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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 54 Documents
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Search results for , issue "Vol 6, No 2: MAY 2025" : 54 Documents clear
Enhancing the Performance of Machine Learning Algorithm for Intent Sentiment Analysis on Village Fund Topic Anam, M. Khairul; Putra, Pandu Pratama; Malik, Rio Andika; Karfindo, Karfindo; Putra, Teri Ade; Elva, Yesri; Mahessya, Raja Ayu; Firdaus, Muhammad Bambang; Ikhsan, Ikhsan; Gunawan, Chichi Rizka
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.v6i2.637

Abstract

This study explores the implementation of Intent Sentiment Analysis on Twitter data related to the Village Fund program, leveraging Multinomial Naïve Bayes (MNB) and enhancing it with Synthetic Minority Over-sampling Technique (SMOTE) and XGBoost (XGB). The analysis categorizes tweets into six labels: Optimistic, Pessimistic, Advice, Satire, Appreciation, and No Intent. Initially, the MNB model achieved an accuracy of 67% on a 90:10 data split. By applying SMOTE, accuracy improved by 12%, reaching 89%. However, adding Chi-Square feature selection did not increase accuracy further. Incorporating XGB into the MNB+SMOTE model led to a 6% improvement, achieving a final accuracy of 95%. Comprehensive model evaluation revealed that the MNB+SMOTE+XGB model achieved 96% accuracy, 96% precision, 96% recall, and a 96% F1-score, with an AUC of 99%, categorizing it as excellent. These findings demonstrate that the combination of SMOTE for addressing class imbalance and XGBoost for boosting performance significantly enhances the MNB model's classification capabilities. The novelty lies in the integration of these techniques to improve intent sentiment classification for public opinion analysis on the Village Fund program. The results indicate that the majority of tweets labeled as "No Intent" reflect a lack of specific sentiment or actionable intent, providing valuable insights into public perception of the program.
Improving Early Detection of Cervical Cancer Through Deep Learning-Based Pap Smear Image Classification Merlina, Nita; Prasetio, Arfhan; Zuniarti, Ida; Mayangky, Nissa Almira; Sulistyowati, Daning Nur; Aziz, Faruq
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.v6i2.576

Abstract

Cervical cancer is one of the leading causes of death in women worldwide, making early detection of the disease crucial. This study proposes a deep learning-based approach that has the advantage of leveraging pre-trained models to save data, time, and computation to classify Pap smear images without relying on segmentation, which is traditionally required to isolate key morphological features. Instead, this method leverages deep learning to identify patterns directly from raw images, reducing preprocessing complexity while maintaining high accuracy. The dataset used in this study is a public data repository from Nusa Mandiri University (RepomedUNM), which has a wider variety of data. This dataset is used to classify images into four categories: Normal, LSIL, HSIL, and Koilocytes. The dataset consists of 400 images evenly distributed, ensuring class balance during training. Transfer learning is applied using five Convolutional Neural Network (CNN) architectures: ResNet152V2, InceptionV3, ResNet50V2, DenseNet201, and ConvNeXtBase. To prevent overfitting, techniques such as data augmentation, dropout regularization, and class weight adjustment are applied. The evaluation results in this study showed the highest accuracy with a value of ResNet152V2 = 0.9025, InceptionV3 = 0.8953 and DenseNet201 = 0.8845. ResNet152V2 excelled in extracting complex features, while InceptionV3 showed better computational efficiency. The study also highlighted the clinical impact of misclassification between Koilocytes and LSIL, which may affect diagnostic outcomes. Data augmentation techniques, including horizontal and vertical flipping and normalization, improved the model's generalization to a wide variety of images. Specificity was emphasized as a key evaluation metric to minimize false positives, which is important in medical diagnostics. The findings confirmed that transfer learning effectively overcomes the limitations of small datasets and improves the classification accuracy of pap smear images. This approach shows potential for integration into clinical workflows to enable automated and efficient cervical cancer detection.
Trust Aware Congestion Control Mechanism for Wireless Sensor Network Priscilla, G. Maria; Kumar, B.L. Shiva; Maidin, Siti Sarah; Attarbashi, Zainab S.
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.v6i2.564

Abstract

Congestion in wireless sensor networks (WSNs) can occur from various factors, including resource limitations and the transmission of packets surpassing the capacity of receiving nodes. This congestion may arise from natural causes or be exacerbated by self-serving nodes. Furthermore, malicious sensor nodes within WSNs have the capability to instigate congestion-like scenarios by either flooding the network with redundant fake packets or maliciously discarding genuine data packets. Relying solely on conventional congestion control techniques proves inadequate for ensuring fair delivery, necessitating a proactive approach to prevent such adversities by segregating these nodes from the network. Existing congestion control strategies often make the unrealistic assumption that all nodes are authentic and behave appropriately. To address these challenges, a proposed Genetic Algorithm based Trust-Aware Congestion Control (GA-TACC) not only manages congestion under natural circumstances but also considers scenarios where hostile nodes deliberately improve packet delivery. The GA evaluates the credibility score (CS), contributing to enhanced performance, and GA-TACC demonstrates superiority over existing state-of-the-art techniques for wireless sensor network.
The Impact of Industrial Security Risk Management on Decision-Making in SMEs: A Confirmatory Factor Analysis Approach Almaiah, Mohammad; Mekimah, Sabri; zighed, Rahma; Alkhdour, Tayseer; AlAli, Rommel; Shehab, Rami
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.v6i2.543

Abstract

This study focuses on the importance of industrial risk management for small and medium-sized enterprises (SMEs) in Algeria, particularly given the administrative, economic, and financial challenges they face, as well as their limited experience in this field. Risk management serves as a strategic tool that aids institutions in achieving safety and sustainability by identifying potential risks that may lead to industrial disasters, such as chemical incidents and technical malfunctions, then analyzing, assessing, and responding to these risks in ways that minimize their impact on the safety of individuals, property, and the environment. The study aims to analyze the impact of risk management on SMEs' ability to make accurate and timely decisions during critical moments while fostering a culture of safety and proactive risk handling. To achieve these objectives, a survey was conducted on a sample of 390 Algerian industrial SMEs. The study employed the Confirmatory Factor Analysis methodology (CB-SEM) to analyze data from these SMEs, which helped in identifying core risk management processes such as risk description, analysis, and conclusion, and evaluating their effectiveness in supporting decision-making. The findings indicate that the impact of the risk description process on decision-making is positive but weak at 14.7%, while the impact of the risk analysis process on decision-making is also positive and weak at 18.9%. However, the effect of the risk conclusion process on decision-making was positive and moderate, at 64.8%. The results further reveal that SMEs that adopt a comprehensive and sustainable approach to risk management have a greater ability to manage disasters and ensure operational safety. The study highlights the importance of regularly reviewing safety protocols, providing training and simulations for employees, improving risk response strategies, and enhancing organizational performance. However, it was observed that some SMEs lack reliance on modern systems for risk avoidance. The study recommends the importance of allocating an independent budget to address potential risks, activating proactive systems for risk prediction, and employing internal and external experts for risk analysis. The study recommends that SMEs focus on developing mechanisms for describing and analyzing risks and collaborating with specialized entities to implement modern systems that support safety and sustainability. Additionally, it advises organizations to raise employees' awareness and provide training on risk handling to enhance the effectiveness of risk management and ensure business continuity.
FUZRUF-onto: A Methodology to Develop Fuzzy Rough Ontologies Sanyour, Rawan; Abdullah, Manal
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.v6i2.625

Abstract

Nowadays, semantic web technologies play a crucial role in the knowledge representation paradigm. With the rise of imprecise and vague knowledge, there is an upsurge demand in applying a concrete well-established procedure to represent such knowledge. Ontologies, particularly fuzzy ontologies, are increasingly applied in application scenarios in which handling of vague knowledge is significant. However, such fuzzy ontologies utilize fuzzy set theory to provide quantitative methods to manage vagueness. In various cases of real-life scenarios, people need to express their everyday requirements using linguistic adverbs such as very, exactly, mostly, possibly, etc. The aim is to show how fuzzy properties can be complemented by Rough Set methods to capture another type of imprecision caused by approximation spaces. Rough sets theory offers a qualitative approach to model such vagueness via describing fuzzy properties at multiple levels of granularity using approximation sets. Using rough-set theory, each fuzzy concept is represented by two approximations. The lower approximation PL(C) consists of a set of fuzzy properties that are definitely observable in the concept. The upper approximation PU(C) on the other hand contains fuzzy properties that are possibly associated with the concept but may not be observed. This paper introduces a methodology named FUZRUF-onto methodology, which is a formal guidance on how to build fuzzy rough ontologies from scratch using extensive research in the area of fuzzy rough combination. Fuzzy set and rough set theories are applied to capture the inherently fuzzy relationships among concepts expressed by natural languages. The methodology provides a very good guideline for formally constructing fuzzy rough ontologies in terms of completeness, correctness, consistency, understandability, and conciseness. To explain how the FUZRUF-onto works, and demonstrate its usefulness, a practical step by step example is provided.
Generating Image Captions in Indonesian Using a Deep Learning Approach Based on Vision Transformer and IndoBERT Architectures Apandi, Ahmad; Mutiara, Achmad Benny; Dharmayanti, Dharmayanti
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.v6i2.672

Abstract

The primary objective of this research is to develop an image captioning system in Indonesian by leveraging deep learning architectures, specifically Vision Transformer (ViT) and IndoBERT. This study addresses the challenge of generating accurate and contextually relevant captions for images, which is a crucial task in the fields of computer vision and natural language processing. The main contribution of this research lies in integrating ViT for visual feature extraction and IndoBERT for linguistic representation to enhance the quality of image captions in Indonesian. This approach aims to overcome limitations in existing models by improving semantic understanding and contextual relevance in generated captions. The methodology involves data preprocessing, model training, and evaluation using the Flickr8k dataset, which was translated into Indonesian. The research employs various data augmentation techniques to enhance model performance. The model is trained on a combined architecture where ViT extracts visual features and IndoBERT processes textual information. The experimental procedures include training the model on the Indonesian-translated Flickr8k dataset and evaluating its performance using BLEU and METEOR scores. The training loss and validation loss graphs provide insights into the model’s learning process. The results indicate that the proposed model outperforms traditional CNN+LSTM and Transformer-based models in terms of BLEU and METEOR scores. A detailed analysis of these results highlights the advantages of using ViT and IndoBERT for this task. The findings of this research have significant implications for real-world applications, such as automatic image captioning for visually impaired users, content tagging for multimedia platforms, and improvements in machine translation. Future research can explore the integration of human evaluation metrics and the use of larger datasets to enhance generalizability.
Impacting Technology on Employees' Job Performance: A Case Study of Commercial Banks in Vietnam Hien, Lam Thanh; Tam, Phan Thanh
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.v6i2.630

Abstract

Technology is driving rapid transformation in the way people work. Many banks have caught up by introducing technology into their operations and training their personnel to use technology to optimize work productivity. Besides, employee technology competency is one of the most critical issues for organizations, helping them maintain a competitive position in the market. Therefore, the research aimed to measure critical factors impacting on bank employees' job performance and policy recommendations. The methodology of this study applied a structural equation model consisting of five factors: knowledge, attitude, hard skills, soft skills, and technology, and examined the impact of the above factors on tasks, context, and job performance. Data were collected from 900 employees working for 40 branches at 15 commercial banks in Vietnam and processed using SPSS 20.0 and Amos software. After testing the scale’s reliability, convergence, and discrimination, the study's findings showed that the critical factors of knowledge, attitude, skills, and technology positively impact task, contextual, and job performance. In addition, the originality of this research includes the introduction of technological factors into the model, a new factor of the banking industry in the digital transformation period in Vietnam. Based on the results of testing the research model, the authors provided empirical evidence that the career competency framework includes critical factors: knowledge, attitude, skills, and technology that positively impact task performance, contextual performance, and employee job performance. The practical implications of the article proposed management implications to help employees, managers, and policymakers improve employees' knowledge, attitudes, and skills, which play an essential role in performing their duties, and improving job performance in digital competency is one of the required skills in the banking industry.
Dimension-Expanding MLP in Transformer: Inappropriate Sentences and Paragraph Digital Content Filtering Wardhana, Ariq Cahya; Yunus, Andi Prademon; Adhitama, Rifki; Latief, Muhammad Abdul; Sofia, Martryatus
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.v6i2.627

Abstract

The creation of digital content is now a pivotal element of today’s digital environment, driven by the need for both individuals and organizations to engage audiences effectively. As digital platforms grow in scope and impact, ensuring the security, professionalism, and appropriateness of user-generated content has become crucial. This study introduces a new approach for filtering inappropriate digital content by integrating dimension-expanding multi-layer perceptions (MLPs) into transformer architectures. The dimension-expanding MLP processed more high-dimensional features in the Transformers network, giving the ability to understand more specific contexts. Experimental findings reveal that the proposed model outperforms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), Transformer (Baseline) in accuracy, computational efficiency, and scalability. The research highlights the model’s practical applications in areas like social media content moderation, legal document compliance monitoring, and filtering harmful content in e-learning and gaming platforms with 0.744 accuracy.
Detection of COVID-19 using EfficientnetV2-XL and Radam Optimizer from Chest X-ray Images Alshalabi, Ibrahim Alkore; Alrawashdeh, Tawfiq; Abusaleh, Sumaya; Alksasbeh, Malek Zakarya; Alemerien, Khalid; Al-Eidi, Shorouq; Alshamaseen, Hamzah
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.v6i2.512

Abstract

Automating the detection of the COVID-19 pandemic has become necessary for assisting radiologists and medical practitioners in the diagnosis process. It enables them not only to save time through early diagnosis but also to ensure that they are making more accurate diagnoses. Therefore, this research presents a novel approach for automatically identifying COVID-19 in chest X-ray images by utilizing the EfficientNetV2-XL model in combination with the Rectified Adam optimizer for training. For conducting the experiments, we used the dataset available on Kaggle, known as the “COVID-19 Radiography Dataset.” The totality of this dataset was 21,165, and it included four patterns: COVID-19, viral pneumonia, lung opacity, and normal cases. The dataset was divided into 80% training and 20% testing. The preprocessing stage included resizing images to 512 × 512 pixels and then applying data augmentation techniques to enhance model robustness. Consequently, a fine-tuned multiclass categorization system was implemented. The proposed system's effectiveness is evidenced by the experimental outcomes, which show a 99.31% accuracy rate and a perfect Area Under the Curve score of 1 for identifying COVID-19. Additionally, the Score-CAM visualization method was utilized to enhance the interpretability of model predictions, identifying key regions within the chest X-ray images that influence the classification outcome. This Localization technique aids healthcare professionals in understanding the reasoning behind the model and confirming the accuracy of the diagnosis. The proposed system outperformed the state-of-the-art models for COVID-19 detection. 
Recognizing Fake Documents by Instance-Based ML Algorithm Tuning with Neighborhood Size S., Prakash; B., Kalaiselvi; K., Sivachandar; 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.v6i2.654

Abstract

The primary objective of this research paper is to classify spam SMS messages for scamming threats as soon as they are received on a device. The study focuses on evaluating the performance of K-Nearest Neighbors (KNN) classifiers with different neighborhood sizes to determine the most effective machine learning technique for improving accuracy and predictions in SMS spam detection. SMS is a short text messages service that permits mobile phone users to exchange messages.  In today’s world, people are so much tending towards mobile phones and it has become easy to spread spam content through them. One can easily access any person’s details through these social networking websites. No information which is shared and stored in the device is not secure. Numerous anti-spam systems have been developed. In this paper, we compare the classification results against spam SMS data to estimate the effectiveness of the KNN classifiers at different k levels and the comparisons shown. An effective method of classifying spam SMS, based on the metrics like F-measures, Precision, and recall score is recognized from the experiment results. The best performance was achieved with K = 4, where the classifier provided a high accuracy of 94.78% and strong results across all key performance metrics. The research highlights that feature selection plays a crucial role in improving classification efficiency by eliminating irrelevant or redundant features. Although KNN is a simple and effective approach, its scalability and real-time processing limitations suggest that future work should explore deep learning, ensemble models, or heuristic-based optimization for further improvements and support process innovation.

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