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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Teknik Informatika (JUTIF)
Lusiana Efrizoni
Informatics Engineering, Universitas Sains dan Teknologi Indonesia, Indonesia
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.
Health Index Modelling of Turbofan Engines Using Residual Dilated Convolutional Neural Networks for Predictive Maintenance Alfia Nurlaili Tahiyat; Lusiana Efrizoni; Triyani Arita Fitri; Susanti Susanti
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5636

Abstract

Data-driven prognostics and health management (PHM) for turbofan engines requires a Health Index (HI) that is learnable from multivariate telemetry and credible as a basis for maintenance decisions. This study presents a deep learning-based HI modelling framework on the N-CMAPSS benchmark that converts operating conditions and sensor streams into a bounded HI and, subsequently, into decision-oriented outputs for predictive maintenance. A baseline convolutional model is benchmarked against a residual dilated CNN to capture multi-scale degradation signatures from fixed-length temporal windows. To preserve evaluative integrity, health-zone thresholds are calibrated on validation predictions and then fixed, producing a three-zone taxonomy (critical, warning, healthy) for rapid field triage, alongside a continuous risk score that induces a rank-ordered maintenance priority list from most critical to most healthy. The selected model achieves HI regression performance of RMSE = 0.1266, MAE = 0.0720, and R² = 0.7241, while the calibrated zone mapping attains accuracy = 0.8688 and macro-F1 = 0.6124. The main contribution is a leakage-aware, decision-coupled pipeline that delivers both interpretable health zoning and risk-ranked prioritization, strengthening the operational linkage between predictive modelling and maintenance triage within PHM-oriented Informatics.
Co-Authors Abhishek Saxena Agustin Agustin Alfia Nurlaili Tahiyat Deshinta Arrova Dewi Junadhi Junadhi Susanti Susanti Triyani Arita Fitri