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All Journal Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri AMMA : Jurnal Pengabdian Masyarakat
Chairul Salam
Mining Engineering, Istanbul Technical University, Turkey
Humanities Chemistry Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Pemanfaatan Air Pit Lake Tambang Batubara Sebagai Media Tanam Kangkung Sistem Hidroponik Maharani Rindu Widara; Arrina Khanifa; Chairul Salam M
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 12 : Januari (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to evaluate the effect of using pit lake water of Mekar Jaya Village with and without the addition of alum on the growth of kale plants in hydroponics. Observations were made every three days to measure plant height, leaf length, and number of leaves per plant, the results of which were presented in tables and line diagrams. The results showed that kale plants using water with alum experienced an average height increase of 2 cm, leaf length of 1-1.5 cm, and number of leaves of 1-4 strands every three days. In contrast, plants without alum showed growth of up to 5 cm in height, 0.4-0.5 cm in leaf length, and 1-2 leaves. Measurement of total plant weight showed that the growth of kale plants without alum was better than those with alum. These findings suggest that the use of alum in pit lake water is less effective in supporting the growth of kale plants in hydroponic systems.
An Interpretable Data-Driven Framework for Smart Tunnel Boring Machine Performance Analysis and Energy–Cost Optimization Chairul Salam; Arrina Khanifa
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 1 (2026): June 2026
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.1.24-46

Abstract

Tunnel Boring Machine (TBM) operations are governed by complex and nonlinear interactions among geological variability, machine control parameters, and energy consumption, posing significant challenges for reliable performance prediction and operational optimization. Conventional empirical and physics-based approaches often struggle to capture regime-dependent behavior and parameter coupling under heterogeneous excavation conditions. To address these limitations, this study proposes an integrated and interpretable data-driven framework that combines ensemble machine learning, time-series modeling, unsupervised regime identification, multi-objective optimization, and explainable artificial intelligence within a unified analytical architecture. A multisource dataset encompassing geotechnical, operational, environmental, energy, and economic parameters was analyzed using Extreme Gradient Boosting (XGBoost), Random Forest, Gradient Boosting Regression, and recurrent neural networks. Among these, XGBoost demonstrated superior predictive capability, achieving the highest coefficient of determination and consistently lower prediction errors compared with baseline models. Unsupervised clustering identified distinct operational regimes—efficient, intermediate, and aggressive—enabling a structured evaluation of energy–cost trade-offs. Regime-aware optimization further indicated substantial potential for reducing both energy consumption and operational costs relative to high-intensity operating conditions. Sensitivity analysis using SHAP, mutual information, ANOVA, and Sobol indices revealed strong interaction effects among thrust force, torque, and rock strength parameters, highlighting the coupled nature of TBM excavation mechanics. The proposed framework extends conventional predictive modeling approaches by translating data-driven insights into interpretable, regime-based operational strategies. It provides a scalable methodological foundation for the future development of digital twin applications in TBM systems and contributes to more energy-efficient, cost-effective, and sustainable tunneling operations in complex underground environments.
Hybrid Surrogate - Physics - AI Optimization of EPB-TBM Performance: A Data-Driven Application from the Jakarta Metro Project Maharani Rindu Widara; Chairul Salam M.
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 28 No. 2 (2026): December 2026 (Pre-Print)
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.28.2.117 - 139

Abstract

The increasing complexity and geological unpredictability associated with mechanised tunnelling demand optimisation frameworks that go beyond static, offline parameter adjustments and embrace adaptive and physically coherent decision-making processes. Traditional metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimisation (PSO), and simulated annealing (SA), have been widely applied to optimise tunnel boring machine (TBM) performance. However, they are inherently limited by their lack of real-time adaptability and restricted physical interpretability. This research presents a hybrid surrogate–physics–AI optimization framework (H-SGP-BO–PINN–DRL) that redefines TBM optimization as a physics-constrained adaptive control challenge, shifting away from a static optimization model. The framework integrates a surrogate-assisted hybrid GA–PSO combined with Bayesian optimisation for a global search that considers uncertainty, a physics-informed neural network (PINN) that incorporates essential mechanical principles to ensure geotechnical feasibility, and a deep reinforcement learning (DRL) controller that enables real-time adjustments within physically feasible operational limits. The framework was validated using field data from EPB-TBM projects in the Jakarta MRT and Istanbul Metro systems, as well as synthetic datasets generated using FEM. The results show that the proposed method reduces the specific energy consumption (SEC) and improves the penetration rate (PR), while also demonstrating superior convergence stability and robustness compared to traditional metaheuristic and surrogate-based methods. Monte Carlo bootstrapping and Sobol global sensitivity analysis further identify thrust force and cutterhead rotation speed as the main factors affecting energy-performance variability. By integrating surrogate-assisted optimization, physics-informed learning, and reinforcement-based adaptability, this study introduces a new paradigm for intelligent TBM operation, enabling interpretable, energy-efficient, and deployable decision support for future smart and autonomous tunneling systems.
Co-Authors Arrina Khanifa Arrina Khanifa Maharani Rindu Widara Maharani Rindu Widara