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p-Index From 2021 - 2026
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All Journal International Journal of Hydrological and Environmental for Sustainability
Ignatius Adi Prabowo
Built Environment and Surveying, Universiti Teknologi Malaysia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Environmental Science

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Waste Sorting Machine Automatic of Organic and Inorganic Using Arduino Mega as Microcontroller : Implication for Environmental Sustainability Bernadeta Wuri Harini; Benediktus Yudha Edy; Agustinus Surya Haryanto; Martanto Martanto; Petrus Setyo Prabowo; Ignatius Adi Prabowo
International Journal of Hydrological and Environmental for Sustainability Vol. 3 No. 2 (2024): International Journal of Hydrological and Environmental for Sustainability
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/ijhes.v3i2.449

Abstract

Increasing waste production worldwide has become a serious environmental and social problem. Waste thrown into traditional landfills can pollute land and water, cause public health problems, and produce greenhouse gas emissions.  To overcome the issues above, in this research, a waste sorting machine will be designed that can sort organic and inorganic waste. This sorting is done automatically, a person only needs to put the waste into the machine, and then the waste will be separated according to its category.  The organic and inorganic waste sorting system consists of 5 sub-systems.  They are metal waste, glass waste, dry waste, plastic waste, and wet waste sorting.  The results of this study can produce a success rate of 66.7% in detecting the occupancy of the trash bin, 70% in sorting metal waste, and 44.4% in sorting glass waste.  The success rate in sorting wet waste is 40%, while in sorting plastic waste is 70%, and in dry waste is 90%.
Geochemical Characteristics of B-Li-Cl Type Waters in Geothermal Area: Implications for the Origin of Tawau Hot Springs, Sabah, Malaysia Fredolin Javino; Ignatius Adi Prabowo; Mohd Rashidi Abdull Manap; Norizah Abdul Rahman
International Journal of Hydrological and Environmental for Sustainability Vol. 3 No. 3 (2024): International Journal of Hydrological and Environmental for Sustainability
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/ijhes.v3i3.511

Abstract

Boron (B), Lithium (L), and Chlorine (Cl) are valuable indicators in geothermal detection due to their unique properties and behavior in hydrothermal systems. Volcanic hot springs are generally believed to originate from meteoric circulation or buried seawater and are controlled by equilibrium exchange with magmatic rocks at high temperatures. In this study, we report the B-Li-Cl geochemical characteristics of Tawau hot springs, in the forearc region of Malaysia. The data has been collected from previous studies that analyzed 8 water samples to determine the levels of 10 dissolved elements or components. We performed data correlation analyses to infer the source materials and origins of the hot springs. In addition, we performed numerical modeling of oxygen and hydrogen isotope fractionation to examine the composition of derived fluids as possible candidates of geofluids. The results suggest that Tawau geothermal originated from deep seawater due to subduction before undergoing magmatisation and alteration processes. This interpretation result has a positive correlation with Li and boron. In addition, the geological conditions in the Sabah region, which has subduction zones from two directions, cause a high probability of seawater or marine sediment contribution into the reservoir before finally coming out in the form of a geothermal fluid phase.
Machine-Learning-Based Vibration Classification of Inter-Module Connector Stiffness Loss in Modular Flood-Response Structures: A Simulation-Based Proof of Concept Sely Novita Sari; Fitri Nugraheni; Bagus Gilang Pratama; Ignatius Adi Prabowo
International Journal of Hydrological and Environmental for Sustainability Vol. 5 No. 3 (2026): International Journal of Hydrological and Environmental for Sustainability
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/ijhes.v5i3.1456

Abstract

Ensuring the safety of modular facilities used for flood response and post-disaster sheltering requires reliable assessment of inter-module connections that may deteriorate under hydrodynamic loading, debris impact, repeated wetting, and corrosion. This study presents a simulation-based proof of concept for classifying connector residual stiffness from vibration response using machine learning. Flood-load analysis quantified hydrostatic, hydrodynamic, and debris-impact forces under five severity scenarios, which were hypothetically associated with seven prescribed connector-stiffness levels ranging from 100% to 5%. Only the stiffness-to-vibration relationship was physically simulated; the flood-exposure-to-stiffness relationship was assumed rather than experimentally validated. Finite element modal analysis in OpenSeesPy provided the first four natural frequencies and mode shapes, from which sixteen diagnostic features were derived. Gaussian perturbations generated 350 synthetic realizations from the seven deterministic stiffness states. To minimize data leakage, classifiers were evaluated using block-grouped cross-validation and compared with majority-class and second-mode-frequency baselines. Random Forest achieved the best multiclass performance, with a macro-F1 of 0.62 and balanced accuracy of 0.63, although early-stage deterioration classes remained difficult to distinguish. When reformulated as a binary severe/non-severe screening task, the model achieved 0.94 balanced accuracy with a 0.10 false-safe rate. These results demonstrate within-configuration separability under synthetic noise rather than field generalization. The proposed framework may support rapid condition screening of modular connections, but experimental validation of the degradation mapping, structural model, and sensing strategy is required before field implementation.
Co-Authors Agustinus Surya Haryanto Bagus Gilang Pratama Benediktus Yudha Edy Bernadeta Wuri Harini Fitri Nugraheni Fredolin Javino Martanto Martanto Mohd Rashidi Abdull Manap Norizah Abdul Rahman Petrus Setyo Prabowo Sely Novita Sari