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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 20 Documents
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Search results for , issue "Vol 7, No 2: May 2026" : 20 Documents clear
Multidimensional Data-Driven Modeling of Sustainable E-Commerce Development with Direct and Interaction Effects Mai Thanh Loan; Phan Thanh Tam
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1213

Abstract

Sustainable e-commerce development has become a critical issue for emerging economies as digital markets expand rapidly, alongside growing concerns about regulatory effectiveness, trust, and resource efficiency. This study investigates the key factors influencing sustainable e-commerce development in Vietnam by explicitly integrating Institutional Theory, the Technology–Organization–Environment (TOE) framework, and the Resource-Based View (RBV) into a unified structural equation modeling (SEM) framework. Institutional Theory is operationalized through regulatory quality, capturing the role of formal rules and enforcement mechanisms in shaping market stability and legitimacy. The TOE framework is reflected in digital infrastructure, government support, and competitive pressure, which together represent technological readiness and environmental conditions. RBV is operationalized through resource availability and management capacity, emphasizing firms’ internal capabilities to sustain long-term e-commerce performance. In addition, trust is incorporated as both a direct determinant and a moderating mechanism that strengthens the effectiveness of institutional and organizational factors. A mixed-method research design was employed. The qualitative phase involved in-depth discussions with 35 policymakers, business managers, and e-commerce platform managers to refine the theoretical integration and measurement scales. Based on these insights, a structured questionnaire was administered to frequent online shoppers in Ho Chi Minh City and Dong Nai Province, yielding 653 valid responses. SEM results indicate that regulatory quality, digital infrastructure, government support, competitive pressure, resource availability, and trust all have significant positive effects on sustainable e-commerce development. Resource availability and regulatory quality exert the strongest impacts, while trust and management capacity significantly moderate the effects of regulatory quality and resource availability, respectively. By explicitly mapping institutional, technological, organizational, and relational constructs into a coherent SEM framework, this study provides a theoretically grounded and empirically validated model of sustainable e-commerce development in an emerging economy context. The findings offer valuable implications for policymakers and practitioners seeking to foster a resilient, trustworthy, and sustainable e-commerce ecosystem in Vietnam and similar developing economies.
RankPro-M Method to Alleviate the Sparsity Problem in Collaborative Filtering Sri Lestari; Yulmaini Yulmaini; Suhendro Yusuf Irianto; Hari Sabita
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1173

Abstract

The rapid shift from conventional commerce to online platforms has been driven by evolving consumer behavior that demands fast, accurate, and personalized services. Consequently, e-commerce has become a primary channel for product marketing and service delivery without temporal or spatial constraints. However, the continuous expansion of e-commerce platforms has led to a substantial increase in both the volume and diversity of available products, thereby complicating the task of delivering personalized recommendations aligned with user preferences. Recommender systems offer an effective solution to this challenge, with Collaborative Filtering (CF) being among the most widely adopted techniques. Despite its popularity, CF suffers from a critical limitation known as the data sparsity problem, which adversely affects recommendation accuracy and system reliability. This study proposes RankPro-M, a ranking-oriented imputation approach designed to mitigate the impact of sparsity in recommender systems. RankPro-M operates by identifying items with high rating frequency and imputing missing ratings using mode values as representations of dominant user preferences. The imputed rating matrix is subsequently processed through ranking aggregation mechanisms (Borda, Copeland, and WP-Rank) to generate item recommendations. Experimental results demonstrate that the application of RankPro-M consistently improves recommendation quality, as indicated by increased Normalized Discounted Cumulative Gain (NDCG) values across multiple evaluation scenarios. These findings confirm that RankPro-M effectively addresses data sparsity and enhances the performance of ranking-based recommender systems.
A Modified Watershed Algorithm for Rice Plant Growth Stage Analysis Teri Ade Putra; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1117

Abstract

Information technology plays a crucial role in enhancing various sectors, including agriculture. In particular, technological advancements in crop monitoring are essential for sustainable food production, where accurate growth analysis is vital. Image-based approaches have emerged as a promising tool for assessing crop growth, particularly in rice plants. This study aims to enhance rice plant image segmentation using an improved Watershed algorithm, integrating the Laplacian operator and Distance Transform. This study utilizes a Support Vector Machine (SVM) classifier for segmenting and classifying rice plant growth stages, achieving accuracy, precision, recall, and F1-score metrics. The dataset consists of 1080 images of rice plants, with 74 images used for training, 31 for testing, and 975 images for validation. The image processing pipeline involves preprocessing steps such as grayscale conversion, normalization, color segmentation, Otsu thresholding, filtering, and edge detection. Following preprocessing, the Watershed algorithm is applied in two scenarios: the conventional method and the enhanced method with the Laplacian operator and Distance Transform. Performance evaluation is based on accuracy, precision, recall, and F1-score metrics. The results show that the enhanced Watershed algorithm significantly outperforms the conventional method, achieving an accuracy of 99.58%, precision of 80.55%, recall of 79.92%, and an F1-score of 81.50%. While there is a slight imbalance in precision and recall, the model demonstrates reliable performance in identifying rice plant growth. This study confirms that integrating the Laplacian operator and Distance Transform into the Watershed algorithm significantly improves segmentation accuracy, supporting the development of automated monitoring systems in smart farming. Furthermore, this approach opens avenues for application in other crops and diverse environmental conditions.
An Adaptive Random Forest for Data Stream Sentiment Classification under Concept Drift Brian Farrel Arkana; Sudianto Sudianto; Nenen Isnaeni
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1153

Abstract

Data labeling plays a crucial role in determining the performance of machine learning models, especially in data stream environments where concept drift frequently occurs. The primary objective of this study is to analyze the effectiveness of adaptive learning models in managing dynamic data distribution changes and to evaluate the influence of different labeling strategies on sentiment classification performance using user reviews from the OVO mobile application. The research contributes to understanding how labeling approaches interact with adaptive modeling under real-time data stream conditions. Two labeling methods were employed: score-based labeling derived from user ratings and content-based labeling generated automatically using the IndoRoBERTa language model. These labeled data streams were evaluated using two classifiers: a conventional Random Forest model and an Adaptive Random Forest model designed to handle evolving data distributions. The evaluation was conducted through streaming experiments that continuously fed new review data to simulate real-world drift scenarios. The results reveal that in the score-based labeling scenario, the conventional Random Forest model’s accuracy gradually declined, reaching a final accuracy of 31%, while the Adaptive Random Forest achieved 80%, reflecting a 49% performance gap. In the content-based labeling scenario, both models improved over time, with final accuracies of 57% for Random Forest and 76% for the adaptive model, resulting in a 19% difference. These findings indicate that Adaptive Random Forest is more robust in adapting to distributional and temporal changes in data streams regardless of the labeling strategy used. This study implies that combining adaptive learning with semantically rich labeling approaches can substantially enhance model reliability in real-time sentiment analysis tasks. Future research may further explore hybrid adaptive mechanisms to improve the resilience of data stream classification models across various domains.
Nutritionally Balanced Menu Optimization for a Healthy Lifestyle using Integer Linear Programming Suwarno Suwarno; Anderson Arvando; Davina Davina; Brain Gantoro; Hendi Sama; Deli Deli
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1141

Abstract

Unhealthy dietary patterns and limited access to personalized nutrition guidance contribute significantly to chronic diseases such as diabetes. These issues highlight the need for a reliable, data-driven approach capable of generating individualized dietary recommendations aligned with nutritional standards. This study aims to develop an Integer Linear Programming (ILP) approach integrated with nutritional datasets to generate personalized and nutritionally balanced meal plans. The goal is to determine whether ILP can effectively balance calorie and macronutrient distribution according to user-specific health profiles while ensuring compliance with dietary guidelines and disease-related restrictions. This study applied an ILP-based optimization framework to calculate total daily energy expenditure and macronutrient ratios, incorporating disease-specific constraints and balanced food distributions across meals. Using 244 standardized food items from clinical dietary data, the model’s performance was validated through comparisons with three AI models (ChatGPT, Gemini, DeepSeek) and a certified medical expert across three evaluation rounds. All AI models indicated that the generated meal plans adhered to macronutrient balance and health-specific requirements. Expert validation produced a mean score of 4.85 out of 5 on a Likert scale, reflecting strong agreement regarding the system’s nutritional adequacy, practicality, and safety. These outcomes confirm the ILP framework’s capability to produce balanced, individualized, and clinically sound meal plans. results demonstrate that ILP-based optimization can effectively generate scientifically sound and practical dietary recommendations, meeting both nutritional standards and user-specific needs. The findings highlight ILP’s potential as a computational decision-support tool that complements professional nutrition guidance. Future work should enhance the objective function by adding parameters that model individual preferences, allergy limitations, and cultural dietary norms, and should incorporate extensive clinical datasets to support adaptive recommendation mechanisms that consider chrononutrition, nutritional adequacy, and preparation methods, along with expert-driven adjustments to portion sizes and meal timing for more tailored dietary guidance.
SME Business Intelligence Support Using Retrieval-Augmented Generation and RFM Segmentation Rosalina Rosalina; Noor Lees Ismail; Genta Sahuri; Joseph Tedja Nugraha Wibawa
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1163

Abstract

This study presents the design and evaluation of a cloud-based business intelligence support system for small and medium enterprises that integrates retrieval-grounded text generation with recency–frequency–monetary customer segmentation to enhance digital customer communication and promotional decision making. The primary objective is to assist individual small businesses in responding accurately to customer inquiries while simultaneously leveraging historical transaction data to identify actionable customer groups, all within their existing messaging workflows through a mobile keyboard interface. The proposed framework combines two complementary components. The first component automatically generates customer replies by retrieving semantically relevant information from a structured business knowledge base and using it to produce grounded, context-aware responses. The second component analyzes invoice records to segment customers into loyal, moderate, and at-risk groups, enabling sellers to tailor promotional strategies based on observed purchasing behavior. The system is implemented as a cloud service accessed by individual enterprises without requiring local infrastructure or model training. System evaluation was conducted using real small business data collected over several weeks. Experimental procedures included retrieval faithfulness analysis, response correctness evaluation with confidence intervals, customer cluster validation using silhouette analysis, end-to-end latency measurement, and structured user acceptance testing. Performance results demonstrate that the retrieval mechanism consistently provides accurate knowledge grounding, while the segmentation module effectively distinguishes high-value and churn-risk customers. The average response time remained within a range perceived as acceptable for real-time mobile conversations, and user testing confirms that the keyboard-based interface does not disrupt normal communication practices. The findings indicate that embedding retrieval-grounded generation and lightweight customer analytics directly into daily messaging tools can significantly improve the operational efficiency of small enterprises. This integrated approach reduces the burden of manual response handling while enabling data-driven promotional decision making. The framework offers a practical pathway for adopting artificial intelligence in small business environments and provides a foundation for future enhancements such as temporal behavior modeling and multilingual support.
A Hybrid TF-IDF and Knowledge Graph-Enhanced Retrieval-Augmented Generation Framework with Large Language Models for Domain-Aware Question Answering Lilyani Asri Utami; Hilda Rachmi; Syarif Hidayatulloh
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1136

Abstract

This study aims to develop a domain-aware legal Question-Answering (QA) system tailored for Indonesia’s Micro, Small, and Medium Enterprises (MSMEs) by proposing a hybrid Retrieval-Augmented Generation (RAG) framework that integrates Term Frequency–Inverse Document Frequency (TF-IDF), Knowledge Graph (KG), and Large Language Model (LLM) components. In this framework, TF-IDF contributes by performing lexical-level retrieval to identify the most relevant documents based on keyword weighting; the KG enriches this retrieval by providing semantic relationships among legal entities, enabling deeper contextual understanding; and the LLM generates coherent responses conditioned on both lexical and semantically grounded evidence. Together, these components work synergistically to strengthen factual grounding during retrieval and improve contextual reasoning during generation. Methodologically, the system processes a curated dataset of 1,400 legal question–answer pairs collected from national legal repositories, including legislation, government regulations, and MSME digitalization guidelines. The process includes text preprocessing, keyword extraction using TF-IDF, semantic enrichment through a KG that maps legal entities and their relationships, and answer generation via an LLM powered by the RAG pipeline. The system was evaluated using Precision, Recall, F1-Score, Bilingual Evaluation Understudy (BLEU), and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, validated by five legal experts. Results show an accuracy improvement from 76.5% to 83.5% after integrating KG, with Precision of 0.853, Recall of 0.877, and F1-Score of 0.865. The generative evaluation yielded a BLEU score of 0.9276 and ROUGE-L of 0.9301, indicating strong linguistic and semantic alignment between system outputs and expert-authored references. The study concludes that this approach offers a practical foundation for building AI-based legal assistance tools and highlights future opportunities for expansion to other legal domains and multilingual RAG applications.
Applying Transfer Learning on Various GNN Model Training in Indoor Positioning System Tasks Kevin Wijaya; Hanif Muhammad Sangga Buana; Gede Putra Kusuma
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1150

Abstract

Determining location and orientation has always been a fundamental challenge, driving advances from maps and compasses to modern global navigation satellite systems (GNSS). However, GNSS performs poorly indoors due to signal attenuation and lack of elevation accuracy, necessitating the development of indoor positioning systems (IPS). Various technologies such as Wi-Fi, Bluetooth Low Energy (BLE), and RFID have been deployed, typically relying on received signal strength (RSS) and fingerprinting to improve accuracy. While previous research focused on training a single model for an entire building, this study explores the creation of floor-specific models by applying transfer learning to various GNN models. This is done to address the substantial signal distortion between floors. Using the UTSIndoorLoc dataset, we evaluate Graph Attention Network (GAT), GraphSAGE, and Graph Convolutional Network (GraphConv) for predicting two-dimensional indoor positions based on RSSI fingerprints. We propose 2 transfer learning model training methods, Schema A and Schema B. Schema A trains the base model iteratively through each floor, and Schema B trains the base model on a unified dataset. Schema B with GraphConv achieved the best results with a mean positioning error of 6.2176 meters. Whilst Schema A achieved a best-case mean positioning error of 6.3900 meters. Both outperforming the standard unified model which has a mean positioning error of 8.0808 meters.
Development of Color Segmentation and Texture Analysis Algorithms for Early Detection of Green Vegetable Deterioration in Retail Environments Dinul Akhiyar; Iskandar Fitri; Gunadi Widi Nurcahyo
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1094

Abstract

Vegetable deterioration in retail environments is often accelerated by improper storage conditions, leading to quality degradation, economic losses, and reduced consumer trust. Early detection of deterioration is therefore essential to enable timely preventive actions before visible spoilage becomes severe. This study proposes an integrated image-based framework for early detection of spinach leaf deterioration by combining K-Means++ for robust color segmentation, Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction, and Convolutional Neural Network (CNN) for classification. K-Means++ improves segmentation stability through optimized centroid initialization, GLCM captures subtle texture variations associated with early spoilage, and CNN enables accurate classification by learning complex visual patterns from segmented images. The dataset consists of 642 spinach leaf images captured under controlled lighting for initial calibration and under varying lighting conditions to simulate real-world retail environments. Experimental results show that the standard K-Means algorithm achieved an average classification accuracy of 77%, while the proposed K-Means++ segmentation improved accuracy to 81.86%. Furthermore, CNN-based validation achieved the highest classification accuracy of 94.82%, demonstrating strong generalization capability. The novelty of this work lies in the optimized integration of K-Means++ segmentation under lighting variability, selective GLCM feature utilization validated through ablation analysis, and end-to-end CNN-based validation with real-time deployment feasibility. The proposed framework offers a practical, scalable, and non-destructive solution for automated freshness monitoring in retail environments and can be extended to other leafy vegetables.
Improved Hybrid GoogLeNet-Based Deep Learning Optimization for Standardized Straw Mushroom Quality Classification in Indonesia Bayu Priyatna; Titik Khawa Abdurahman; Muhammad Fahmi Miskon; April Lia Hananto; Agustia Tia Hananto; Aviv Yuniar Rahman
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1206

Abstract

Deep learning plays a crucial role in modern computer vision due to its ability to automatically extract hierarchical features from large-scale image data. Among various architectures, Convolutional Neural Networks (CNNs) have been extensively utilized for image pattern interpretation, including in agricultural product inspection. Straw mushrooms (Volvariella volvacea) are important agro-industrial commodities in Indonesia; however, their quality assessment still relies on subjective manual evaluation based on the Indonesian National Standard (SNI:01-6945-2003), leading to inconsistency in grading results. To address this limitation, this research proposes an Improved Hybrid GoogLeNet model integrated with a YOLO-based detection framework and hybrid preprocessing to enhance feature clarity and classification robustness. The system is capable of conducting object detection, 3-class morphological quality classification (Pure White, Oval, and Black Spot/Defect), and automatic diameter measurement using calibrated pixel-to-centimeter conversion. Performance evaluation is carried out by benchmarking the proposed model against several popular deep learning architectures including YOLOv5, LeNet, AlexNet, VGGNet, and ResNet. Experimental results demonstrate that the Improved Hybrid GoogLeNet achieves the highest performance with precision of 97.99%, recall of 96.07%, and F1-score of 96.98%, along with low misclassification rates across all classes. These results indicate that the proposed method provides accurate, reliable, and efficient quality assessment that supports standardized automated grading in industrial applications. Therefore, this study contributes to the advancement of intelligent computer vision solutions for digital transformation in the Indonesian mushroom agro-industry.

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