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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 55 Documents
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Search results for , issue "Vol 6, No 4: December 2025" : 55 Documents clear
ACLM Model: A CNN-LSTM and Machine Learning Approach for Analyzing Tourist Satisfaction to Improve Priority Tourism Services Arsyah, Ulya Ilhami; Pratiwi, Mutiana; Fryonanda, Harfeby; Anam, M. Khairul; Munawir, Munawir
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.974

Abstract

Tourist satisfaction is a key proxy for destination service quality, yet automatic sentiment analysis of online reviews still faces class imbalance, overfitting, and limited deployability. This study proposes ACLM, a hybrid sentiment classification pipeline that learns semantic and temporal features with a CNN-LSTM backbone and evaluates three classifier heads (Softmax, Logistic Regression, XGBoost) on a three-class corpus (neutral, satisfied, dissatisfied). The objective is to deliver an accurate and operational model for decision support in tourism services. The idea combines Word2Vec embeddings, a compact CNN for local patterns, an LSTM for sequence dependencies, and a training workflow with text cleaning, SMOTE based balancing, and regularization to curb overfitting; outputs are exposed through a simple Streamlit interface. Results show that CNN-LSTM with a Softmax head attains accuracy 0.89, macro precision 0.89, macro recall 0.84, and macro F1 0.86, outperforming Logistic Regression (accuracy 0.87, macro precision 0.84, macro recall 0.82, macro F1 0.82) and XGBoost (accuracy 0.85, macro precision 0.80, macro recall 0.82, macro F1 0.80). The findings indicate that deep sequence features paired with a simple Softmax head provide the best tradeoff between accuracy and stability for three-way sentiment classification. The contribution is a reusable, end to end blueprint from preprocessing and balanced training to quantitative evaluation and an inference GUI, and the novelty lies in testing interchangeable classifier heads on a single CNN-LSTM feature extractor while explicitly addressing data imbalance and deployment constraints. The GUI is implemented using the highest accuracy model, namely CNN-LSTM with Softmax.
Image-Based Fish Freshness Classification Using Two-Phase Transfer Learning with Deep Learning Fusion Model Helmud, Ellya; Edi Widodo, Catur; Dwi Nurhayati, Oky
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.988

Abstract

This study introduces a novel deep learning approach for automated fish freshness classification using image analysis. The objective is to design and validate a Deep Learning Fusion Model that combines the strengths of EfficientNetB0 and InceptionV3 architectures to improve accuracy and robustness in classifying fresh and non-fresh fish. Input images were subjected to extensive augmentation, including RandomFlip, RandomRotation, RandomZoom, RandomContrast, RandomBrightness, and RandomTranslation, applied exclusively to the training dataset to enhance generalization, followed by backbone-specific pre-processing. Extracted features were fused via global average pooling and forwarded to a newly designed classification head with dropout and L2 regularization to mitigate overfitting. A two-phase transfer learning strategy was employed: initially training the classification head with frozen backbones, followed by fine-tuning the backbone layers using the Adam optimizer with a reduced learning rate. To highlight the contribution of the fusion strategy, ablation studies were conducted with single-backbone models. The EfficientNetB0 model achieved 89.17% validation accuracy, 85.83% test accuracy, and an F1-score of 85.69%, while the InceptionV3 model achieved 86.67% validation accuracy, 81.67% test accuracy, and an F1-score of 81.59%. In contrast, the proposed Fusion Model achieved 93.33% validation accuracy, 95.00% test accuracy, and an F1-score of 94.95%. Additional evaluations with confusion matrices, ROC curves, AUC, and precision-recall curves confirmed the model’s superiority. The findings demonstrate that integrating features from diverse CNN architectures enables the model to learn richer representations, resulting in significantly improved classification performance. The novelty of this work lies in the effective fusion of complementary backbones through global average pooling and fine-tuned transfer learning, establishing a human-centric computational approach that offers a reliable solution for practical fish freshness assessment in food safety and market scenarios.
Acceptance and Success Model for AI Use in Higher Education: Development, Instrument Decomposition, and Its Triangulation Testing Subiyakto, Aang; Huda, Muhammad Q; Hakiem, Nashrul; Suseno, Hendra B; Arifin, Viva; Azmi, Agus N; Sani, Asrul; Yuniarto, Dwi; Hartawan, Muhammad S; Suryatno, Agung; Muji, Muji; Kurniawan, Fachrul; Kusumawati, Ririen; Balogun, Naeem A; Ahlan, Abd. Rahman
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.619

Abstract

Prior social computing studies described that the performance of technology products is about how the product use benefits the users, including Artificial Intelligence (AI). To have an impact, ensuring how AI is used is a prerequisite after the development. Furthermore, its use is also influenced by how users accept AI. This study aimed to develop an acceptance and success model of AI use in the higher education world from the user perspective, to decompose the model into its instrument level, and to test the validity and reliability of the research instrument. The researchers developed the model by adopting and combining the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM) and adapting the proposed model in the context of AI use in higher education learning. The measurement items were derived from definitions of the variables and indicators of the model. The instrument was tested sequentially using triangulation methods. The quantitative testing was online survey with about 51 respondents and the qualitative one was interview involving five experts. This study may contribute methodologically as one of the guidance for novice scholars in similar works. It may relate to the clarity of the research procedure and the implementation of the mixed testing methods. Of course, the assumptions, samples, and data used in the study cannot be generalized for the other studies. Referring to the model development, the proposed model may not cover the other factors related to the ethical, cultural, and organizational barriers for adopting AI. These barriers may also affect its acceptance and success. Thus, the adoption of the factors related the barriers may also be interesting to study further.
Classification of Batak Toba Ulos Motifs Based on Transfer Learning with MobileNetV2 Limbong, Tonni; Simanullang, Gonti; Silitonga, Parasian DP.; Silalahi, Donalson
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.1036

Abstract

Indonesia possesses a rich cultural heritage, including the traditional Batak Toba Ulos textile, which is known for its diverse motifs and deep philosophical meanings. However, the preservation and visual recognition of Ulos remain challenging, particularly in terms of systematic documentation and automated classification. This study presents a visual recognition system for Batak Toba Ulos motifs using a transfer learning approach based on the MobileNetV2 architecture. The methodology involves the construction of a curated dataset of Ulos images, the application of data augmentation and preprocessing techniques, and model training utilizing ImageNet pre-trained weights. The system’s performance was evaluated using accuracy, precision, recall, and F1-score metrics. Results show that the model is capable of accurately classifying all 12 Ulos classes, achieving F1-scores ranging from 0.93 to 0.97. These findings demonstrate that transfer learning is effective in overcoming the limitations of culturally specific, small-scale datasets. This research contributes to the development of artificial intelligence tools for cultural preservation and supports the digital documentation and promotion of Batak Toba Ulos to younger generations and broader audiences in an efficient and scalable manner.
Applied Data Science for Exploring Multi-Channel Retail Service Quality Affecting Customer Satisfaction and Loyalty at Commercial Banks Le, Man Thi; Thanh, Tam Phan
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.1134

Abstract

This study examines how service quality across physical and digital channels influences customer satisfaction and loyalty within the omnichannel environment of commercial banks in Vietnam. Although digital transformation has accelerated rapidly, there remains limited empirical evidence on how integrating traditional service encounters with online and mobile platforms shapes customer perceptions and behavioral intentions. Addressing this gap, the paper develops and tests a comprehensive model that integrates traditional service quality dimensions, digital platform quality, and multi-channel integration, while also considering the moderating role of customers’ digital competence. The study contributes to the literature by extending conventional service quality frameworks to encompass the realities of omnichannel banking in an emerging market. It highlights the relative importance of physical facilities, staff professionalism, digital platform usability, and cross-channel consistency in shaping customer experiences. A two-phase methodology was employed. The qualitative phase involved expert evaluations and customer focus groups to refine measurement items and ensure contextual relevance. The quantitative phase gathered data from 785 retail banking customers and analyzed the relationships among the constructs using variance-based structural modeling. Findings indicate that all dimensions of service quality positively influence satisfaction, with physical facilities and multi-channel integration emerging as the strongest drivers. Satisfaction significantly enhances loyalty and mediates the effects of service quality dimensions. Digital competence both directly strengthens loyalty and moderates the satisfaction–loyalty relationship, suggesting that customers with higher digital skills derive more value from omnichannel services and are more likely to remain loyal. The study underscores the need for banks to invest in both modern physical infrastructures and high-performing digital platforms, while ensuring seamless integration across channels. It also emphasizes the importance of designing differentiated strategies tailored to customers’ digital capabilities to enhance overall satisfaction and foster long-term loyalty.

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