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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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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.
Celebrity Characteristics and Purchase Intentions: A Structural Equation Modeling Analysis of YouTube Culinary Content Mutiarasari, N Azizia Gia; Hartini, Sri; Sangadji, Suwandi S.; Lina, Lia Febria
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.v6i2.634

Abstract

The increasing popularity of YouTube Vloggers has attracted attention in marketing strategies, specifically in the Food Industry. This phenomenon highlights a behavioral shift where relationships between YouTube Vloggers and their followers generate trust, influencing purchase intention. Previous research has explored the formation of parasocial interactions between YouTube vloggers and their followers; this study examined the characteristics of YouTube vloggers that influence credibility and parasocial interactions and the role of these two variables in driving purchase intention, which is still limited. This study collected data through a survey targeting active social media users on Instagram and TikTok who have been exposed to content from YouTube vloggers with food content. Data were analyzed using Structural Equation Modeling (SEM) to examine the relationships between variables. The results suggest that homophily and social beauty broadly influence credibility and parasocial interactions. In contrast, physical attractiveness only influences credibility, while self-disclosure does not significantly affect parasocial interactions. Credibility and parasocial interactions were found to play an important role in driving consumer purchase intention. This finding strengthens the relevance of the Uses and Gratifications (UG) theory and inducement theory in understanding consumer actions in digitalization.
Severity Prediction of Jordan Road Accidents using Artificial Intelligence Mustafa, Dheya; Al-Hammouri, Mohammad; Khabour, Safaa
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.v6i2.624

Abstract

Road traffic accidents are a significant global concern, with developing countries accounting for 85% of annual fatalities and 90% of disability-adjusted life years lost. This study investigates the severity of road accidents in Jordan using a machine learning-based predictive approach. A dataset of 73,000+ accident reports from 2018 was analyzed, covering factors such as road conditions, weather, vehicle attributes, and driver demographics. The primary objective is to develop and evaluate machine learning models for predicting accident severity. Seven classification algorithms were tested: Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost). The results indicate that LR achieved the highest accuracy at 98.1%, followed by RF (95.02%) and XGBoost (95.27%). Feature importance analysis revealed that road type, lighting conditions, and driver violations were the most influential factors in predicting accident severity. A key novelty of this research is the integration of real-world Jordanian accident data with machine learning models to enhance predictive accuracy. The study's findings provide actionable insights for policymakers, enabling targeted interventions to reduce accident severity. The dataset is made publicly available to support future research. This research contributes to the advancement of AI-driven traffic safety solutions, demonstrating the effectiveness of machine learning in real-time risk assessment and decision-making.
The Influence of Logistics Technology Innovation on the Efficiency of Operations in Small and Medium-Sized Businesses in Thailand Inmor, Sureerut; Rangsom, Kritiya; Šírová, Eva; Wongpun, Sukontip
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.684

Abstract

Logistics technology innovation include technology for moving materials and products, such as robotics and automated logistics systems; technology used to transmit information that enables real-time data exchange to optimize material movement; and technology to assist in decision-making as artificial intelligence enhances decision-making. These technologies include the use of digital transformation, automation, and enhanced decision-making tools to increase the efficiency of supply chain operations. This study aimed to examine how environmental factors (legal regulations, market competition, and stakeholder involvement) influence the operational efficiency of small and medium-sized enterprises in Thailand, with logistics technology innovation serving as a mediating factor, and to propose strategic guidelines for improving business performance through innovation. Data were collected from 400 small and medium-sized businesses in the Eastern Special Development Zone which are Chachoengsao, Chonburi, and Rayong provinces. A purposive sampling method was used to select enterprises in logistics-related industries, followed by convenience sampling for survey distribution. The investigation was carried out utilizing structural equation modeling. The findings revealed that environmental variables have a considerable impact on operational efficiency, with logistics technology innovation serving as a mediating variable. The direct effect of environmental factors on innovation technology was strong (β = 0.73), while innovation technology had a significant positive effect on operational efficiency (β = 0.37). Product movement technologies, including robots and automated vehicles, had the greatest influence (β = 0.62), followed by digital data transmission technologies (β = 0.34) and decision support systems (β = 0.06). These results imply that small and medium-sized businesses should emphasize logistics automation, artificial intelligence-driven decision-making, and digital data sharing platforms to increase efficiency. This study offers important insights for corporate executives and politicians in creating a favorable climate.
A New Data Preprocessing Framework to Enhance the Accuracy of Herbal Plants Classification Using Deep Learning Kunlerd, Attapol; Ritthiron, Atipat; Nabumroong, Boonlueo; Luangmaneerote, Sakchan; Chaiwachirakhampon, Anyawee; Kaewyotha, Jakkrit
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.733

Abstract

This research proposes to solve the problem of herbal plant classification, which plays a key role in Thai pharmacy and traditional medicine. Moreover, there are limitations due to similar physical characteristics of plants and the reliance on specialists to classify herbal plants, which hinder the utilization of herbal plants by the general public at the local level. To solve this problem, this research presents a new preprocessing framework called P4, which integrates 7 techniques as follow: Image Cropping, Resizing, Normalization (0–1), Data Augmentation, Label Noise, Label Cleaning, and Dataset Quality Score (DQS). The prominent point of P4 technique is the combination of intentional mislabeling and label cleaning process, as well as, quantitative data quality assessment and additional expert review in order to filter out potentially inaccurate data before inputting to Deep Learning model. In the experiment, a dataset of 4,211 herbal images covering 30 herbal plant species is used and compared with 3 proposed techniques in previous research (P1–P3) with 5 deep learning architectures, namely DenseNet201, EfficientNetB7, ViT, Swin Transformer, and ConvNeXt. The experimental results showed that the P4 technique combined with DenseNet201 model provided the highest performance in herbal plant classification, with an Accuracy of 92%, Precision of 92%, Recall of 91%, and a training time of merely 22.92 minutes. This was a result of combining the good data quality from the P4 technique, which enhanced to increase efficiency in producing higher quality and more balanced data. When combined with the structural capability of DenseNet201 that supported feature reuse from previous layers, it increased the robustness to mislabeled data and was able to accurately distinguish plants with similar characteristics. The results of this experiment are able be applied as a guideline for future application in Thai traditional medicine support system and herbal plant learning system.
Factors Influencing the Intention to Use Insurance Technology (Insurtech) Among Generation Z Using the Extended D-M Model Umran, M. Fankar; Maupa, Haris; Irawan, Agustinus Purna; Sadat, Andi Muhammad
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.v6i2.726

Abstract

This study investigates the factors influencing Generation Z’s intention to use Insurtech in Indonesia using an extended DeLone and McLean model. The research introduces two additional variables: perceived trust and regulatory expectancy. Data were collected via an online survey of 431 Generation Z respondents aged 17 and above, residing in ten major Indonesian cities: Jakarta, Bandung, Semarang, Yogyakarta, Surabaya, Denpasar, Palembang, Medan, Balikpapan, and Makassar, all with a basic understanding of Insurtech. The questionnaire included demographic questions and research variables measured on a five-point Likert scale. Data were analyzed using Structural Equation Modeling (SEM) through Smart PLS 4. Descriptive analysis revealed that most respondents were aged 25-28 years, predominantly female, residing in Jakarta, employed in private sectors, with monthly expenditures below USD 300, and holding a bachelor’s degree. The analysis indicated that respondents viewed Insurtech positively, noting its organized information, flexible services, knowledgeable providers, honest services, and legal protection of personal data. Additionally, respondents expressed a strong interest in using Insurtech soon. The measurement model evaluation confirmed the validity and reliability of all indicators based on convergent validity, discriminant validity, and reliability tests. The structural model analysis showed that the independent variables explained 57% of the variance in intention to use Insurtech and 69% in perceived trust. Hypothesis testing revealed that information quality, system quality, service quality, and regulatory expectancy positively influenced both intentions to use Insurtech and perceived trust. However, contrary to expectations, perceived trust did not significantly affect the intention to use Insurtech. This finding suggests that for Generation Z, trust may be considered a baseline expectation, with factors like system and service quality playing a more direct role in their adoption decisions. Additionally, no significant mediation effects were found. The model demonstrated strong predictive relevance and good fit, confirmed by Q², NFI, and SRMR values.
Modelling and Investigation of Solar Photovoltaic-Based Converter Configurations with Data Science Approach S., Prakash; S., Lakshmi; S., Priya; Batumalay, Malathy
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.715

Abstract

Renewable energy sources, such as solar photovoltaic (PV) systems, typically produce low-voltage outputs, necessitating the use of high-gain direct current (DC) converters for efficient energy conversion. This study proposes a high-gain DC-DC converter for PV applications, designed with two MOSFET switches, two inductors, and two capacitors, offering a compact and efficient configuration. The converter achieves a high voltage gain of 6.8 and maintains a conversion efficiency of 97.7%, making it suitable for high-power applications. A data science-driven approach was employed to analyze the converter’s performance, integrating conventional simulation with machine learning techniques. Simulation results, conducted using MATLAB, confirmed the converter's superior performance, achieving an input ripple of 0.05% and an output ripple of 0.01%. Machine learning models, including Linear Regression, Decision Tree, Ridge Regression, and Support Vector Machine (SVM), provided deeper insights into the converter's behavior. Linear Regression accurately predicted output voltage, Ridge Regression minimized overfitting, and the Decision Tree model identified Duty Ratio and Input Voltage as the most critical factors affecting efficiency. SVM effectively classified operating conditions into high, moderate, and low efficiency. The Zero-Voltage Switching (ZVS) technique minimized switching losses, enhancing overall efficiency. This study demonstrates that integrating data science techniques with conventional analysis enhances the understanding and optimization of high-gain converters. The proposed converter provides a scalable and efficient solution for PV applications, offering insights for further optimization as part of process innovation.

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