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All Journal Jurnal Rekayasa Mesin Jurnal Teknik Mesin Indonesia
Sunardi
Universitas Muhammadiyah Yogyakarta
Aerospace Engineering Automotive Engineering Control & Systems Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Field-scale evaluation of a solar-powered double-nozzle venturi aeration system for water quality management in tilapia cage culture Thoharudin; Sunardi; Fitroh A.K. Yudha; Muhammad Nadjib; Aqiel Z. A. Pudjianto
Jurnal Teknik Mesin Indonesia Vol 21 No 1 (2026): April
Publisher : BKS-TM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71452/jtmi2112026114

Abstract

Freshwater aquaculture requires efficient and affordable aeration to maintain water quality, especially in stagnant or low-circulation waters where dissolved oxygen (DO) depletion can reduce fish survival and productivity. This study evaluated the field-scale performance of a solar-powered double-nozzle Venturi bubble generator for nighttime aeration in a large tilapia floating cage. The system was installed in an 11 m × 17 m cage containing about 6000 tilapia and used eight double-nozzle Venturi aerators driven by a 12 V DC pump with an off-grid photovoltaic-battery supply. It operated intermittently from 00:30 to 08:30 using a 30 min ON/30 min OFF cycle, equivalent to 4 h day⁻¹ of effective operation. Each unit produced an average water flow of 6.73 L min⁻¹ and air entrainment of 1.12 L min⁻¹, while the pump consumed 63.12 W. A minimum battery capacity of 449.28 Ah was required and met by a 500 Ah battery bank, while the photovoltaic system was adequately sized for local solar conditions. Field results showed stable pH (6.7–7.3), while DO became the main limiting factor. Aerated water maintained 4.5 mg L⁻¹ DO compared with 3.8 mg L⁻¹ in non-aerated water, confirming the system’s feasibility and effectiveness for off-grid tilapia cage culture.
Clustering-Based Analysis of Fuel Efficiency and Emissions in Automotive Data Using PCA and K-Means Sunardi; Ananda Nur Daffa Zain
Jurnal Rekayasa Mesin Vol. 20 No. 2 (2025): Volume 20, Nomor 2, August 2025
Publisher : Mechanical Engineering Department - Semarang State Polytechnic

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/jrm.v20i2.6709

Abstract

Growing concerns regarding greenhouse gas emissions and fuel consumption have placed considerable demands on the automotive sector. To address these issues, this research applies unsupervised learning approaches namely Principal Component Analysis (PCA) and K-Means Clustering to categorize vehicles based on attributes associated with energy efficiency and environmental impact. Using a publicly available vehicle dataset, PCA was used to simplify the data by reducing dimensionality while preserving significant patterns. Subsequently, K-Means was employed to segment the data into three distinct clusters according to shared features like engine size, fuel usage, and CO₂ output. The resulting groupings effectively identified categories such as fuel-efficient, moderately consuming, and high-consumption vehicles. Visual representation in two-dimensional space further confirmed meaningful distinctions among the clusters, offering practical insights for both manufacturers and consumers.
Co-Authors Ananda Nur Daffa Zain Aqiel Z. A. Pudjianto Fitroh A.K. Yudha Muhammad Nadjib Thoharudin