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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 54 Documents
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Search results for , issue "Vol 7, No 1: January 2026" : 54 Documents clear
Dynamic IoT–PID Control for Energy-Efficient Water Distribution: EPANET-Based Digital Twin Validation in Varied Geographical Terrains Kusuma, Bagus Adhi; Isnaini, Khairunnisak Nur; Hamdi, Aulia
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.v7i1.1188

Abstract

Topographical heterogeneity in water distribution networks frequently causes pressure imbalance, hydraulic inefficiency, and elevated energy consumption, particularly in regions with significant elevation gradients. This study aims to develop and validate a dynamic Internet of Things (IoT)-based pressure control model within a cyber–physical system framework for energy-efficient water distribution under varied geographical conditions. The primary contribution of this work lies in the separation of strategic and tactical control layers, where a Digital Twin based on EPANET dynamically generates optimal pressure setpoints, while distributed proportional–integral–derivative controllers execute real-time valve regulation at the network edge. The research adopts a Design Science Research methodology to design, implement, and evaluate a four-layer architecture consisting of physical sensing and actuation, long-range communication, tactical control, and strategic simulation layers. Validation is conducted using EPANET-based simulations across three control scenarios: a baseline condition without dynamic control, a static rule-based valve control scenario, and the proposed dynamic IoT–PID control scenario. The experimental procedure involves comparative analysis using control performance metrics including overshoot, settling time, steady-state error, and root mean square error. Simulation results demonstrate that the baseline configuration suffers from severe pressure imbalance and hydraulic backflow, while static rule-based control partially mitigates inefficiencies but fails to adapt to demand variability. In contrast, the proposed dynamic IoT–PID approach achieves precise pressure regulation with overshoot below 2% and tracking error maintained under 0.5 meters across all evaluated scenarios. These findings confirm that integrating a Digital Twin with real-time PID control significantly improves pressure stability and operational efficiency. The proposed architecture offers practical implications for smart water infrastructure in geographically diverse regions, providing a scalable foundation for adaptive pressure management, energy optimization, and future digital-twin-driven water distribution systems.
Navigating Institutional Pressures and Cognitive Aspects: Strategies for Sustainable Corporate Performance in Creative Industries Ishkak, Iskhak; Wibowo, Setyo Ferry; Suparno, Suparno
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.v7i1.1058

Abstract

This study examines the relationship between institutional pressures and corporate performance through the lens of corporate cognitive aspects in the Indonesian creative industry sector. Data were collected from 531 business owners across fashion, food, and crafts sectors and analyzed using Structural Equation Modeling Partial Least Squares (SEM–PLS). The results show that institutional pressures significantly affect digital transformation (β = 0.656, p 0.001), entrepreneurial orientation (β = 0.748, p 0.001), dynamic capability (β = 0.660, p 0.001), and competitive advantage (β = 0.759, p 0.001). However, the direct effect of institutional pressures on corporate performance is not significant (β = 0.039, p = 0.231). Instead, digital transformation (β = 0.347, p 0.001), entrepreneurial orientation (β = 0.146, p = 0.007), and dynamic capability (β = 0.446, p 0.001) mediate the relationship, explaining 65% of the variance in corporate performance (R² = 0.65). This study contributes to the literature by highlighting the role of corporate cognitive aspects and emphasizing the integration of institutional theory into entrepreneurial practice. Moreover, it aligns with Sustainable Development Goals (SDGs) 9 and 12 by promoting responsible consumption and innovation in industry. One limitation of this study lies in its focus on Indonesia’s creative sector, suggesting that future research should explore broader contexts. The findings encourage businesses to adopt corporate cognitive aspects to achieve sustainable growth. Future studies could also investigate additional antecedents of corporate cognitive aspects and their influence on environmental performance.
Feature Engineering for Tropical Rainfall Forecasting Using Random Forest and Support Vector Regression Slamet, Cepy; Imron, Rizka M; Wahana, Agung; Maylawati, Dian Sa'adillah; Zulfikar, Wildan Budiawan; Ramdhani, Muhammad Ali
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.v7i1.1111

Abstract

The complex dynamics of weather variability in Indonesia, influenced by multiple climatic drivers, make rainfall forecasting in tropical regions a significant scientific challenge. This study proposes an automated feature engineering pipeline to enhance the performance of Random Forest Regression (RFR) and Support Vector Regression (SVR) models for tropical rainfall prediction. Monthly rainfall data spanning 388 months (1993–2025) from a BMKG station were used as the basis for model development. The pipeline systematically generates temporal, seasonal, statistical, and anomaly-based features to provide domain-informed representations for non-sequential learning algorithms. Model performance was evaluated under four temporal data partitioning scenarios using R², RMSE, and probabilistic confidence intervals derived from bootstrap residual simulations. Results indicate that RFR achieved the highest predictive accuracy (R² = 0.93; RMSE = 31.01 mm) and demonstrated superior temporal–seasonal stability (Rolling CV: R² = 0.81 ± 0.07; RMSE = 55.44 ± 16.18), with comparable performance between wet and dry seasons. Conversely, SVR showed greater sensitivity to seasonal variability, with R² dropping to 0.55 during the wet season, indicating higher uncertainty under extreme rainfall conditions. Robustness and drift analyses further revealed that RFR adapts better to temporal and seasonal shifts, while SVR remains relevant as an adaptive model for extreme risk analysis. Overall, this study contributes to the development of automated feature engineering, reproducible climatological forecasting pipelines, and probabilistic modeling frameworks for rainfall prediction under uncertainty in tropical regions.
Analyzing Student Sentiments and Insights on Generative AI for Independent Learning in Universities Iswari, Ni Made Satvika; Wijaya, I Nyoman Yudi Anggara; Yuniari, Ni Putu Widya
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.v7i1.1083

Abstract

Transformations in higher education brought about by Generative AI have significantly changed how university students’ access, comprehend, and develop learning materials. This study explores Indonesian university students’ perceptions and experiences regarding the use of Generative AI for independent learning, employing qualitative surveys together with sentiment analysis powered by machine learning. Data were collected from open-ended questionnaires and analyzed using four key algorithms, such as Naive Bayes, Logistic Regression, Random Forest, and Linear SVM, to classify student sentiments towards generative AI technologies. These four classical machine learning models were employed as baseline algorithms commonly used in sentiment analysis to benchmark performance on small, imbalanced educational datasets before applying more complex transformer-based methods. In addition to quantitative analysis, this study also implements thematic analysis of open-ended responses to identify prominent issues, challenges, and student recommendations concerning the use of generative AI in learning. Evaluation results identified Linear SVM as the most consistent model, with the highest weighted F1-score (0.63), although all models showed limitations in detecting negative sentiment due to class imbalance (only three negative samples out of forty responses), which affected model generalization. Key findings indicate that students perceive Generative AI as a supportive tool that accelerates understanding, creativity, and reference searching; however, they remain wary of risks related to dependency, reduced originality, and academic integrity dilemmas. This article recommends the implementation of ethical policy, AI digital literacy training, and enhancement of campus infrastructure to ensure that AI technologies enrich the learning process without compromising student independence and integrity.

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