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All Journal TEKNIK Jurnal Locus Penelitian dan Pengabdian Eduvest - Journal of Universal Studies
Moh. Djaeni
Universitas Diponegoro
Aerospace Engineering Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Mathematics Neuroscience Social Sciences

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Performance Evaluation and Evaporation Loss Prediction in Counter Flowinduced Draft Geothermal Wet Cooling Tower Type Through Computational Fluid Dynamics (CFD) Simulation Mukhamad Nashir; Moh. Djaeni; Muchammad Muchammad
Eduvest - Journal of Universal Studies Vol. 6 No. 1 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i1.52564

Abstract

Cooling towers are critical components in geothermal power generation systems, playing a vital role in maintaining thermal efficiency and managing water resources. This study investigates the thermal performance and water loss due to evaporation in an induced draft counter-flow cooling tower using a modeling and simulation approach based on Computational Fluid Dynamics (CFD). Validation of the simulation results against actual data demonstrated high accuracy, with an error margin of 1.8%, indicating that CFD is a reliable method for analyzing and optimizing cooling tower design Simulation results show that increasing the hot water inlet temperature from 35°C to 49°C leads to a rise in evaporation loss from 5.0 kg/s to 13.0 kg/s (CFD), while the ASHRAE method yields higher values, ranging from 5.5 kg/s to 14.5 kg/s. For variations in hot water mass flow rate (423–845 kg/s), the ASHRAE method exhibits a linear increase in evaporation loss, whereas CFD results remain relatively stable. Additionally, increasing the hot water mass flow rate causes the cold-water outlet temperature to decrease from 21°C to 11°C, accompanied by a reduction in system effectiveness from 92% to 86%. Furthermore, increasing the cold air inlet velocity from 3.5 m/s to 6.5 m/s raises the evaporation loss from 14.5 kg/s to 16.0 kg/s (CFD) and significantly enhances system effectiveness from 11% to 91%. Overall, the findings demonstrate that CFD simulations provide realistic performance estimations compared to empirical methods, particularly under dynamic operating conditions.
The Role of Algae in Biofuel Production: Potentials and Challenges for Sustainable Transportation Dessy Agustina Sari; Moh. Djaeni; Hadiyanto Hadiyanto; Aji Prasetyaningrum
TEKNIK Vol 46, No 1 (2025)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/teknik.v46i1.67532

Abstract

This study aims to explore the potential of algae for sustainable biofuel production by examining their molecular biology and the use of advanced cultivation techniques. As concerns over greenhouse gas emissions and rising transportation energy costs grow, algae offer a promising alternative for fuel derived from both food and non-food sources. This review looks at the main biological pathways involved in making biofuels from algae. It focuses on species diversity, lipid content, and new technologies like photobioreactors and magnetic nanoparticle harvesting. The results showcase noteworthy advancements in biotechnology and genetic engineering that boost algae productivity and fuel yield, while also critically examining the environmental impacts such as CO2 emissions and water use, as well as the economic and policy challenges through a life cycle analysis. Adopting a global perspective, this review emphasizes the role of international collaboration and technology transfer in overcoming barriers. Conclusion: Algae-based biofuels hold considerable potential for reducing CO2 and supporting sustainable transportation, yet scaling up production and lowering costs remain challenges. Future research should focus on improving integrated biorefinery platforms, exploring CO2 capture, and promoting international partnerships.
Computer Vision-Based Chili Pepper Dryness Classification Using Lightweight CNN Models for Affordable Post-Harvest Sorting Tri Raharjo Yudantoro; Moh. Djaeni; R. Rizal Isnanto; Prayitno Prayitno; Z. N. Novita Sari
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 7 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i7.5975

Abstract

The manual grading of chili pepper dryness is uneven because human graders tend to perceive color, texture, and form changes that occur gradually during drying in a subjective way. The purpose of this study is to develop a lightweight convolutional neural network model that can effectively balance classification accuracy, validation stability, and deployment feasibility for inexpensive post-harvest sorting. A controlled visual dataset of 1,662 photos of red chili pepper from 32 samples at eleven drying times was gathered and classified into Fresh, Medium, and Dry classes. We assessed MobileNetV2, NASNetMobile, and InceptionV3 using the same pre-processing, augmentation, and hold-out testing protocol, along with additional robustness analysis. MobileNetV2 achieved the best hold-out performance with 93% accuracy, 93% precision, 92% recall, and 92% F1-score, while having fewer parameters and lower computational cost than NASNetMobile and InceptionV3. The class-wise analysis showed that the greatest errors were found between the Fresh–Medium and Medium–Dry boundaries, as the visual transition of chili dryness is gradual. MobileNetV2 is the most suitable baseline for low-cost camera-based chili pepper dryness sorting, and this study provides an evidence-based standard for post-harvest visual inspection using compact deep learning.
Co-Authors Aji Prasetyaningrum Dessy Agustina Sari H Hadiyanto Muchammad Muchammad Mukhamad Nashir Prayitno R Rizal Isnanto Tri Raharjo Yudantoro Z. N. Novita Sari