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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 3: September 2025" : 55 Documents clear
Factor Analysis on Teaching Quality Management for Art Design Students Using Data Driven Approach Junru, Chen; Sangsawang, Thosporn; Pigultong, Metee; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.746

Abstract

This study aimed to improve teaching quality management for Art Design students using a data-driven approach through three objectives: (1) synthesizing key factors influencing instructional quality, (2) analyzing those factors using expert consensus, and (3) evaluating student satisfaction after applying the data-driven methodology. The Delphi Method was used to gather insights from 17 education experts, while 30 purposively selected Art Design students participated in satisfaction assessments. Data collection involved questionnaires and interviews, with analysis techniques including mean, standard deviation, Coefficient of Variation (CV), and t-tests. Cronbach’s α was 0.98, indicating high internal reliability. Results showed expert consensus on relevant teaching quality factors (M = 3.92, SD = 0.33, CV = 19.96, p = .002). Key aspects identified included instructional design, digital integration, feedback mechanisms, and curriculum alignment. Post-intervention analysis revealed significant student improvement, with average skill levels increasing from 16.12 (SD = 0.89) to 20.34 (SD = 0.566, p = .002). Student satisfaction reached 78.59%, with a mean of 3.90 (SD = 0.72, CV = 18.78). All statistical terms were properly defined and contextualized. The findings underscore the role of structured data analysis and expert-informed models in enhancing instructional strategies, aligning teaching with professional expectations, and promoting continuous improvement in Art and Design education.
Designing a Data-Driven, Innovative Practical Model for Minority Dance Courses in Higher Education Institutions Zhou, Dan; Sangsawang, Thosporn; Vipahasna, Kitipoom; Prammanee, Noppadol; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.768

Abstract

This study aimed to design and evaluate a data-driven, innovative practical teaching model for minority dance courses in higher education by integrating constructivist learning theory, multicultural education, and experiential learning. The objectives were threefold: (1) to develop a systematic instructional design framework, (2) to measure students' knowledge improvement before and after applying the model, and (3) to assess student satisfaction with the model, particularly regarding cultural identity, learning experience, and engagement. A total of 17 expert instructors from Chinese universities and Kunming University were selected through purposive sampling to contribute to the design process using the Delphi Method. Additionally, 402 first-year dance students participated in evaluating the model’s effectiveness. Quantitative analysis was conducted using means, standard deviations, coefficients of variation, and t-tests. The experts' evaluation of the teaching model yielded a mean of 4.63 (SD = 0.31, CV = 17.84, p = .002), indicating moderate agreement. Student performance significantly improved after intervention, with average skill scores rising from 16.11 (SD = 0.884) to 20.33 (SD = 0.564), p = .002. Student satisfaction reached 78.58% (mean = 3.90, SD = 0.72, CV = 18.78). The hybrid teaching model—blending traditional methods with interactive digital tools and interdisciplinary content (effectively enhanced students' dance proficiency, cultural awareness, and engagement). These findings support the use of blended learning and data-informed instructional strategies to drive innovation and improve outcomes in minority dance education.
Fine-Grained Sentiment Analysis Approach on Customer Reviews Based on Aspect-Level Emotion Detection Paramita, Adi Suryaputra; Jusak, Jusak
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.964

Abstract

In the era of digital platforms, customer reviews constitute a vital resource for understanding user sentiment and perception toward products and services. Traditional sentiment analysis methods predominantly operate at the document or sentence level, often missing fine-grained emotional cues tied to specific product or service aspects. To address this limitation, this study proposes a novel Fine-Grained Sentiment Analysis (FGSA) framework that performs aspect-level sentiment classification using a joint learning approach. The proposed model employs a hybrid deep learning architecture that integrates transformer-based contextual encoders with Bidirectional Long Short-Term Memory (Bi-LSTM) layers. This design allows the model to capture both rich contextual semantics and sequential dependencies a combination that has not been widely adopted in existing FGSA research. Additionally, we introduce a new annotated dataset of 5,000 customer reviews spanning multiple domains (electronics, food and beverages, and general services), enabling robust training and evaluation. Experimental results show that the model outperforms standard baselines, achieving an F1-score of 82.0% for aspect extraction and an accuracy of 79.8% for sentiment classification. Further analysis reveals consistent patterns, such as positive sentiments linked to design and quality, and negative sentiments associated with customer service and delivery. These insights highlight the practical value of aspect-level sentiment modelling. The key contribution of this work is the integration of a transformer-Bi-LSTM joint architecture for aspect-based sentiment analysis, supported by a domain-diverse benchmark dataset. This framework enhances the interpretability and granularity of sentiment insights and sets a foundation for future research in multilingual and multimodal contexts.
Lora Communication System for Early Detection and Monitoring of Water Toxicity in Floating Net Cages Rahmafadilla, Rahmafadilla; Irawati, Indrarini Dyah; Rizal, Mochammad Fahru; Maidin, Siti Sarah
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.787

Abstract

Floating Net Cages/ Keramba Jaring Apung (KJA) are at risk of polluting the air, which can affect fish farming. Therefore, an early monitoring system is needed that can measure air quality such as temperature, pH, and dissolved oxygen (DO) in real-time. This system utilizes the LoRa RFM95W module to wirelessly transmit environmental data from sensors installed on the cages, which continuously monitor water quality parameters such as temperature, pH, and DO in real-time. The data obtained is then processed to monitor changes in water toxicity in real-time, allowing early detection of potential threats to the ecosystem. Tests were conducted at distances of 50m, 180m, 300m, 340m, and 440m. The results showed that the system worked well up to a distance of 300m with RSSI values between -85 dBm to -120 dBm and SNR more than 2 dB. However, at distances of 340m and 440m, the signal decreased and the delay increased. At a depth of 340m, only one experiment was successful with RSSI -134 dBm and SNR -6 dB, while at a depth of 440m, only a few experiments were successful with RSSI between -122 dBm to -132 dBm and SNR between 1 dB to -6 dB. The prototype system successfully transmitted real-time air quality data to a web-based monitoring center. Data from the sensors were sent via the LoRa network to a central server for further monitoring.
Optimizing Function-Level Source Code Classification Using Meta-Trained CodeBERT in Low-Resource Settings Septiadi, Abednego Dwi; Prasetyo, Muhamad Awiet Wiedanto; Daffa, Geusan Edurais Aria
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.902

Abstract

This study investigates the effectiveness of a meta-trained transformer-based model, CodeBERT, for classifying source code functions in environments with limited labeled data. The primary objective is to improve the accuracy and generalizability of function-level code classification using few-shot learning, a strategy where the model learns from only a few labeled examples per category. We introduce a meta-learning framework designed to enable CodeBERT to adapt to new function types with minimal supervision, addressing a common limitation in traditional code classification methods that require extensive labeled datasets and manual feature engineering. The methodology involves episodic few-shot classification, where each episode simulates a low-resource task using five labeled and five unlabeled samples per function class. A balanced subset of Python functions was sampled from the CodeXGLUE benchmark, consisting of ten function categories with equal representation. The source code was preprocessed by removing comments and docstrings, then tokenized into a fixed length of 128 tokens to fit the model input format. The meta-trained CodeBERT was evaluated across 10 episodes, each representing a different task composition. Results show that the model achieves an average classification accuracy of 73.0%, with high accuracy on function categories characterized by unique syntax patterns, and lower performance on categories with overlapping logic or naming structures. Despite this variability, the model-maintained accuracy above 60% in all episodes. These findings suggest that meta-learning significantly enhances the adaptability of CodeBERT to unseen tasks under data-constrained conditions. This research demonstrates that meta-trained transformer models can serve as practical tools for real-time code analysis, particularly in integrated development environments and continuous integration pipelines. Future work may include extending the framework to other programming languages and incorporating semantic code representations to further reduce classification ambiguity.

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