealed that the LR-based model achieved an accuracy of 96.4%, significantly outperforming the KNN-based model, which obtained an accuracy of 89.9%. These findings highlight the potential of EEG-based models for accurately identifying learning styles, offering valuable insights for educators and researchers aiming to implement adaptive learning systems. This study demonstrates the feasibility and effectiveness of combining EEG data with machine learning techniques for learning style detection, paving the way for more personalized and efficient educational approaches. Future research will explore the integration of additional physiological data and advanced machine learning methods to further improve model accuracy and applicability.
Power Quality Assessment in Grid-Connected Solar PV Systems Using Deep Learning Techniques S., Dhivya; S., Prakash; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.655

Abstract

To address challenges in stability, power quality, and computational demands while supporting sustainable energy goals in grid-connected solar PV systems, this research introduces a novel deep learning approach: Adaptive Graph-Aware Reinforced Autoencoder with Attention-Based Neural Architecture Search (AGRAAN). AGRAAN simplifies and accelerates the development of neural networks by automatically identifying optimal architectures through Neural Architecture Search (NAS), enabling efficient learning from limited data using Few-Shot Learning, and enhancing performance through attention mechanisms for time-series forecasting. This integrated approach reduces manual tuning and adapts effectively to various tasks. High levels of solar PV integration in power grids introduce variability due to weather conditions and limited forecasting, often resulting in high operational costs. To address this, the AGRAAN model enhances real-time solar variability prediction, improving adaptability, cost-efficiency, and grid stability. NAS supports architectural optimization, Few-Shot Learning improves adaptability with minimal data, and attention mechanisms enhance forecasting accuracy. Additionally, high PV penetration causes voltage fluctuations and harmonic distortions in diverse grid environments. To mitigate these effects, a complementary system named Graph-Aware Reinforced Autoencoder Control System (GRAACS) is proposed. GRAACS detects and manages power quality issues using Autoencoders for anomaly detection, Graph Convolutional Networks (GCNs) for spatial prediction, and Reinforcement Learning for adaptive real-time control. The combined AGRAAN and GRAACS models significantly enhance performance, achieving a high efficiency score of 0.98, an F1-Score of 0.97, and a low Mean Absolute Error (MAE) of 0.11. These results demonstrate the effectiveness of the proposed AI-driven framework in optimizing solar PV grid integration for energy efficiency.
Instructional Strategy Competence Model for Pre-Service Teachers Using Data-Driven Approaches Tang, Lin; Pasawano, Tiamyod; Sangsawang, Thosporn
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.732

Abstract

The objectives of this study were to: (1) identify and analyze the factors influencing the instructional strategy competence of pre-service primary and secondary school teachers, (2) examine how these factors impact their competence, and (3) develop a comprehensive competence model incorporating personal, school, and social factors using data-driven approaches. The sample consisted of 17 Chinese experts and 320 pre-service teachers in Sichuan Province, selected through purposive random sampling. Data collection involved the Delphi method with experts to gather insights on influential factors and a structured questionnaire for pre-service teachers. Statistical analyses included Cronbach’s alpha for reliability, descriptive statistics (mean, standard deviation, interquartile range), exploratory factor analysis for structural validity, and structural equation modeling (SEM) using AMOS to assess factor influences. The results demonstrated strong internal consistency with a Cronbach’s alpha of 0.90. Expert responses showed a high level of consensus (mean = 4.86, standard deviation = 0.40, IQR = 1). The developed instructional strategy competence model was validated by experts and found to be highly appropriate for pre-service teachers.
Blended Teaching Model Optimization for Innovation and Entrepreneurship Courses through Data Analytics in Higher Education Yang, Liu; Sangsawang, Thosporn; Thepnuan, Naruemon; Chankham, Nawaphas; Kulnattarawong, Thidarat
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.734

Abstract

This study aimed to (1) develop a blended teaching model for Innovation and Entrepreneurship courses in Chinese higher education, and (2) assess the effectiveness of the proposed model. The sample consisted of 17 Chinese experts selected through purposive sampling and 30 higher education students from China. The research employed statistical analysis techniques including mean, standard deviation, coefficient of variation, and t-test to analyze the data. Results demonstrated significant improvements in students' entrepreneurship skills. In the experimental group, the pre-test mean score increased from 2.21 to 3.78 post-intervention, while the control group showed a slight improvement from 2.32 to 2.84. The standard deviation of learning outcomes decreased from 0.884 to 0.564, indicating a more consistent student performance. A statistically significant difference was observed (p = 0.003), confirming the effectiveness of the blended teaching model. These findings highlight the potential of blended learning in enhancing the quality of innovation and entrepreneurship education.
Adaptive Estimation for the Distribution Model of Golden Apple Snail (Pomacea canaliculata (Lamarck)) Pests Using Kernel and Spline Smoothers with Goldenshluger-Lepski Method Zulfikar, Zulfikar; Nasirudin, Mohamad; Susanti, Ambar; Sifaunajah, Agus
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.578

Abstract

The accuracy of the golden apple snail pest distribution model estimation is very much needed by farmers in dealing with pest attacks, especially in the rainy season. This research aimed to obtain the best distribution model of golden apple snail pests with kernel estimators and spline smoothing through the Goldenshluger-Lepski adaptive bandwidth selection method with an estimation error rate below 10%. The parameters measured were population density 7-42 days after planting, Morisita index, and environmental correlation. The results showed that the population density of golden apple snail pests from four research locations differed significantly in both the juvenile phase (PrF = 0.00161), pre-adult (PrF = 0.000872), and adult (PrF = 0.019122). The highest density was found in Bandar Kedungmulyo District (9.23 individuals.m-2), while the lowest was found in Megaluh District (6.37 individuals.m-2). The population pattern is evenly distributed with a Morisita index of less than one and the highest index (Id = 0.469) was recorded in Megaluh District. The best population distribution model was obtained using the optimum h(7) kernel smoothing estimator, with the lowest Mean Square Error (0.001), and Mean Absolute Square Error (0.032) values in Megaluh District. Furthermore, the best distribution model was obtained using the natural cubic spline smoother with the lowest Mean Square Error (0.055), and Mean Absolute Square Error (0.020) values in Tembeleng District. In conclusion, the best golden apple snail pest distribution model was obtained using the adaptive kernel smoothing estimator of the Goldenshluger-Lepsky model approach, which produced the lowest estimation error rate compared to the spline smoother. This research contributes to developing the best distribution model for golden snail pests, which can strengthen the information technology database for monitoring, controlling, and utilizing the potential of golden snail pests.
Automated Brain Tumor Analysis with Multimodal Fusion and Augmented Intelligence R., Karthick Manoj; S., Aasha Nandhini; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.719

Abstract

Brain tumor segmentation and classification are critical tasks in medical imaging, having a major impact on spotting and treating brain tumors. In the medical field, augmented intelligence has garnered a lot of attention lately since it emphasizes how human knowledge and artificial intelligence can be combined to enhance efficiency and decision-making in applications like brain tumor identification. This research concentrates on developing a novel approach utilizing Attention U-Net and Multimodal Transformers to assist doctors with precise tumor segmentation and classification while maintaining their critical clinical judgment. Attention U-Net is used to segment brain tumor because it efficiently collects detailed spatial data while focusing on key locations compared with traditional U-Net models. Multimodal Transformers provide reliable as well as effective feature extraction when utilized for early fusion to merge data from many modalities, such as T1, T2, and FLAIR This work utilizes CycleGAN-based data augmentation to supplement limited training data, thus improving the variety and quality of the dataset. The fused multimodal features are then utilized for the segmentation of the tumor and further classified as benign and malignant using hybrid transformer. The performance of the proposed system is assessed using standard metrics like accuracy for classification and Dice Similarity Coefficient and Intersection Over Union for segmentation. The proposed approach demonstrates high effectiveness in both segmentation and classification tasks, achieving 98 % accuracy showcasing its potential as a process innovation for clinical applications.

Page 4 of 6 | Total Record : 54

 2   3   4   5   6 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 7, No 1: January 2026 Vol 6, No 4: December 2025 Vol 6, No 3: September 2025 Vol 6, No 2: MAY 2025 Vol 6, No 1: JANUARY 2025 Vol 5, No 4: DECEMBER 2024 Vol 5, No 3: SEPTEMBER 2024 Vol 5, No 2: MAY 2024 Vol 5, No 1: JANUARY 2024 Vol 4, No 4: DECEMBER 2023 Vol 4, No 3: SEPTEMBER 2023 Vol 4, No 2: MAY 2023 Vol 4, No 1: JANUARY 2023 Vol 3, No 4: DECEMBER 2022 Vol 3, No 3: SEPTEMBER 2022 Vol 3, No 2: MAY 2022 Vol 3, No 1: JANUARY 2022 Vol 2, No 4: DECEMBER 2021 Vol 2, No 3: SEPTEMBER 2021 Vol 2, No 2: MAY 2021 Vol 2, No 1: JANUARY 2021 Vol 1, No 2: DECEMBER 2020 Vol 1, No 1: SEPTEMBER 2020 More Issue