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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Go-Integratif : Jurnal Teknik Sistem dan Industri
Safitri, Tari Hardiani
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Analisis Tata Letak Produksi CNC Batik dengan Group Technology dan Particle Swarm Optimization Fauzi, Rifqi; Hartanti, Sri; Safitri, Tari Hardiani; Rifai, Achmad Pratama; Saifurrahman, Anas
Go-Integratif : Jurnal Teknik Sistem dan Industri Vol. 4 No. 02 (2023): Go-Integratif : Jurnal Teknik Sistem dan Industri
Publisher : Engineering Faculty at Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35261/gijtsi.v4i02.10950

Abstract

The production of batik experiences an increasing demand every year. This requires an improvement in both productivity and flexibility in the batik production process. Layout becomes one of the crucial factors in enhancing productivity and flexibility since it influences the material handling process. Well-designed facility layouts can improve the smoothness of material transfer operations, reducing material handling distances and, consequently, lowering material handling costs. This research aims to provide the best alternative layout. Several stages are carried out in this research, including classification to group cells based on part family, using the Rank Order Clustering (ROC) and Similarity Coefficient (SC) methods. Next, the calculation of the shortest transfer distance is conducted using Distance-Based Score Calculation, along with the use of Particle Swarm Optimization (PSO) to determine the department sequence. The research results show that the same cell grouping is achieved using both ROC and SC methods, with Cell 1 {Spray paint, proxy paint, Compressor, Welding, Grinding}, and Cell 2 {CNC Milling and CNC Turning}. Based on the distance traveled problem, it is found that the travel distance on the existing intuitive layout and the new suggested flow is 315.75 and 292.96, respectively. Therefore, by implementing group technology to implement cell manufacturing, it can provide a more effective and efficient material flow and Work In Process (WIP). Meanwhile, the results PSO yields a total minimum travel distance of 7,701.851 meters and a total material handling cost of Rp. 220 per meter, resulting in a total material handling cost of Rp 1,694,407.
Classification of single origin Indonesian coffee beans using convolutional neural network Rifai, Achmad Pratama; Sari, Wangi Pandan; Rabbani, Haidar; Safitri, Tari Hardiani; Hajad, Makbul; Sutoyo, Edi; Nguyen, Huu-Tho
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5140-5156

Abstract

This research aims to develop a coffee bean type detection model using convolutional neural networks (CNN), leveraging a dataset of 14,525 images from 116 types of Indonesian coffee beans. Pre-processing steps including resizing, rescaling, and augmentation were applied to improve the dataset quality. The dataset was split into training, validation, and testing sets with proportions of 80%, 10%, and 10%, respectively. Two model development approaches were used: transfer learning with Inception V3 in two scenarios and a model built from scratch. The transfer learning Inception V3 model in scenario 1 achieved the best performance, with a test accuracy of 0.87 and optimal evaluation metrics across precision, recall, and F1-score. This model was fine-tuned using pretrained weights, allowing it to adapt effectively to the coffee bean dataset. The results highlight that transfer learning, especially with Inception V3, provides a robust method for classifying coffee beans, offering potential applications in the coffee industry for improving classification efficiency and accuracy. The study demonstrates how deep learning can enhance the objectivity and precision of coffee bean classification, contributing to greater consistency in product sorting and quality assessment.
Co-Authors Achmad Pratama Rifai Fauzi, Rifqi Hajad, Makbul Hartanti, Sri Nguyen, Huu-Tho Rabbani, Haidar Saifurrahman, Anas Sutoyo, Edi Wangi Pandan Sari