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Manufaktur: Publikasi Sub Rumpun Ilmu Keteknikan Industri
Published by Asosiasi Riset Ilmu Teknik Indonesia
ISSN : 30314992     EISSN : 30313996     DOI : 10.61132
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering
1. Mechanical Engineering (and Other Mechanical Sciences) 2. Production Engineering (and or Manufacturing) 3. Chemical Engineering 4. Pharmaceutical (Industry) Engineering 5. Industrial Engineering 6. Aviation/Aeronautics and Astronautics 7. Textile Engineering (Textile) 8. Refrigeration Engineering 9. Biotechnology in Industry 10. Nuclear Engineering (and Or Other Nuclear Sciences) 11. Engineering Physics 12. Energy Engineering 13. Remote Sensing 14. Materials Engineering (Materials Science) 15. Other Industrial Engineering Fields That Have Not Been Listed
Arjuna Subject : Teknik (semua kategori) - Tenik Industri dan Manufaktur
Articles 94 Documents
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Mitigasi Food Loss pada Rantai Pasok Kakao: Analisis Risiko Menggunakan FMEA dan AHP di Sulawesi Selatan Nurul Chairany; Oktavianti Mariana; Yan Herdiansyah; Lastri Wiyani
Manufaktur: Publikasi Sub Rumpun Ilmu Keteknikan Industri Vol. 4 No. 2 (2026): Manufaktur : Publikasi Sub Rumpun Ilmu
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/manufaktur.v4i2.1553

Abstract

Cocoa is one of Indonesia’s leading plantation commodities and contributes substantially to the national economy. However, along its supply chain from cultivation to trade, cocoa frequently experiences Food Loss that reduces quality, lowers farmers’ income, and creates logistical inefficiencies. This study identifies the causes of Food Loss risk in the cocoa supply chain in South Sulawesi using Failure Mode and Effect Analysis (FMEA) and determines appropriate mitigation strategies using the Analytical Hierarchy Process (AHP). Data were collected from farmers, collectors, and traders through interviews and questionnaires. FMEA was used to evaluate the severity, occurrence, and detection of potential risks to obtain a Risk Priority Number (RPN) for each failure mode, while AHP prioritized mitigation strategies. The results show that the highest risk for farmers is failing to meet bean quality standards (RPN 567), for collectors is high moisture content (RPN 315), and for traders is price fluctuation (RPN 448). The most effective strategies are intensive training and direct mentoring for farmers (weight 0.664), mechanical drying for collectors (0.729), and long-term contracts for traders (0.682). Integrating FMEA and AHP offers a structured approach to prioritizing Food Loss risks and formulating strategic recommendations for stakeholders.
Aplikasi Metode JONSWAP untuk Pengukuran Tinggi dan Periode Gelombang di Teluk Tomini, Kabupaten Poso, Tahun 2025 Riwan Fridolin Kelo
Manufaktur: Publikasi Sub Rumpun Ilmu Keteknikan Industri Vol. 4 No. 2 (2026): Manufaktur : Publikasi Sub Rumpun Ilmu
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/manufaktur.v4i2.1563

Abstract

This study aimed to analyze wave height and wave period in Tomini Bay, Poso Regency, using the Joint North Sea Wave Project (JONSWAP) method. The data used consisted of wind direction and wind speed data for February 2025 obtained from the BMKG Kasiguncu Meteorological Station, Poso. The research stages included wind rose analysis, effective fetch calculation, wind speed conversion, and wave height and period analysis using the JONSWAP method. The results showed that the dominant wind direction came from the north with a percentage of approximately 71% and a maximum wind speed of 11.66 knots. The effective fetch length obtained was 205.05 km. The JONSWAP calculation results indicated wave heights ranging from 0.32 m to 1.60 m with wave periods between 3.61 seconds and 7.37 seconds. Meanwhile, direct field measurements showed an average wave height of 0.12 m with a period of 2.59 seconds. The difference in results occurred because Tomini Bay is a semi-enclosed water area that reduces wave energy before reaching the coast.
Analisis Prioritas dalam Sistem Pemeliharaan Crane Zoomlion 55 Ton Menggunakan Metode Risk Priority Number Muhammad Dwi Yulianto; Mulyadi Mulyadi
Manufaktur: Publikasi Sub Rumpun Ilmu Keteknikan Industri Vol. 4 No. 2 (2026): Manufaktur : Publikasi Sub Rumpun Ilmu
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/manufaktur.v4i2.1594

Abstract

This study aims to analyze risk priorities in the maintenance system of the Zoomlion 55-ton crane using the Failure Mode and Effects Analysis (FMEA) and Risk Priority Number (RPN) methods. Cranes are vital equipment in the construction and manufacturing sectors, so component failure can have serious impacts on work safety, operational smoothness, and downtime costs. The research method was carried out through identification of the main components of the crane, field observations, collection of historical damage data, and interviews with experienced technicians. Each failure mode was analyzed based on the parameters of Severity (S), Occurrence (O), and Detection (D) to obtain the RPN value as a basis for determining risk priorities. The results showed that the Main Hoist Motor, Brake System, Hydraulic System, and Hoisting Motor components had the highest RPN values and were included in the critical risk category. Failure in these components has the potential to cause work accidents or sudden stoppage of crane operations. The implementation of mitigation measures in the form of preventive maintenance, sensor-based predictive maintenance, non-destructive test (NDT) inspection, and improvement of lubrication quality was proven to be able to reduce the RPN value significantly, with a percentage reduction ranging from 55% to 75%. This study concludes that the implementation of risk-based maintenance using FMEA and RPN methods is effective in improving the reliability, safety, and efficiency of the Zoomlion 55-ton crane maintenance system.
Klasifikasi Motif Ulos Batak Toba Menggunakan Convolutional Neural Network Berbasis Segmentasi Mask R-CNN Chiki Dwi Putri Sibarani; I Nyoman Saputra Wahyu Wijaya; Ni Putu Novita Puspa Dewi
Manufaktur: Publikasi Sub Rumpun Ilmu Keteknikan Industri Vol. 4 No. 2 (2026): Manufaktur : Publikasi Sub Rumpun Ilmu
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/manufaktur.v4i2.1604

Abstract

This study aims to examine the extent to which segmentation can improve the classification accuracy of Batak Toba Ulos motifs and to compare the effectiveness of three Convolutional Neural Network (CNN) architectures, namely VGG16, Inception-V3, and MobileNetV3, in classifying the segmentation results. The study is motivated by the high similarity of patterns, colors, and textures among ulos motifs, as well as visual noise from background, lighting, and shadows that reduce classification accuracy. The method consists of two main stages, segmentation and classification. Segmentation begins with manual polygon annotation using VGG Image Annotator (VIA), converted into COCO format as ground truth to train a Mask R-CNN model, which then separates the motif area from the background, producing a Region of Interest (ROI) as input for classification. The dataset consists of 700 images of seven types of Batak Toba ulos obtained through direct image acquisition using a smartphone camera. Evaluation used the mean Average Precision (mAP) metric for segmentation, and accuracy, precision, recall, and F1-Score for classification. The results show that Mask R-CNN segmentation is effective, achieving a Mean IoU of 0.9062, a Bounding Box AP of 0.9228, and a Segmentation AP of 0.8808. In classification, all three CNN architectures achieved accuracy above 98%, with VGG16 and MobileNetV3 reaching the highest accuracy of 99.71%, while Inception-V3 achieved 98.57%. In terms of computational efficiency, MobileNetV3 is the most recommended architecture, as it matches VGG16's accuracy with far fewer parameters and a shorter training time.

Page 10 of 10 | Total Record : 94

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