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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 628 Documents
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Newton Divided Difference Optimization for Fingerprint-Based Neural Virtual Screening against Avian Influenza A/H9N2 Siti Amiroch; Mohammad Jamhuri; Awawin Mustana Rohmah; Mohammad Hamim Zajuli Al Faroby; Chairul Anwar Nidom; Reviany Vibrianita Nidom
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1416

Abstract

Avian influenza A/H9N2 poses persistent zoonotic and veterinary threats, yet efficient computational tools for antiviral compound prioritization remain underdeveloped, particularly with respect to optimizer behavior in high-dimensional neural screening. This study proposes Newton Divided Difference (NDD) optimization, a lightweight positive diagonal curvature-aware training strategy, as a novel optimizer for fingerprint-based neural virtual screening against avian influenza A/H9N2, with the objective of evaluating its performance across aligned molecular fingerprint representations and chemically structured validation protocols. An aligned benchmark of 1,459 molecules consisting of 615 candidate active compounds and 844 decoys was represented by EState (79 features), PubChem (881 features), and Klekota–Roth (4,860 features) fingerprints, sharing identical molecule identities, labels, and split assignments. A fixed multilayer perceptron (MLP) classifier was trained with NDD and seven baseline optimizers under stratified, scaffold-key, and similarity-cluster split protocols across five repeated seeds. NDD achieved the highest descriptive ROC-AUC (Receiver Operating Characteriztic – Area Under the Curve) and PR-AUC (Precision-Recall Area Under the Curve) on Klekota–Roth fingerprints under scaffold-key and similarity-cluster protocols, and remained competitive under the stratified split with ROC-AUC of 0.9872. Architecture-sensitivity tests confirmed stable NDD performance across multiple network configurations, with ROC-AUC values ranging from 0.9876 to 0.9890. Compared with Hessian-free optimization, NDD reduced per-run runtime from approximately 50–54 seconds to approximately 8 seconds on Klekota–Roth under the same CPU-only configuration while achieving comparable ranking performance. The novelty of this work lies in the first systematic assessment of NDD for H9N2 neural virtual screening, demonstrating that positive diagonal curvature-aware scaling provides a practical, stable, and computationally efficient optimization alternative in sparse high-dimensional ligand-based screening settings, although external validation and prospective experimental confirmation remain necessary before practical antiviral prioritization.
Comparative Analysis of YOLOv8s and Faster R-CNN for High-Resolution UAV RGB Oil Palm Health Detection: Accuracy versus Inference Speed Trade-Off Kristia Yuliawan; Danny Manongga; Hendry Hendry
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1430

Abstract

Accurate and rapid detection of oil palm health conditions using UAV imagery is essential for supporting precision agriculture and large-scale plantation monitoring. However, challenges such as overlapping canopies, complex background textures, varying illumination, and severe class imbalance often reduce the reliability of automated detection systems. This study presents a comparative evaluation between YOLOv8s, a one-stage object detector, and Faster R-CNN, a two-stage detector, for identifying healthy and unhealthy oil palm trees using high-resolution UAV RGB imagery. The dataset consists of 2,303 annotated images collected from drone surveys and divided into training (70%), validation (20%), and testing (10%) subsets under a controlled experimental design. Both models were trained and evaluated using identical preprocessing pipelines and annotation formats to ensure fairness in comparison. Performance was assessed using precision, recall, F1-score, mean Average Precision (mAP@50 and mAP@50–95), and inference time. Experimental results show that YOLOv8s achieves superior performance with 0.987 precision, 0.998 recall, 0.977 mAP@50–95, and extremely fast inference speed of 1.1 ms per image. In contrast, Faster R-CNN achieves comparable detection accuracy at 0.981 precision and 0.993 recall but with significantly higher computational cost, reaching 875 ms per image. These findings indicate that YOLOv8s provides an optimal balance between accuracy and efficiency, making it more suitable for real-time UAV-based monitoring systems, while Faster R-CNN is more appropriate for offline and high-precision analytical tasks. The study contributes a standardized benchmarking framework for deep learning-based oil palm health detection and provides practical insights for selecting appropriate models in smart agricultural applications.
Financial Condition Prediction Using a Soft Voting Ensemble Model Based on Structured Financial Indicators and Unstructured News Data Ibnu Rasyid Munthe; Sumijan Sumijan; Muhammad Tajuddin
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1417

Abstract

The rapid growth of capital market participation has increased the need for reliable analytical tools to assess the financial conditions of listed companies. Conventional approaches commonly rely on structured financial indicators and may not fully capture contextual information reflected in financial news. This study aims to evaluate the predictive potential of structured financial data and unstructured textual data using a soft voting ensemble framework. The structured dataset consists of 1,263 financial records collected from the reports of manufacturing companies listed on the Indonesia Stock Exchange, whereas the unstructured dataset contains 6,329 online financial news records related to Indonesian stocks and listed companies. The structured data were processed through missing-value handling, normalization, and class balancing, while the textual data were processed through cleaning, case folding, tokenization, filtering, stemming, and Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction. The proposed ensemble model combines the probability outputs of six base classifiers: Decision Tree, Support Vector Machine, Multinomial Naive Bayes, Logistic Regression, Random Forest, and K-Nearest Neighbors. Experimental results show that the soft voting ensemble achieved an accuracy of 92.66% on the structured dataset and 98.53% on the unstructured dataset. These findings indicate that both financial indicators and textual information can provide valuable predictive signals for identifying company financial conditions. The contribution of this study lies in the comparative evaluation of two distinct data sources using a consistent ensemble-learning framework. However, the two datasets were evaluated independently rather than integrated into a single multimodal pipeline. Therefore, future research should develop a data-fusion mechanism to assess whether combining financial indicators and news-based sentiment can further improve predictive performance and strengthen decision support for investors and financial analysts.
Enhancing Support Vector Machines Accuracy Through Firefly Algorithm-Driven Feature Optimization for Forest-Fire Sentiment Classification Wafa Salma Sentanu; Dinar Ajeng Kristiyanti
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1144

Abstract

Forest fires are a growing global environmental issue that significantly impact ecosystems, human health, and exacerbate climate change. Data from 2023 indicate that approximately 11.91 million hectares of forest were lost due to fires, highlighting the urgent need for technological approaches in addressing this issue. One relevant approach is public sentiment analysis based on the social media platform X, which enables the rapid and real-time capture of public perception. In text-based sentiment analysis, key challenges include high-dimensional feature space and class imbalance, which can degrade the performance of machine learning algorithms. Therefore, this study applies feature selection methods based on swarm intelligence, namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA), to enhance classification efficiency and accuracy. The models evaluated include Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), using the Knowledge Discovery in Databases (KDD) approach, which involves selection, preprocessing, transformation with Term Frequency-Inverse Document Frequency (TF-IDF) and Synthetic Minority Oversampling Technique (SMOTE), and data splitting with an 80:20 ratio. Model performance was evaluated using four metrics: accuracy, precision, recall, and F1-score. The models achieved strong performance on the balanced training data, indicating effective learning after feature selection, with the fastest execution time recorded by FA optimization at 0.0071 seconds. To ensure a fair assessment of generalization, the main conclusions of this study are based on the testing results. On the testing data, SVM with FA achieved the highest accuracy of 96.36% with an execution time of 0.0064 seconds. Overall, swarm intelligence-based feature selection (PSO and FA) enhances the efficiency of conventional classifiers by reducing high-dimensional feature representations and execution time while maintaining strong predictive performance for forest-fire sentiment classification.
GoogLeNetMP: A Development of GoogLeNet Architecture for Multi-Class Microplastic Classification in Subsurface Water Image Halifia Hendri; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1372

Abstract

Microplastic pollution has become a major environmental concern due to its persistence in marine ecosystems and its potential impact on aquatic organisms and human health. Automatic detection of microplastic particles in underwater environments remains challenging because of turbidity, low contrast, light distortion, and the visual similarity between microplastics and natural marine objects. This study proposes GoogLeNetMP, an enhanced GoogLeNet-based deep learning architecture for multi-class classification of subsurface marine images into four categories: primary microplastics, secondary microplastics, non-microplastics, and marine biota. The proposed framework integrates basic image preprocessing (resizing and noise reduction) with a modified GoogLeNetMP architecture designed to intrinsically handle fine-grained feature extraction under degraded conditions, thereby minimizing the reliance on complex external enhancement pipelines. A dataset of underwater images acquired from the coastal waters of Padang, Indonesia, was used for model development and evaluation. Experimental results show that GoogLeNetMP outperformed the standard GoogLeNet model, achieving 95.75% accuracy, 92.80% sensitivity, 97.00% specificity, and an F1-score of 92.06%. The proposed model also demonstrated more stable training convergence and better discrimination of visually challenging classes. The architecture is designed to internalize the robust feature extraction process, thereby minimizing the reliance on extensive external enhancement pipelines while maintaining standard normalization steps for input consistency. These findings indicate that GoogLeNetMP is a promising approach for AI-based marine pollution monitoring and decision support in sustainable coastal management.
A Climate Driven Decision Support System for Rice Management Using SPI-3 Prediction and Particle Swarm Optimization Eka Putra; Syahril Efendi; Poltak Sihombing; T. Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1408

Abstract

Climate variability and irregular rainfall patterns have become critical challenges affecting rice productivity, irrigation planning, and agricultural sustainability. Previous studies have primarily focused on rainfall forecasting or drought monitoring independently, with limited attention given to transforming climate predictions into actionable agricultural management strategies. This study addresses this gap by proposing an integrated climate-driven decision support framework that combines predictive drought-index modeling with optimization-based agronomic decision-making for adaptive rice field management. The proposed framework integrates satellite-based rainfall observations, seasonal climatic characteristics, and large-scale climate variability indicators to predict short-term moisture conditions represented by the three-month standardized precipitation index. The framework consists of three interconnected stages: climate prediction, optimization, and recommendation generation. In the prediction stage, a gradient boosting regression model enhanced with Bayesian hyperparameter optimization was employed to model nonlinear relationships among rainfall accumulation, lag rainfall patterns, seasonal cyclic features, and climate variability indicators. In the optimization stage, particle swarm optimization was applied to determine optimal fertilizer dosage, irrigation allocation, and harvest timing under varying climate conditions. Experimental procedures included comparative evaluations across multiple machine learning models, hyperparameter tuning strategies, and optimization iterations. The research figures and tables demonstrate the complete framework architecture, prediction performance comparisons, optimization convergence behavior, and adaptive rice management recommendations. Experimental results show that the proposed framework achieved strong predictive performance with a coefficient of determination of 0.851, a root mean square error of 0.391, and a mean absolute error of 0.322. Comparative analysis further confirmed that integrating climate variability indicators significantly improved predictive accuracy compared with baseline models using only historical rainfall information. The optimization process also demonstrated stable convergence toward climate-adaptive agronomic recommendations.
Deep Deterministic Policy Gradient for Simulation-Based Control of Water Quality in Nano Aquatic Systems Iwan Fitrianto Rahmad; Syahril Efendi; Poltak Sihombing; Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1447

Abstract

This study proposes a simulation-based Deep Deterministic Policy Gradient (DDPG) framework for water-quality control in nano aquatic systems. Nano tanks are highly sensitive to small disturbances because their limited water volume reduces buffering capacity and causes rapid changes in dissolved oxygen, ammonia, pH, temperature, biological oxygen demand, and chemical oxygen demand. To represent these coupled dynamics, a nonlinear simulation model adapted from the Continuously Stirred Tank Reactor concept is developed and implemented as an OpenAI Gym-compatible reinforcement learning environment. The DDPG agent learns continuous control actions related to aeration, feeding, and filtration through repeated interaction with the simulated nano-tank environment. The proposed nonlinear CSTR-DDPG framework is evaluated against a linear-model DDPG baseline using RMSE, cumulative reward, and closed-loop control performance. Simulation results show that the nonlinear model reduced RMSE by 45.2% for dissolved oxygen, 64.0% for ammonia, and 61.3% for pH compared with the linear baseline. The DDPG agent also achieved a 41.7% higher cumulative reward under the same reward structure. These findings indicate that nonlinear simulation can provide a more informative training environment for DDPG-based water-quality control. However, the present study remains limited to simulation-based evaluation, and physical validation using calibrated sensors, actuators, communication-delay analysis, and real nano-tank experiments is required in future work.
An Interpretable Composite Index for Real-Time RFID Anomaly Detection in Predictive Maintenance Zhang Shude; Adnan Yahaya
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1406

Abstract

Radio-frequency identification (RFID) systems are widely deployed for asset and inventory tracking in industrial environments, yet their reliability degrades under dynamic conditions where early, subtle tag anomalies remain difficult to detect. The objective of this work is to develop an interpretable, lightweight, and real-time index for detecting anomalous tag behavior at the point of reading, addressing the limitation that most existing methods rely on a single indicator such as received signal strength or raw read counts and therefore lack sensitivity to incipient instability. The core idea is to fuse two dimensionally consistent sub-metrics into a single composite score: a read-speed deviation coefficient that quantifies instability in tag-read cadence, and a communication-frequency concealment coefficient that captures temporal and communication-rate irregularities, with each component mapping to a physically identifiable failure mode so the score is interpretable by construction. The contribution is this anomaly evaluation index together with a single-pass, constant per-event computation suitable for commodity reader hardware. Validation spanned three tiers: a simulated dataset of 500 virtual tags and 30,284 events with controlled anomaly injection, a semi-synthetic dataset built from industrial warehouse logs, and a public benchmark of approximately 1,100,000 reads across multiple tag manufacturers. Key results: on the simulated test set the index achieved 95.7% accuracy, an AUC of 0.924, a 6.0% false-positive rate, and a mean detection latency of 4.8 ms on an embedded ARM-class processor; ablation confirmed the complementary contribution of both sub-metrics (accuracy dropped when either was removed), and the index ran approximately 40× faster than a deep-learning baseline of comparable detection quality. The novelty lies in combining two dimensionally consistent, physically interpretable sub-metrics into one constant-cost score, delivering deep-learning-comparable accuracy with a 40× speedup, making it well suited to embedded, real-time anomaly detection in resource-constrained industrial deployments.
CLaGAtt: A Hybrid CNN-LSTM-GRU-Attention Model for Stunting Classification Based on Anthropometric Sequences Sofiansyah Fadli; Ahmad Tantoni; Novia Arista; M. Khairul Anam; Muhammad Bambang Firdaus
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1454

Abstract

Stunting is a chronic nutritional problem that requires accurate early identification because it affects child growth, cognitive development, and long-term human capital. This study adapts the CLaGAtt model, a hybrid CNN–LSTM–GRU–Attention architecture, for stunting classification using anthropometric sequence data. Rather than proposing a new deep learning architecture, the main contribution of this study lies in adapting the existing CLaGAtt framework through an integrated preprocessing pipeline, sequence construction strategy, class balancing using SMOTE, and an evaluation protocol specifically designed for stunting prediction. The preprocessing pipeline included irrelevant-column removal, data transformation, label encoding, standard scaling, class balancing using SMOTE, and sequence generation with a time step of five and a step of one. Three train–test split scenarios were evaluated, namely 90:10, 80:20, and 70:30. Experimental results showed that the 90:10 split produced the best performance, with 91.42% accuracy, 91.50% precision, 91.50% recall, and 91.43% F1-score. The 80:20 and 70:30 scenarios achieved 87.14% and 85.71% accuracy, respectively, indicating that larger training proportions improved model generalization in the available dataset. These findings suggest that the adapted CLaGAtt framework can effectively integrate convolutional feature extraction, sequential learning, and temporal attention for stunting classification from structured anthropometric data. Future work should validate the model on external datasets and integrate regional visualization to support priority intervention mapping.
Optimization of Croplands for Efficient and Sustainable Resource Use and Management Maria Cristina Punay Pammit; Julieta A. Delos Reyes; Antonio Jesus A. Quilloy; Danesto B. Anacio
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1129

Abstract

The farmers in Ilocos Norte, Philippines, produce vegetable crops during the dry season (November to May). During this time, they face simultaneous climatic and resource challenges, which impact their production decisions and threaten food security. Land use decisions at the farm level require trade-offs due to resource constraints and other inherent factors, thus, informed decision-making is called for. This study determines the optimal cropland allocation where farmers can earn the highest income given their level of existing resources and production objectives during the dry season. Only prime agricultural lands were considered, and it excluded livestock production due to resource limitations and more sophisticated models and analytical software required. Production data from 80 farmers were subjected to optimization through linear programming in Microsoft Excel Solver. Results revealed that the farmers should adjust their current production level and cropland areas during the dry season to further maximize profit. Particularly, the farmer-respondents should devote 2.22-ha croplands to tomato production to earn 208.09 percent higher profit, and another 2.90-ha for purple corn and peanut production to generate 41 percent more profit than their existing crop production system. The study provides scientific evidence that maximum income can be attained if the farmers in the study areas change their existing crop production to the identified best alternative. It also offers information that could be a basis for crafting a decision tool for efficient and sustainable agricultural land use and management in the respective areas. Future studies may consider incorporating price volatility, risk preferences of farmers, crop rotation requirements, and non-linear yield responses. Analysis of wider agricultural lands combined with livestock production may also provide a more comprehensive perspective.

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