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p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Quantitative Research and Modeling International Journal of Ethno-Sciences and Education Research
Muhammad Rizky Aulia
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Transportation Other

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Ethnoscience-Based Project Learning Rooted in Sundanese Indigenous Ecological Knowledge: Effects on Environmental Awareness, Environmental Responsibility, Scientific Literacy, and Sustainability Awareness among Junior High School Students Muhammad Rizky Aulia; Dhika Surya; Jumadil Saputra
International Journal of Ethno-Sciences and Education Research Vol. 6 No. 3 (2026): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v6i3.1355

Abstract

Science instruction in many Indonesian junior high schools remains largely decontextualized from students' own cultural surroundings, a condition that has been associated with persistently low environmental literacy and a weak sense of ecological responsibility among adolescents. Ethnoscience-based learning offers a way to reconnect formal science with indigenous ecological knowledge, yet most existing studies have used it mainly to raise scientific literacy or as descriptive documentation of local wisdom, rather than as a live, field-based project learning experience whose effect is tested simultaneously across environmental and scientific outcomes. This study proposes and evaluates an Ethnoscience-Based Project Learning (EBPL) design that embeds the indigenous ecological knowledge of the Sundanese customary community of Kampung Kuta, Ciamis Regency, West Java — including its sacred-forest conservation system (Leuweung Gede), communal water-source governance, palm-sugar agroforestry, and home-garden medicinal-plant practices — into a science unit on environmental pollution and ecosystem sustainability. A quasi-experimental nonequivalent control-group design was used, involving eighth-grade students assigned to an experimental group (EBPL) and a control group (conventional project-based learning without explicit ethnoscience content). Four constructs were measured before and after the six-week intervention: environmental awareness, environmental responsibility, scientific literacy, and sustainability awareness. [Results to be completed after the study is conducted]. The study is expected to show that grounding project-based learning in a specific, living indigenous knowledge system produces larger gains than generic project-based learning, and it argues that ethnoscience-based project learning can simultaneously serve science-literacy goals and the preservation of endangered local ecological wisdom. Implications for science curriculum design under Indonesia's Merdeka Curriculum, particularly the Projek Penguatan Profil Pelajar Pancasila (P5), are discussed.
Quantitative Risk Management Modeling for Automotive Component Supply Chains Using the FMEA (Failure Mode and Effects Analysis) Method Muhammad Rizky Aulia; Fahmi Sidiq
International Journal of Quantitative Research and Modeling Vol. 7 No. 3 (2026): International Journal of Quantitative Research and Modeling (IJQRM)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i3.1448

Abstract

Automotive component supply chains are exposed to interconnected disruptions arising from supplier dependence, demand variability, production coordination, logistics, information exchange, and external economic conditions. This study develops a quantitative risk-prioritization framework using Failure Mode and Effects Analysis (FMEA) to identify critical failure modes and evaluate the robustness of their priorities within an automotive component supply chain. The analysis integrates supply-chain process mapping, systematic failure-mode identification, expert validation, multi-expert scoring, Risk Priority Number (RPN) calculation, domain-level aggregation, and Monte Carlo sensitivity analysis. Eight cross-functional experts assessed 27 failure modes across nine supply-chain domains using standardized Severity, Occurrence, and Detection scales. After data screening, 212 valid ratings were retained. The aggregated results produced a mean RPN of 144.24, with Severity showing the highest mean score (6.60), followed by Detection (5.35) and Occurrence (4.20). Demand fluctuation recorded the highest RPN (233.38), followed by delayed supplier delivery (230.73) and nonconforming supplier components (226.82). Supplier risk also exhibited the highest domain-level mean RPN (201.45). Monte Carlo sensitivity analysis demonstrated strong ranking stability, with a mean Spearman correlation of 0.961 across 1,000 iterations; the ten baseline top-ranked failure modes all exceeded the 0.70 robustness threshold. The findings establish a supported mitigation agenda spanning demand forecasting, supplier diversification, quality assurance, preventive maintenance, financial hedging, logistics flexibility, information-system integration, and procurement planning. The framework strengthens proactive risk governance by linking failure-mode prioritization with robustness assessment and targeted intervention.
Co-Authors Dhika Surya Fahmi Sidiq Jumadil Saputra