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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
Using Evolutionary Optimization Techniques to Improve the Efficiency of Transportation Scheduling Shambour, Mohd Khaled Yousef
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.v6i2.581

Abstract

This study addresses the challenge of enhancing transportation efficiency during large-scale events, with a particular focus on the Hajj pilgrimage. Every year, more than two million pilgrims visit Makkah in Saudi Arabia to perform their Hajj rituals. The Haj ritual requires transporting vast numbers of pilgrims within a limited time, compounded by diverse transportation preferences that make timely, optimal scheduling complex. To tackle this, the study employs three optimization algorithms -Harmony Search (HS), Differential Evolution (DE), and Black Widow Optimization (BWO) - to optimize transportation schedules based on individual preferences. A comprehensive mathematical model was developed for this purpose, incorporating both hard and soft constraints that reflect the scheduling requirements and preferences of pilgrims. Experimental results show that the DE algorithm consistently outperforms HS and BWO, achieving the highest mean scores in 100% of scenarios with a population size of 100, 66.7% of scenarios with a population size of 20, and 16.7% of scenarios with a population size of 5. In contrast, BWO struggles to adapt to varying parameter settings, producing consistently lower-quality solutions. DE, in particular, performs exceptionally well with lower crossover probabilities, demonstrating its ability to balance exploration and exploitation effectively. On the other hand, HS yields better results when higher exploration probabilities are used, highlighting its strength in broader search space exploration. In contrast, the performance of BWO remains largely unaffected by variations in exploration and exploitation parameters, leading to consistently inferior solutions. These findings underscore the importance of dynamic parameter tuning for large-scale optimization tasks, suggesting that such approaches are promising for addressing complex scheduling challenges in major events like Hajj.
Decision Support Model for Determining Fuel in Boiler Machines Widyanto, Jeremia; Utama, Ditdit Nugeraha
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.v6i2.520

Abstract

This investigation seeks to formulate a Decision Support Model (DSM) aimed at identifying the most suitable fuel for boiler systems utilized in industrial contexts, encompassing three distinct fuel categories: natural gas, industrial diesel oil, and coal. The assessment is predicated on four fundamental criteria: cost, calorific value, safety, and emissions. Employing a synergistic methodology that combines Analytic Hierarchy Process (AHP) and Fuzzy Logic, AHP allocates weights to each criterion (cost: 0.503, calorific value: 0.273, safety: 0.145, emissions: 0.079). The Fuzzy Logic approach is utilized to effectively address uncertainty and process subjective assessments. The findings indicate that cost constitutes the paramount determinant, exhibiting the highest weight, succeeded by calorific value, safety, and emissions. In accordance with these weighted criteria, the fuels are ordered as follows: coal (0.794), natural gas (0.653), and industrial diesel oil (0.456). These results underscore that cost remains the predominant factor in fuel selection for industrial boilers, whilst safety and environmental ramifications concurrently exert significant influence. The originality of this inquiry is manifested in its implementation of an all-encompassing DSM for fuel selection, marking a pioneering effort within this domain, which integrates both AHP and Fuzzy Logic to furnish a versatile and resilient decision-making framework. The implications of this research are substantial, as it offers a transparent and systematic approach for fuel selection in industrial environments, providing valuable insights into the optimization of energy resources while taking into account economic, environmental, and safety considerations. Subsequent investigations could further examine the incorporation of renewable energy sources and the ramifications of advancing environmental policies on fuel selection.
The CNN Model with YOLO Architecture for Ultrasonography Images in Early Breast Cancer Detection Ayuningtyas, Astika; Wintolo, Hero; Sumari, Arwin Datumaya Wahyudi; Setyaningsih, Emy; Pujiastuti, Asih; Honggowibowo, Anton Setiawan; Nuryatno, Edi Triono; Kusumaningrum, Anggraini
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.v6i2.587

Abstract

The rapid development of object detection technology has opened new opportunities in the healthcare sector, especially in early cancer detection. This paper presents a deep learning-based breast cancer detection system using ultrasound images. The primary goal of this study is to create a model that can effectively differentiate between malignant and benign breast tumors, assisting in early diagnosis. The proposed system employs the Convolutional Neural Network (CNN) algorithm with You Only Look Once version 5 (YOLOv5) architecture, which is renowned for its high speed and accuracy in object detection tasks. A dataset comprising 10,954 ultrasound images was used to train the model, with 70% allocated for training, 20% for validation, and 10% for testing. The study reveals that the model achieves a high accuracy rate of 92.8% for malignant tumor detection and 99.1% for benign tumors, with precision rates of 99.6% for malignant tumors and 97.5% for benign tumors. These results demonstrate the feasibility of the proposed model as a reliable tool for early breast cancer detection. The findings highlight the potential of deep learning in medical image processing, suggesting that this technology could be further developed into an accessible, efficient early detection system for breast cancer in clinical settings. Future research could explore the integration of additional imaging modalities and the application of this model in real-world healthcare environments
Nature-based Hyperparameter Tuning of a Multilayer Perceptron Algorithm in Task Classification: A Case Study on Fear of Failure in Entrepreneurship Saputri, Theresia Ratih Dewi; Kurniawan, Edwin; Lestari, Caecilia Citra; Antonio, Tony
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.v6i2.539

Abstract

Entrepreneurship plays a key role in generating economic growth, encouraging innovation, and creating job opportunities. Understanding which demographic, psychological, and socio-economic factors contribute to fear of failure in entrepreneurship is essential to developing proper standards in entrepreneurship education and policy. However, it remains challenging to accurately classify these factors, especially when balancing model performance with model complexity in a multilayer perceptron algorithm. An effective model requires the correct parameter setting via a hyperparameter tuning process. Adjusting each hyperparameter by hand requires significant effort and knowledge, as there are frequently multiple combinations to consider. Furthermore, manual tuning is prone to human error and may overlook optimal configurations, resulting in inferior model performance and prediction accuracy. This study evaluates nature-inspired optimization techniques, including particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimization (GWO). Several parameters are tuned in the present multilayer perceptron model, including the number of hidden layers and the number of nodes in each hidden layer, learning rate, and activation functions. The used dataset which consists of 39 features from 333 samples captured individual fears, loss score, and computational efficiency as the required amount of time for finding the best parameter combination. Model accuracy performance scores are 45.16%, 53.76%, and 58.61% for GA, PSO, and GWO, respectively. Meanwhile their execution time are 10 minutes, 27 minutes, and 23 minutes, for GA, PSO, and GWO, respectively. Experiment results further reveal that each optimization algorithm has distinct advantages: GA excels at speedy convergence, PSO provides a robust exploration of hyperparameter space, and GWO offers remarkable adaptability to complicated parameter interdependencies. This study provides empirical evidence for the efficacy of nature-inspired hyperparameter modification in improving multilayer perceptron performance for fear of failure categorization tasks.
The Model of Carbon Price Risk Prediction in European Markets Using Long Short-Term Memory- Geometric Brownian Motion Pradana, Yan Aditya; Mukhlash, Imam; Irawan, Mohammad Isa; Putri, Endah Rokhmati Merdika; Iqbal, Mohammad
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.v6i2.536

Abstract

Accurate carbon market price prediction is one of the fundamentals in assessing the risks associated with carbon trading. Related studies on carbon price prediction were mainly focused on two major approaches: mathematical and/or machine learning models. Geometric Brownian Motion (GBM) is one of the mathematical models that can represent carbon price movements but requires modifying the sample size and the number of parameters for compiling the simulation numerically. Moreover, two critical parameters: (μ) mu and (σ) sigma need to be estimated to simulate the carbon price movements. In this study, the parameters μ and σ estimation are based on the average return value and standard deviation. However, if the carbon price movement is very volatile, we need to recognize its trend and characteristics by estimating the parameters precisely until there is no significant change (or stable) patterns. That is very expensive and may be intractable on high-dimensional data with less precise prediction. Therefore, we propose a hybrid model for carbon price prediction based on GBM with the parameter estimation using the Long Short-Term Memory (LSTM) model. The LSTM model was chosen because it has high accuracy in parameter estimation without losing the characteristics of the GBM stochastic model. Furthermore, Value at Risk (VaR) is utilized to measure the risk of carbon price volatility predictions. The simulation results showed the proposed model has higher prediction accuracy with a not-too-significant time difference, and the model is proven reliable in measuring future risks.
An Artificial Ant-Based Approach Using Polynomial Algorithms to Tackle the Text Aspect of Clustering Web Pages Moufok, Souad; Belkadi, Khaled; Lebbah, Fatima Zohra
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.v6i2.546

Abstract

Nowadays, the web clustering problem represents a scalable research area, which is based on deep study and efficient analysis of the user's browsing behavior. Managing huge amounts of unstructured data that are given through web pages is described as a hard and primary task. In this article, we analyze clusters by grouping users based on the similarity of the web pages they have visited. Our work focuses on cleaning, analyzing, and clustering web data to facilitate users’ access to relevant content. Thus, we propose a novel algorithm, called WCLARTANT, to cluster WEB pages, which consists of finding groups of sessions according to the corresponding Web access patterns. We propose a new approach based on the ANTTREE algorithm, inspired from the self-assembling behavior observed in real ants and the binary search tree concept. The combination that we present in our approach is applied for the first time in web usage mining clustering. More precisely, different topologies are built in terms of different similarity measures, such as SBS, Euclidean, Jaccard and Cosine. Afterward, the clusters are extracted from the binary tree, which is built by the prefix depth algorithm. In other words, the proposed algorithms in this manuscript provide the corresponding binary tree to the sessions' matrix, where each node models a WEB session and each branch represents a cluster. In addition, we use the Silhouette index to evaluate and to analyze the clustering performance of WCLARTANT relative to the DBScan algorithm. WClArtAnt combined with the similarity measure SBS provides the best results compared to DBScan. The performance of our algorithm varies between 0.62 and 0.39, which are considered good. The considered log files are coming from NASA and contain all HTTP requests for a month period, from 1st July, 1995, to 31st July, 1995, for a total of 65,194 entries.
Structural Equation Modeling of Social Media Influences: How Visual Appeal and Product Information Shape Positive Word of Mouth Iswanto, Dedy; Premananto, Gancar Candra; Sudarnice, Sudarnice; Sangadji, Suwandi 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.v6i2.584

Abstract

Social media has become an essential tool in contemporary marketing strategies, allowing brands to enhance consumer engagement and foster trust. This study examines the direct effects of visual appeal and product information on brand satisfaction and positive Word of Mouth (WOM) in the context of Samsung’s social media campaigns. The research aims to provide empirical insights into how specific content elements drive consumer satisfaction and WOM, which are critical factors in expanding a brand’s digital influence. Data were collected from 132 active social media users frequently exposed to Samsung’s advertisements. Structural Equation Modeling (SEM) using the Partial Least Squares (PLS) approach was employed to analyze the relationships between variables. The results indicate that visual appeal significantly impacts brand satisfaction (path coefficient = 0.419, T-statistic = 3.765, P-value = 0.000) and WOM (path coefficient = 0.221, T-statistic = 2.437, P-value = 0.015). Product information also shows a significant influence on brand satisfaction (path coefficient = 0.337, T-statistic = 3.126, P-value = 0.002) and WOM (path coefficient = 0.320, T-statistic = 3.795, P-value = 0.000). Additionally, brand satisfaction strongly contributes to positive WOM (path coefficient = 0.458, T-statistic = 7.191, P-value = 0.000). The findings emphasize the critical role of high-quality visual and informational content in fostering brand satisfaction and promoting WOM. The novelty of this research lies in its detailed examination of how visual appeal and product information independently influence consumer outcomes, offering actionable insights for marketers. This study contributes to the growing literature on digital marketing by providing evidence-based recommendations for optimizing social media strategies in highly competitive and digitally connected marketplaces.
Transforming Mathematics Learning: Students' Integrative Skills in Technology and Pedagogy Turmuzi, Muhammad; Hikmah, Nurul; Junaidi, Junaidi
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.v6i2.482

Abstract

In the digital age, mastering the topic or developing pedagogical design skills alone is no longer sufficient for instructors or aspiring math educators. They also need to be able to make connections between the two. In addition, other specialized abilities are required, such as the ability to use technology for learning (technological skills). Technological Pedagogical and Content Knowledge (TPACK) is a common term for this skill. TPACK is a framework that helps educators integrate technology into learning effectively. TPACK covers three main types of knowledge-Technology, Pedagogy, and Content, and how the combination of these three elements creates a more meaningful learning experience for students. This study aims to assess the TPACK teaching abilities of aspiring mathematics teachers in the Microteaching course. This research uses a qualitative descriptive method, which describes the object of research in its original form without quantitative measurement or manipulation of variables but focuses on describing the observed phenomena. In this instance, the study provides a general picture of how well math education students comprehend and utilize their TPACK. A Likert scale measures an individual's or group's attitudes, views, and perceptions concerning social phenomena to ascertain their understanding of TPACK. Twenty University of Mataram students enrolled in the Microteaching course in Mathematics Education served as the research subjects. Based on the findings of the research and discussion, it can be said that students applying TPACK to learning have an average score of 3.88 medium categories for technological pedagogical knowledge, 3.82 medium categories for pedagogical knowledge, and 3.63 medium categories for content knowledge. Since most students are already familiar with the TPACK instrument, their total TPACK ability has an average score of 3.89, which is considered to be medium.
Evaluating Usability and Clustering of SILCARE System for MSME Shipping: A Data-Driven Approach Using SUS and User Behavior Analysis Permatasari, Ririt Dwiputri; Bora, M Ansyar; Hernando, Luki; Saputra, Tommy; Fauzan, Haidil; Shilah, Nur; Salsabila, Tia Andini
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.v6i2.590

Abstract

The SILCARE system is a digital logistics platform designed to optimize shipping operations for Micro, Small, and Medium Enterprises (MSMEs). This study evaluates its usability and user behavior patterns through System Usability Scale (SUS) assessments and clustering analysis. The research involved 100 SME users performing key system tasks such as registration, product management, and order confirmation. The SUS results showed a significant usability improvement, with the pre-test score of 74.5 (B grade) increasing to 90.25 (A grade) in the post-test, indicating enhanced user experience. User interaction data analysis revealed that registration took an average of 7.11 minutes, product addition 8.91 minutes, and order confirmation 5.15 minutes. Clustering using DBSCAN identified four distinct user groups, highlighting behavioral differences, where 37% of users struggled with complex tasks while 25% displayed balanced engagement. These findings inform targeted system improvements, such as simplifying workflows for new users and enhancing features for power users. The novelty of this study lies in integrating usability testing with behavior-driven clustering to refine a logistics platform tailored to MSMEs. By leveraging data-driven insights, the SILCARE system contributes to digital transformation in MSME logistics, improving operational efficiency and user satisfaction The paper explores the development process of the system, starting from the requirements gathering phase, where user needs were identified through extensive surveys and interviews with stakeholders. The iterative prototyping method allowed for the creation of an initial version of the system that was refined based on user feedback, ensuring that the final product met both functional and usability standards. The SILCARE system holds substantial promise for MSMEs, offering a digital solution for streamlining logistics and shipping processes and contributing to the overall success of small businesses.
Improved Deep Learning Model for Prediction of Dermatitis in Infants Setiawan, Debi; Noratama Putri, Ramalia; Fitri, Imelda; Nizar Hidayanto, Achmad; Irawan, Yuda; Hohashi, Naohiro
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.v6i2.542

Abstract

Indonesia's equatorial climate, characterized by summer and rainy seasons, presents environmental conditions that contribute to a high incidence of dermatitis in infants. Dermatitis, an inflammatory skin condition, can lead to significant discomfort in infants, affecting their sleep, growth, and development. Early diagnosis is crucial for effective treatment; however, conventional diagnostic methods in clinics and hospitals—such as physical observation and parental interviews—are often time-consuming, subjective, and may lack precision, creating a need for more efficient diagnostic tools. This study explores the application of deep learning models to enhance the accuracy and speed of dermatitis diagnosis in infants. Four convolutional neural network (CNN) models were evaluated: MobileNet, VGG16, ResNet, and a Custom CNN model specifically designed for this study. Using a dataset of 1,088 skin images collected from three regions in Riau Province, Indonesia, we conducted training and testing to assess each model’s performance in distinguishing between dermatitis-affected and healthy skin. Results show that MobileNet and the Custom CNN outperformed other models, achieving accuracy rates of 97% and 85%, respectively. MobileNet’s high accuracy and efficiency make it a viable option for mobile applications, enabling rapid, on-site diagnosis in resource-limited settings. The Custom CNN model, tailored to the unique features of infant skin, also showed promising results. These findings demonstrate the potential of automated, image-based diagnostic tools for assisting medical professionals in early dermatitis detection, improving patient outcomes. This study contributes a valuable diagnostic solution that leverages deep learning to support healthcare providers, particularly in areas with limited access to specialized medical resources.

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