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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Deshinta Arrova Dewi
Center for Data Science and Sustainable Technologies, INTI International University, Malaysia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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PREDICTIVE MODELLING OF CLEAN WATER SUPPLY IN RIAU PROVINCE: A DEEP LEARNING APPROACH Agustin Agustin; Junadhi Junadhi; Lusiana Efrizoni; Deshinta Arrova Dewi; Abhishek Saxena
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2447-2460

Abstract

The supply of clean water remains a critical issue in many regions, including Riau Province, where factors such as population growth and climate variability significantly affect its availability and distribution. This study aims to develop a time-series–based predictive model for clean water supply in Riau Province using deep learning approaches. Using historical data from 2019 to 2023, including variables such as the number of customers, water volume, economic value, and input costs, this research identifies temporal patterns to support proactive water resource management. The methodology consists of exploratory data analysis, data preprocessing, and model training using several architectures, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN). Among these models, the LSTM achieved the best performance, with a Mean Absolute Error (MAE) of 1.25, a Mean Squared Error (MSE) of 2.56, and an R-squared (R²) of 0.92. After hyperparameter optimization, further improvements in predictive accuracy were obtained. Based on the optimized LSTM predictive model, the forecasted clean water volume for 2024 is 19,496.90 thousand m³, a slight decline from the previous year. The novelty of this study lies in the comprehensive comparison of multiple deep learning architectures for regional-scale clean water time-series forecasting and the optimized implementation of LSTM for operational prediction. In practical terms, the results can support local water authorities in improving planning, infrastructure development, and demand management strategies. However, this study is limited by the use of secondary data from a single province and a relatively short observation period, which may affect the model's generalizability. The proposed predictive framework can serve as a reference for future studies in sustainable water resource management.
EXTRACTIVE CLINICAL NOTES SUMMARIZATION USING SINGLE MACHINE LEARNING, ENSEMBLE, AND STACKING APPROACHES Junadhi Junadhi; Agustin Agustin; Deshinta Arrova Dewi; Abhishek Saxena
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2461-2474

Abstract

Summarizing clinical notes is pivotal to supporting medical decision-making by presenting relevant information concisely and efficiently. However, the complexity of clinical language, the unstructured nature of the text, and the inherent class imbalance pose major challenges for the development of automatic summarization systems. This study develops a framework for extractive clinical notes summarization and compares the performance of single-model machine learning, simple ensembles, and stacking. A synthetic dataset comprising 2,000 clinical notes was segmented into 22,000 sentences, each labeled as important or not important according to a reference extractive summary. The methodology includes text preprocessing (normalization, expansion of medical abbreviations, tokenization, and stopword removal), feature extraction (TF-IDF, Named Entity Recognition, and structural features), and implementation of multiple models. Evaluation relies on Accuracy, Precision, Recall, and F1-score, complemented by Entity-F1, redundancy analysis, and latency per document. Experimental results show that the best single model, XGBoost, achieves an F1-score of 0.76, reflecting its ability to capture non-linear interactions among heterogeneous clinical text features under class imbalance, while simple ensembles further improve performance to 0.78. The most substantial gains are obtained with stacking, which reaches an F1-score of 0.80, precision of 0.83, and recall of 0.78. The confusion matrix indicates low false negatives, and the Precision–Recall curve (AP = 0.73) demonstrates consistent behavior under imbalanced data conditions. Overall, the findings establish stacking as the most effective approach for extractive summarization of clinical notes. Beyond theoretical relevance, the results carry practical implications for developing clinical decision support systems that are safe, efficient, and readily integrable into digital health services.
MULTI-OBJECTIVE MIXED-INTEGER PROGRAMMING MODEL WITH BATTERY AND CHARGING CONSTRAINTS FOR ELECTRIC FEEDER BUS NETWORKS Rini Yanti; Parlindungan Kudadiri; Eka Setia Novi; Febria Marta Siska; Deshinta Arrova Dewi; R. Raja Subramanian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2475-2490

Abstract

The deployment of electric vehicle (EV)–based feeder bus networks is increasingly promoted to support sustainable urban transportation systems. However, their operational planning is challenged by limited battery capacity, charging time requirements, and restricted charging infrastructure, which introduce complex trade-offs between operational efficiency, energy consumption, and service coverage. This study aims to develop a Multi-Objective Mixed-Integer Programming (MOMIP) model that explicitly incorporates battery state-of-charge dynamics and charging station constraints for optimizing electric feeder bus networks. The proposed model simultaneously minimizes operational costs and total energy consumption while maximizing service coverage, enabling a comprehensive evaluation of conflicting operational objectives. The use of MOMIP is justified by the need to capture Pareto-optimal trade-offs among these competing objectives within a unified mathematical formulation. Numerical experiments based on hypothetical operational scenarios demonstrate that the model generates feasible Pareto-optimal solutions, revealing clear trade-offs between cost efficiency, energy usage, and network accessibility. Analysis further indicates that increasing charging capacity significantly enhances system performance, reducing energy consumption by more than 20% and improving service coverage by over 7 percentage points. The proposed model provides a robust decision-support tool for transport planners and contributes to the development of energy-efficient and sustainable electric feeder bus operations.
Co-Authors Abhishek Saxena Abhishek Saxena Agustin Agustin Agustin Agustin Eka Setia Novi Febria Marta Siska Junadhi Junadhi Junadhi Junadhi Lusiana Efrizoni Parlindungan Kudadiri R. Raja Subramanian Rini Yanti