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Husni Teja Sukmana
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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
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.
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.
Transformer Architectures for Automated Brain Stroke Screening from MRI Images Abstract Sukmana, Husni Teja; Hasibuan, Zainal Arifin; Rahman, Abdul Wahab Abdul; Bayuaji, Luhur; Masruroh, Siti Ummi
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.v6i2.736

Abstract

Early and accurate detection of stroke is critical for timely medical intervention and improved patient outcomes. This study explores the application of deep learning models, particularly the Vision Transformer (ViT), for the automated classification of brain stroke from medical images. A curated dataset of brain scans was used to train and evaluate the ViT model, which was benchmarked against a widely used convolutional neural network (CNN), ResNet18. Both models were trained using transfer learning techniques under identical preprocessing and training configurations to ensure fair comparison. The results indicate that the ViT model significantly outperforms ResNet18 in terms of validation accuracy, class-wise precision, and recall, achieving a peak accuracy of 99.60%. Visual analyses, including confusion matrices and sample prediction comparisons, reveal that ViT is more robust in detecting subtle stroke patterns. However, ViT requires more computational resources, which may limit its deployment in real-time or low-resource settings. These findings suggest that transformer-based architectures are highly effective for medical image classification tasks, particularly in stroke diagnosis, and offer a viable alternative to traditional CNN-based approaches.
Unveiling Hybrid Model with Naive Bayes, Deep Learning, Logistic Regression for Predicting Customer Churn and Boost Retention Subramanian, Devibala; Ajitha, Ajitha; Maidin, Siti Sarah
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.v6i2.675

Abstract

The telecommunications sector is rapidly evolving but is increasingly challenged by customer churn, where subscribers switch to competing service providers. This study introduces a hybrid model for churn prediction and customer retention by combining machine learning methods—Naive Bayes, Deep Learning, and Logistic Regression—with sentiment analysis on user-generated content (UGC). Data was gathered through two primary sources: survey responses and 352 social media comments from users aged 20–35. The survey data was enriched with features such as gender, age, subscription period, complaints, and retention efforts. The preprocessing steps included handling missing values, scaling features, and encoding categorical variables to ensure model robustness. Experimental results demonstrated that Logistic Regression achieved the highest accuracy (88.45%) and sensitivity (91.33%) in detecting potential churners. The PCA-based approach followed closely with an accuracy of 86.77% and a balanced sensitivity-specificity profile (89.95% and 83.58%, respectively), effectively capturing key churn indicators. Random Forest and Decision Tree classifiers yielded lower sensitivity but remained strong in specificity, indicating their suitability for identifying loyal customers. Attribute weight analysis across models revealed that subscription plan, age, and retention effort were consistently influential in churn prediction. Furthermore, the integration of sentiment analysis provided emotional context to churn behavior, with negative comments triggering alerts for proactive engagement. The study highlights the predictive strength of combining structured survey data and unstructured UGC through machine learning and sentiment analytics. It underscores the importance of personalized retention strategies based on model interpretability and correlation weight findings. This hybrid approach equips telecom companies with actionable insights to minimize churn and sustain customer loyalty in a competitive market.
Human Capital and Sustainable Teacher Performance: Examining the Impact of Servant Leadership, Competence, and Professional Commitment in Catholic Education Budiyanto, Hendro; Djati, Sundring Pantja; Alirejo, Mohamad Subroto; Rini, Wahju Astjarjo
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.v6i2.638

Abstract

This study examines the impact of servant leadership on the performance of Catholic religious teachers, with competence and professional commitment as mediating variables. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 151 Catholic religious teachers in the Jakarta Archdiocese. The results show that servant leadership has a direct positive impact on teacher performance (β = 0.317, p 0.001) and indirectly enhances performance through competence (β = 0.199, p = 0.008) and professional commitment (β = 0.186, p = 0.002). Competence (β = 0.357, p = 0.001) and professional commitment (β = 0.340, p = 0.002) significantly improve teacher performance. The structural model explains 74.9% of the variance in teacher performance, indicating strong predictive power. This study contributes to the literature by demonstrating the mediating role of competence and professional commitment in the relationship between servant leadership and performance, particularly in Catholic education. The findings provide practical implications for school administrators and policymakers to implement servant leadership strategies that enhance teacher competence and commitment. This research introduces a comprehensive approach to improving teacher effectiveness in religious education settings, emphasizing the importance of leadership styles that prioritize service, empowerment, and professional development.
Factors Affecting the Intention to Buy Electric Vehicles Through the Integration of Technology Acceptance Model and Prior Experience Saleh, Hendra Noor; Maupa, Haris; Cokki, Cokki; 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.v6i2.730

Abstract

To enhance the adoption of electric vehicles (EVs), governments have implemented regulatory policies, such as providing incentives. However, this approach is temporary and relies on the active involvement of manufacturers to better understand the driving factors behind EV adoption. While previous studies, largely based on behavioral theory, emphasize psychological and environmental factors, individual subjective factors also play a crucial role. This study introduces a novel approach by integrating variables from the Technology Acceptance Model (TAM)—perceived usefulness and perceived ease of use—with consumer experience variables, namely technology discomfort and customer experience. The goal is to improve TAM's explanatory power regarding the intention to buy EVs from the consumer perspective. The research targeted residents of Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi) aged 17 and older, all of whom had prior experience with Battery Electric Vehicles (BEVs). Data was collected from 330 respondents through an online survey. Structural Equation Modeling (SEM) with AMOS was used for the analysis. The results indicated that perceived usefulness, perceived ease of use, and customer experience significantly influenced intention to buy, while perceived usefulness did not significantly affect customer experience. Customer experience mediated the relationship between perceived ease of use and intention to buy, but did not mediate the effect of perceived usefulness. Additionally, technology discomfort negatively impacted perceived usefulness and ease of use, although it did not significantly affect customer experience. These findings suggest that while government incentives remain important, a market-driven approach that focuses on improving consumer perceptions and experiences is critical for accelerating EV adoption.
Price Prediction of Aglaonema Ornamental Plants Using the Long Short-Term Memory (LSTM) Algorithm Sugiarti, Yuni; Suroso, Arif Imam; Hermadi, Irman; Sunarti, Euis; Yamin, Fadhilah Bt Mat
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.v6i2.640

Abstract

The Aglaonema ornamental plant is a horticultural commodity with high economic value and promising prospects. It is well known for its attractive leaf variations, earning it the nickname "Queen of Leaves." However, unpredictable price fluctuations make investing in Aglaonema speculative and high-risk. This research aims to predict the price of Aglaonema over the next five years using the Long Short-Term Memory (LSTM) algorithm. LSTM is considered superior to other algorithms in handling time series data. The model's performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) on a weekly Aglaonema price dataset covering the period from January 2012 to December 2023. The results demonstrate that the LSTM algorithm can predict Aglaonema prices with high accuracy, as indicated by the following metrics: MSE: 0.005 – Represents the average squared difference between predicted and actual prices. A lower MSE indicates higher model accuracy. RMSE: 0.07-RMSE provides a more interpretable error measurement as it retains the same units as the original data. A low RMSE signifies that the model's predictions closely align with actual values. MAE: 0.04 – Measures the absolute average difference between predicted and actual prices. A lower MAE value reflects a smaller prediction error. Thus, this research makes a significant contribution to the development of a machine learning-based price prediction system for the ornamental plant industry.
Dynamic Replica Management Strategy Based-on Data Accessing Popularity for Load Balancing and Optimizing Network Performance in Cloud Storage Thavamani, S.; Maidin, Siti Sarah; Varun, S. T.
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.v6i2.674

Abstract

Network performance plays a vital role in organizational efficiency, where large volumes of data, fast transmission, and low latency significantly enhance productivity and reduce downtime. Cloud storage offers a service model that enables remote data management and efficient content distribution. In such systems, data replication is widely used to improve availability, reliability, fault tolerance, and throughput. However, static replication policies often allocate replicas during system initialization, failing to adapt to the dynamic and heterogeneous nature of cloud environments. These environments are susceptible to challenges such as data loss, node failures, and fluctuating demand, which can degrade service quality. To address this, we propose a dynamic replica management strategy that considers data popularity, active peer participation, and peer capacity. Virtual peers are grouped into strong, medium, and weak clusters based on their weight values, which are derived from bandwidth, CPU speed, memory size, and access delay. Content is categorized into Class I, II, and III based on access frequency. Highly popular data (Class I) is replicated in strong clusters, while less frequently accessed data is placed in medium and weak clusters. A hierarchical routing mechanism ensures that queries are directed to the appropriate cluster. The proposed system was implemented and evaluated through simulations. Results show up to 25% improvement in throughput, 20% reduction in packet drops, 97% query efficiency, and decreased bandwidth utilization under high load. By maintaining optimal replica counts without compromising availability, the system supports cloud SLA compliance while minimizing overhead. This solution is aligned with the ninth UN Sustainable Development Goal: Industry, Innovation, and Infrastructure.
Data Science Approaches to Analyzing Aesthetic Strategies in Contemporary Presidential Campaigns Isnawijaya, Isnawijaya; Lexianingrum, Siti Rahayu Pratami; Taqwa, Dwi Muhammad; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
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.v6i2.609

Abstract

In today’s digital political landscape, social media platforms play a critical role in shaping voter engagement, especially among youth. This study investigates how aesthetic political strategies were applied in Prabowo Subianto’s 2024 presidential campaign on TikTok and Instagram. It focuses on decoding voter sentiment, optimizing content delivery, and identifying visual elements that resonate with the public. Using machine learning models tailored to various data types, the research analyses over 50,000 comments and 30 million engagements. A BERT-based sentiment analysis model achieved 88% accuracy, revealing 60% positive, 25% neutral, and 15% negative sentiment, reflecting broad public approval. Meanwhile, a Gradient Boosting engagement prediction model reached 85% accuracy in forecasting post performance based on content format, timing, and hashtag use. Posts with videos and trending hashtags had a 78% chance of high engagement, while static images without hashtags scored only 45%. Evening posts performed best, with a 25% higher likelihood of engagement. The findings highlight the value of AI-driven insights in political communication, emphasizing that emotionally and visually rich content—particularly patriotic and relatable themes—enhances audience connection. This study offers a practical framework for political actors to develop adaptive, data-informed strategies that align with voter preferences in an increasingly fragmented and fast-paced digital media environment.

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