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All Journal Jurnal Teknik Industri Rekayasa Mesin International Journal of Advances in Intelligent Informatics Journal of Mechanical Engineering Science and Technology JOIV : International Journal on Informatics Visualization Journal of Energy, Mechanical, Material and Manufacturing Engineering Quantum Teknika JTTM: Jurnal Terapan Teknik Mesin BEMAS: Jurnal Bermasyarakat
Achmad Fauzan Hery
Universitas Muhammadiyah Malang
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Pengaruh Campuran Minyak Jarak Pagar dengan Dexlite Terhadap Performa Mesin Diesel Andinusa Rahmandhika; Nur Hasanah; Rizqi Arif Viandi; Achmad Fauzan Hery Soegiharto
Quantum Teknika : Jurnal Teknik Mesin Terapan Vol. 5 No. 2 (2024): April
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jqt.v5i2.21068

Abstract

One of the alternative energy fuels in the transportation sector is biodiesel. Biodiesel made from a mixture of vegetable materials has great potential as an environmentally friendly fuel for diesel engines. This experimental research aims to analyze the effect of using Jatropha curcas (Jatropha curcas Linn) oil on diesel engines. The experiment was carried out using a mixture of Dexlite and pure castor oil (JCO) with varying compositions of JCO 0%, JCO 20%, and JCO 30%. Torque test results against load show an insignificant comparison between Dexlite without mixture and Dexlite with 20% and 30% JCO mixture, especially when loading high using Prony Brake. Although the effective engine power of the three fuel variations does not show significant differences at the same engine speed and load, the use of a mixture of Dexlite and 20% castor oil at an engine speed of 1700 rpm is more effective in improving diesel engine performance and saving fuel. Additionally, fuel consumption decreases as load increases, indicating high efficiency potential. In the context of exhaust gases, the mixture also has the potential to reduce the opacity of diesel engine exhaust gases.
Smart Early Detection of Rheumatoid Arthritis Tool on Nails with a Certainty Factor Technology Approach Based on Image Processing Octavio, Abi Mufid; Syafaah, Lailis; Vhirdausia, Nuri; Wijaya, Frenischa Yincenia; Hery Soegiharto, Achmad Fauzan; Faruq, Amrul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3252

Abstract

This study developed the Smart Early Detection Rheumatoid Arthritis (SEDRA) tool, designed to diagnose RA at an early stage by analyzing nail conditions. Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects joints, commonly in older individuals. Left untreated, RA can lead to severe complications such as pain, fatigue, paralysis, and even death. Early detection is essential to mitigate these effects. The research utilized advanced image processing techniques, MATLAB, Python, and a certainty factor approach. The experimental method involved capturing nail images, which were then processed in MATLAB to identify abnormalities associated with RA. Key nail indicators, including yellowing, brittleness, bloody splinters, textured surfaces, and jagged or perforated patterns, were validated using certainty factor technology to ensure diagnostic accuracy. The findings indicate that SEDRA effectively identifies RA through these nail features, providing accurate and timely diagnostic results. The results showed that this tool can detect Rheumatoid Arthritis disease through yellowing, brittle nails, bloody splinters, textured nails, and jagged or perforated nails. SEDRA was created to meet the needs of innovation in the health sector. SEDRA represents a breakthrough in health technology, providing a practical tool for early RA detection that can be integrated into primary healthcare systems. Its implications include improving patient outcomes by enabling early intervention and monitoring. Future research should focus on enhancing the diagnostic accuracy of SEDRA, expanding its applicability to diverse populations, and integrating it with mobile or wearable technologies to increase accessibility and usability in remote or underserved areas.
AIR CONDITIONING LOAD AND ANALYSIS CALCULATION OF AIR DISTRIBUTION IN VEHICLE CABINS BRIDGE MAINTENANCE INSPECTION Mulyono, Mulyono; Sadewa, Maulana; Soegiharto, Achmad Fauzan Hery; Restu, Firdausa Retnaning
Jurnal Rekayasa Mesin Vol. 16 No. 2 (2025)
Publisher : Jurusan Teknik Mesin, Fakultas Teknik, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jrm.v16i2.2217

Abstract

Cabin comfort is essential for an inspector to work focusly and produce valid data in a bridge maintenance vehicle. The use of an air conditioner (AC) in the cabin of this vehicle is a concern for the inspector's comfort while supervising the bridge. Choosing an inappropriate AC can result in excessive energy consumption or inadequate thermal comfort. Furthermore, the transparent windows and windshields of glass elevate the temperature and allow sunlight to invade the room. Consequently, it intensifies the cooling demand. Considering this situation, this study focuses on calculating the cooling load in the cabin and simulating the thermal performance using computational fluid dynamics (CFD). The simulations were conducted using Ansys 2023 software, with a runtime of 4 minutes and 2,400 iterations. It is to achieve a cabin temperature within the comfort range of 22–26°C. The cabin is designed for two inspectors. At an airflow velocity of 3 m/s, the simulation yielded a maximum temperature of 40°C and a minimum of 21.13°C. Reducing the airflow to 2 m/s resulted in a range of comfortable temperatures for the cabin. In conclusion, the simulation results fulfill the criteria and confirm that an airflow velocity of 2 m/s is sufficient to maintain the comfortable temperature in the cabin.
Enhancing minority class recognition in cattle monitoring: A robustness analysis of lightweight decision-level fusion Putri Nayla Sabri; Nisrina Nurhafizhah; Amrul Faruq; Achmad Fauzan Hery Soegiharto; Muhammad Ilham Perdana
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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

Abstract

Accelerometer-based monitoring has become an important approach in precision livestock farming, but achieving both high sensitivity for rare, health-relevant behaviors and computational efficiency remains challenging. Deep learning methods can address class imbalance but are often unsuitable for edge deployment due to their computational cost. This study evaluates the robustness of a lightweight decision-level fusion framework for imbalanced cattle behavior classification using tri-axial accelerometer data. To ensure rigorous evaluation, the Synthetic Minority Over-sampling Technique (SMOTE) was applied only in the training feature space to prevent data leakage. Because the dataset is strongly imbalanced, with Salt Licking (SLT) as the minority yet health-relevant class, model performance was assessed using Macro-F1 for global robustness and RecallSLT_{SLT}SLT for rare-event sensitivity. Individual tree-based models achieved strong results, with the best single model, XGBoost, obtaining 93.70% accuracy and 0.9421 Macro-F1. The proposed soft-voting fusion of Extra Trees, XGBoost, and CatBoost further improved performance to 94.21% accuracy and 0.9447 Macro-F1, with a statistically significant gain over the best single model (Wilcoxon signed-rank test, (p=0.0326). The framework also maintained strong minority-class recognition, with SLT achieving precision = 0.9571, recall = 0.9853, and PR-AUC = 0.9990. These results show that lightweight decision-level fusion can improve robustness and rare-event sensitivity without temporal deep learning, making it suitable for resource-constrained edge monitoring in livestock systems.
Co-Authors Ahmad Nur Sidiq Ali Mokhtar Amrul Faruq Andinusa Rahmandhika Andinusa Rahmandika Angga Dwi Aprilio Aprilio Arya Setyo Nugroho Daryono Daryono Daryono Elvina Anjarini Erikyatna, Mohammad Faishol Faizal Romadhi Fajar Trikusuma Wardana Laela Nursaumi Mohammad Isrok Muhammad Ilham Perdana Mulyono, M Nisrina Nurhafizhah Nur Hasanah Octavio, Abi Mufid Putri Nayla Sabri Restu, Firdausa Retnaning Reza Aulia Rahman Rizqi Arif Viandi Sadewa, Maulana Satworo Adiwidodo Selly Riansyah Sudarman Sudarman Sudarman Syafaah, Lailis Vhirdausia, Nuri Wijaya, Frenischa Yincenia Zamzami Septiropa