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All Journal JTTM: Jurnal Terapan Teknik Mesin Mechanical Engineering for Society and Industry
Agus Mujianto
Universitas Muhammadiyah Kalimantan Timur, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Analysis of the effect of the number of blades on the palm frond counter tool on the counting results Fajar David Aminuddin; Anis Siti Nurrohkayati; Agus Mujianto; Hery Tri Waloyo
JTTM : Jurnal Terapan Teknik Mesin Vol 6 No 1 (2025): JTTM: Jurnal Terapan Teknik Mesin
Publisher : Teknik Mesin - Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/jttm.v6i1.740

Abstract

The growth of oil palm plantations in Indonesia, especially in Kalimantan and Sumatra, has resulted in an increase in palm oil waste from harvesting and tree care, both in the form of liquid waste and solid waste. As waste, palm fronds and leaves experience natural decomposition for about 4 months, which can cause a very large buildup and become a nest for pests to inhabit before decomposing. Designing an oil palm frond and leaf shredding machine involves designing special blades mounted on a rotating shaft, as well as a strong support structure to handle tough fiber materials, such as oil palm fronds and leaves, by producing flakes that can be used as organic fertilizer or ingredients. animal feed. The aim of this research is to create and test testing machines for chopping palm fronds and leaves showing that different blades have the ability to chop quite fine parts of the leaves with a chop length of between 20 mm and 50 mm, and the hardest part, palm fronds, can also be chopped finely. The amount of time spent is 1 minute and is able to chop 3 palm fronds. In 5 repetitions, an average time of 1 minute was obtained to chop the fronds. Based on the effective capacity of 5 repetitions on 19 blades, the average result was 1.45 kg/minute. Meanwhile, for effective capacity from 5 repetitions on 24 blades, the average result was 1.57 kg/minute and for effective capacity from 5 repetitions on 26 blades, the average result was 1.76 kg/minute
Robust SVM optimization using PSO and ACO for accurate lithium-ion battery health monitoring Mufti Reza Aulia Putra; Muhammad Nizam; Agus Mujianto; Feri Adriyanto; Henry Probo Santoso; Arif Nur Afandi; Indar Chaerah Gunadin
Mechanical Engineering for Society and Industry Vol 5 No 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/mesi.12280

Abstract

The increasing demand for reliable lithium-ion battery in various applications is focused on the need for accurate State of Health (SOH) predictions to prevent performance degradation and potential safety risks. Therefore, this research aimed to improve the accuracy of SOH prediction by integrating Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) with Support Vector Machine (SVM) to overcome the overfitting problem in traditional machine learning models. The dataset used consisted of data from 1000 cycles of lithium-ion battery, collected under laboratory conditions. Data from lithium-ion battery cycles were analyzed using optimized PSO-SVM and ACO-SVM models. These models were evaluated using Mean Square Error (MSE) and Root Mean Square Error (RMSE) metrics, showing significant improvements in prediction accuracy and model generalization. The results showed that although both optimized models were superior to the baseline SVM, PSO-SVM had higher generalization performance during testing. The higher performance was due to the effective balance between exploring the search space and exploiting optimal solutions, making it more suitable for real-world applications. In comparison, ACO-SVM showed superior performance in training data accuracy but was more prone to overfitting, suggesting the potential for scenarios prioritizing high training accuracy. These results could be applied to extend the lifespan of lithium-ion battery, contributing to enhanced reliability and cost-effectiveness in applications.
Co-Authors Anis Siti Nurrohkayati Arif Nur Afandi Fajar David Aminuddin Feri Adriyanto Henry Probo Santoso Hery Tri Waloyo Indar Chaerah Gunadin Mufti Reza Aulia Putra Muhammad Nizam