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INDONESIA
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 40 Documents
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Search results for , issue "Vol 7, No 1: January 2026" : 40 Documents clear
The Application of Deep Learning in Qur’anic Tafsir Retrieval Using SBERT, FAISS and BERT-QA Herliana, Asti; Najiyah, Ina; Susanti, Sari; Billah, Lutfhi Muayyad
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1000

Abstract

Accurate understanding of the Qur’an requires access to reliable tafsir, yet many classical tafsir resources remain non-digital, making search and retrieval time-consuming. This study presents a semantic-based retrieval system for Tafsir Ibn Kathir, covering 114 entries and 6,236 Verses, using SBERT embeddings and FAISS indexing. The system enables users to perform semantic queries, retrieving relevant passages in response to their questions. Evaluation was conducted using 50 representative queries spanning diverse topics, including Fiqh, Aqidah, History, and Spirituality. Relevance judgments were independently provided by three Qur’anic studies experts and reconciled through discussion, with inter-annotator agreement indicating substantial consistency. Each query included 20 non-relevant passages as negative samples to increase evaluation difficulty. Two approaches were tested: retrieval-only and retrieval combined with a zero-shot QA module for span extraction. Retrieval-only achieved slightly higher top-1 accuracy (0.72), but retrieval + QA improved ranking-oriented metrics, including Accuracy@5 (0.88), Mean Reciprocal Rank (MRR = 0.76), and normalized Discounted Cumulative Gain at 5 (nDCG@5 = 0.82), with the increase in Accuracy@5 statistically significant (p = 0.01). The zero-shot QA module enabled the system to extract more precise and contextually relevant information, enhancing overall retrieval quality and robustness. These results indicate that the proposed system effectively retrieves relevant tafsir passages and provides accurate, context-specific answers. The study demonstrates the potential and limitations of zero-shot QA for domain-specific religious texts and supports the development of web-based applications or Islamic chatbots, facilitating easier access to shahih tafsir knowledge for scholars and the broader Muslim community.
Multiclass Skin Lesion Classification Algorithm using Attention-Based Vision Transformer with Metadata Fusion Furqan, Mhd.; Katuk, Norliza; Hartama, Dedy
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1017

Abstract

Early and accurate classification of skin lesions is essential for timely diagnosis and treatment of skin cancer. This study presents a novel multiclass classification framework that integrates dermoscopic images with clinical metadata using an attention-based Vision Transformer (ViT) architecture. The proposed model incorporates a mutual-attention fusion mechanism to jointly learn from visual and tabular inputs, augmented by a class-aware metadata encoder and imbalance-sensitive loss function. Training was conducted using the HAM10000 dataset over 30 epochs with a batch size of 32, utilizing the Adam optimizer and a learning rate of 0.0001. The model demonstrated superior performance compared to a ViT Baseline, achieving 93.4% accuracy, 92.2% F1-score, 0.95 AUC, and significant reductions in MAE and RMSE. Additionally, Grad-CAM visualizations confirmed the model’s ability to focus on diagnostically relevant regions, enhancing interpretability. These findings suggest that the integration of structured clinical information with transformer-based visual analysis can significantly improve classification robustness, particularly in underrepresented lesion types. However, the model’s current performance is evaluated only on the HAM10000 dataset, and its generalizability to other clinical or non-dermoscopic image sources remains to be validated. Future studies should therefore explore multi-institutional datasets and real-world deployment scenarios to assess robustness and scalability. The proposed framework offers a practical, interpretable solution for AI-assisted skin lesion diagnosis and demonstrates strong potential for clinical deployment.
Assessing Consumer Perception in Muslim-made Cosmetics: A Relationship Quality Perspective Scorita, Kurnia Budhy; Handaru, Agung Wahyu; Wibowo, Setyo Ferry
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1016

Abstract

This study investigates the drivers of repurchase intention in the Muslim-made cosmetics market by examining the mediating roles of trust and satisfaction within a relationship quality framework. Grounded in Relationship Quality Theory and the Stereotype Content Model, the research integrates perceived product quality, brand image, and perceived AI warmth as key antecedents influencing consumer loyalty. A comparative analysis of two structural models (Model 1 and Model 2) is conducted to explore the directionality of the mutually reinforcing relationship between trust and satisfaction. Data were collected via structured online surveys from 439 Muslim consumers in Jakarta, Indonesia, and analyzed using PLS-SEM. Findings reveal that perceived AI warmth is the strongest predictor of both trust (t=7.587, p0.001) and satisfaction (t=8.874, p0.001). In Model 1, trust significantly precedes satisfaction (t=4.869, p0.001); conversely, in Model 2, satisfaction reciprocally enhances trust (t=5.280, p0.001), supporting a dynamic, co-evolutionary process. Perceived product quality significantly impacts trust (t=3.780, p0.001) but only affects satisfaction when satisfaction is modeled as an antecedent to trust (Model 2: t=1.984, p=0.048). Brand image exerts a strong effect on satisfaction (Model 1: t=4.235, p0.001; Model 2: t=4.855, p0.001) but loses its direct path to trust in Model 2. Both trust and satisfaction have significant direct effects on repurchase intention (Model 1, Trust t=4.577, p0.001, Satisfaction t=8.538, p0.001; Model 2, Trust: t=4.630, p0.001; Satisfaction t=8.130, p0.001). The study validates the dual mediating roles of trust and satisfaction in translating perceptions of product quality, brand symbolism, and AI-induced warmth into behavioral loyalty. Theoretically, it advances a reciprocal, experience-driven model of relationship quality, extending prior discussions of reciprocity beyond traditional unidirectional frameworks. Practically, it offers actionable insights for marketers seeking to leverage AI personalization and identity-based branding to cultivate long-term loyalty in culturally sensitive, value-driven markets.
Software to predict maternal and child health risks with machine learning Fitriana, Fitriana; Zulkifli, Zulkifli; Rahayu, Sri
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1088

Abstract

Objective: Maternal healthcare services are essential in public health, prioritizing the health and well-being of women throughout pregnancy, childbirth, as well as the postpartum period. The services include various efforts to safeguard the health of both the mother and the unborn child. During these stages, mothers face numerous risks and complications, making early risk detection critical for ensuring the safety of the pregnancy. Method: A novel method is needed that enables more accurate and affordable screening to improve early detection as well as increase maternal and child healthcare. Therefore, this study aimed to propose a solution including the development of software that uses the Naive Bayes algorithm to predict maternal and child health risks. The perceptions provided by the application served as an initial diagnostic reference for both expectant mothers and healthcare providers, offering a cost-effective as well as precise alternative. Result: During the analysis, the Naive Bayes algorithm was compared with Neural Network (NN) and Random Forest (RF) models to evaluate the prediction accuracy. Among the models used, NN produced the lowest accuracy at 48%. Conclusion: The estimated cost for developing this application was IDR 1,635,913.
Image-Based Detection of Reduced Security Features in Indonesian Banknotes Using U-Net Architecture Andini, Silfia; Tukino, Tukino
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1087

Abstract

The circulation of fake currency banknotes in Indonesia continues to rise alongside rapid technological advancements, while conventional verification systems remain limited and often ineffective in detecting subtle authenticity cues. The main objective of this study is to develop an image-based fake currency detection system using the U-Net deep learning architecture and its modified version, T-Net, to enhance feature extraction and classification accuracy. The key contribution of this research lies in combining convolutional architectures with a practical, web-based interface that enables real-time image analysis, thus bridging the gap between model performance and user accessibility. A quantitative experimental method was employed, involving model development in Python using TensorFlow and Keras, and implementation of a Flask-based web application for real-time classification. The research utilized a dataset of 2,141 Indonesian rupiah banknote images, consisting of 1,015 genuine and 1,126 fake currency samples synthetically generated through digital modification of security features such as watermarks and color-shifting ink. Image preprocessing included resizing, normalization, and augmentation techniques such as random flipping and brightness adjustment to enhance data quality. Three convolutional architectures U-Net, ResNet-50, and the modified T-Net were trained and compared using identical hyperparameters. The T-Net model achieved the best performance, with 97.8% training accuracy, 82.6% validation accuracy, precision of 0.83, recall of 0.80, and an F1-score of 0.81. Despite the performance gap indicating overfitting, the model effectively distinguishes genuine from fake currency notes. The Flask-based interface allows users to upload images and receive classification results from all three models within 0.3–1.8 seconds per image. The findings demonstrate the feasibility and efficiency of U-Net based architectures for image-driven fake currency detection and provide a foundation for developing advanced, reliable, and real-time financial authentication systems that can strengthen digital security infrastructures in future applications.
EagleEyes: An Artificial Intelligence-Based Approach for Automatic Traffic Violation Detection Using Deep Learning Gata, Windu; Haris, Muhammad; Prasetiyowati, Maria Irmina; Harianto, Sony
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1097

Abstract

Rapid urbanization and the advancement of smart city programs in Indonesia necessitate intelligent, automated solutions for traffic monitoring and law enforcement. This study introduces EagleEyes, an artificial intelligence–based framework designed for automatic detection of multiple traffic violations by integrating the YOLOv8 deep learning architecture with Optical Character Recognition (OCR) for vehicle license plate identification. YOLOv8 was selected due to its anchor-free design, decoupled detection head, and enhanced feature fusion modules, which collectively improve detection accuracy, convergence speed, and small-object recognition compared to YOLOv5 and YOLOv7, while maintaining lightweight computational efficiency suitable for real-time applications. The proposed system was trained on a multi-class dataset representing common Indonesian violations, including seat belt non-compliance, helmet absence, motorcycle overcapacity, and unreadable license plates. Experimental results demonstrate robust performance, achieving a precision of 0.91, recall of 0.92, and mean average precision (mAP@0.5) of 0.96 at the optimal epoch, with an average inference speed of 25 frames per second and total training time of approximately 15 minutes on an NVIDIA RTX GPU. The OCR module attained an average recognition accuracy of 98.7%, although its performance decreased for vehicles captured beyond a five-meter distance due to reduced clarity and illumination inconsistencies. Implemented as a web-based application using the Flask framework, EagleEyes enables flexible browser-based visualization, and can be seamlessly integrated into Indonesia’s Electronic Traffic Law Enforcement (ETLE) infrastructure. Overall, the system demonstrates high potential to enhance smart city traffic management through scalable, AI-driven, and ethically responsible automation.
Assessing Large Language Models for Zero-Shot Dynamic Question Generation and Automated Leadership Competency Assessment Gheartha, I Gusti Bagus Yogiswara; Adiwijaya, Adiwijaya; Romadhony, Ade; Ardiansyah, Yusfi
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.970

Abstract

Automated interview systems powered by artificial intelligence often rely on fine-tuned models and annotated datasets, limiting their adaptability to new leadership competency frameworks. Large language models have shown potential for generating questions and assessing answers, yet their zero-shot performance, operating without task-specific retraining remains underexplored in leadership assessment. This study examines the zero-shot capability of two models, Qwen 32B and GPT-4o-mini, within a multi-turn self-interview framework. Both models dynamically generated questions, interpreted responses, and assigned scores across ten leadership competencies. Professionals representing the role of Digital Marketing and Account Manager participated, each completing two AI-led interview sessions. Model outputs were evaluated by certified experts using a structured rubric across three dimensions: quality of behavioral insights, relevance of follow-up questions, and fit of assigned scores. Results indicate that Qwen 32B generated richer insights than GPT-4o-mini (mean = 2.86 vs. 2.62; p less than 0.01) and provided more differentiated assessments across competencies. GPT-4o-mini produced more consistent follow-up questions but lacked depth in interpretation, often yielding generic outputs. Both models struggled with accurate scoring of candidate responses, reflected in low answer score ratings (Qwen mean = 2.35; GPT mean = 2.21). These findings suggest a trade-off between insight richness and scoring stability, with both models demonstrating limited ability to fully capture nuanced leadership behaviors. This study offers one of the first empirical benchmarks of zero-shot model performance in leadership interviews. It underscores both the promise and current limitations of deploying such systems for scalable assessment. Future research should explore competency-specific prompt strategies, fairness evaluation across demographic groups, and domain-adapted fine-tuning to improve accuracy, reliability, and ethical alignment in high-stakes recruitment contexts.
CNN-LSTM with Multi-Acoustic Features for Automatic Tajweed Mad Rule Classification Anggraini, Nenny; Rahman, Yusuf; Hidayanto, Achmad Nizar; Sukmana, Husni Teja
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1062

Abstract

The rules of mad recitation in the Qur’an are a crucial aspect of tajwīd, governing the lengthening of vowel sounds that affect both meaning and recitational accuracy. Despite its importance, there is currently no reliable automatic system capable of classifying mad rules based on voice input. This study proposes a deep learning-based approach using a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to automatically classify mad rules from Qur’anic recitations. The research follows the CRISP-DM methodology, covering data understanding, preparation, modeling, and evaluation stages. Acoustic features were extracted from 3,816 annotated audio segments of Surah Al-Fātiḥah, combining Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Root Mean Square (RMS) to represent phonetic and prosodic attributes. The CNN layers captured spatial characteristics of the spectrum, while LSTM layers modeled temporal dependencies of the audio. Experimental results show that the combination of all four features achieved an accuracy of 97.21%, precision of 95.28%, recall of 95.22%, and F1-score of 95.25%. These findings indicate that multi-feature integration enhances model robustness and interpretability. The proposed CNN-LSTM framework demonstrates potential for practical deployment in voice-based tajwīd learning tools and contributes to the broader field of Qur’anic speech recognition by offering a systematic, ethically grounded, and data-driven approach to mad classification.
Self-consistency and Graph-based Filtering to Enhance Synthetic Arabic SMS Generation for Smishing Detection Alotaibi, Amal; Almasre, Miada; Surougi, Hadeel; Alkhozae, Mona; Alghanmi, Nouf
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1033

Abstract

Smishing or SMS phishing is a growing cybersecurity threat in mobile security, with Arabic-speaking regions particularly vulnerable due to the absence of large, labeled datasets. The main objective of this study is to develop a scalable pipeline that can generate and classify Arabic SMS messages to overcome the lack of data and enhance detection performance. The contributions are threefold: (i) constructing a balanced dataset of 6,903 messages by combining 903 synthetic samples with 6,000 real Arabic SMS messages; (ii) introducing a hybrid generation framework that integrates a fine-tuned GPT-3.5-turbo language model with Conditional WGAN embeddings, refined using self-consistency sampling and graph-based redundancy filtering; and (iii) evaluating the dataset using multiple machine learning (Logistic Regression, Random Forest, SVM) and deep learning (CNN, BERT) models. The pipeline unifies adversarial embedding generation, large language model fine-tuning, and cosine similarity filtering. Experimental results show consistently strong performance: Logistic Regression and Random Forest both achieved accuracy of 0.9949 and F1-score of 0.9950, while SVM outperformed all with accuracy 0.9957 and F1-score 0.9957. Among deep learning models, CNN reached accuracy 0.9942 and F1-score 0.9942, and BERT achieved 0.9900 across all metrics. These findings confirm that while SVM is most effective for this dataset, CNN and BERT add robustness by capturing semantic subtleties. Visual analyses, including confusion matrices and t-SNE projections, validated the overlap between real and synthetic embeddings, while comparative tables positioned this study within the context of recent Arabic smishing research. The novelty of this work lies in combining self-consistency and graph-based filtering within a hybrid generation-classification pipeline tailored for Arabic SMS, providing a reproducible framework extendable to low-resource, multilingual, and cross-platform environments such as WhatsApp and Telegram.
IndoBERT-SupCon: A Supervised Contrastive Learning Model for Analyzing Public Perception on Halal Tourism Octafia, Sri Mona; Malik, Rio Andika; Weriframayeni, Annisa; Delpa, Delpa
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1045

Abstract

The primary objective of this research is to develop and evaluate a robust deep learning model for accurately analyzing stakeholder perceptions of halal tourism development in Pariaman, West Sumatra, based on qualitative textual data. The main contribution is the introduction of IndoBERT-SupCon, a novel architecture that enhances the Indonesian BERT model with a Supervised Contrastive Learning (SupCon) mechanism. A novel method for producing more discriminative feature representations for complex viewpoints is presented in this paper, which is one of the first to use this sophisticated fine-tuning technique to Indonesian socio-political sentiment analysis. Conceptually, the model is trained to simultaneously minimize classification error while optimizing the feature space, pulling representations of similar sentiments closer together and pushing dissimilar ones further apart. To achieve this, we collected 1,022 primary textual responses through online surveys with tourists and in-depth interviews with key stakeholders, including SME owners and government officials. The SMOTE oversampling technique was employed on the training data to mitigate class imbalance. Experimental results on the test data demonstrate that the IndoBERT-SupCon model achieved outstanding performance, with a final accuracy of 96.59% and a macro F1-score of 0.97. These results significantly surpass the performance of a standard fine-tuned IndoBERT baseline, confirming the effectiveness of the SupCon approach. The findings provide the Pariaman local government with a highly valid, data-driven tool for more responsive and effective policy formulation. This research offers a robust framework that can be applied to other public policy domains, showcasing the value of advanced deep learning in transforming qualitative stakeholder feedback into actionable insights.

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