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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Engineering Science Letter Bincang Sains dan Teknologi
Nguyen, Huu-Tho
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Transportation

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Development of a Hybrid Decision Support System for Selecting the Air Filters Based on Multiple Criteria Nguyen, Thi Thao Uyen; Nguyen, Vu Anh Duy; Nguyen, Huu-Tho; Ghazali, Ihwan
Engineering Science Letter Vol. 3 No. 02 (2024): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/esl.v3i02.553

Abstract

To select an air purifier that fully meets the needs of consumers, this study was conducted to provide the most objective and effective suggestions in making decisions and evaluating different smart purifier options that suit the tastes of users. Such decision-making is complicated for non-professionals when there are countless large and small brands on the market today with many different prices, features, and incentives. In such complex situations, decision-making with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) can be used to eliminate risk and to better represent the preferences of decision-makers. From modeling to using the Entropy method and the TOPSIS method, all are done to serve the ultimate goal of choosing a good air purifier that meets the needs of customers.
Trends of Unmanned Aerial Vehicles in Logistics Delivery Nguyen, Huu-Tho; Ghazali, Ihwan
Bincang Sains dan Teknologi Vol. 3 No. 02 (2024): Bincang Sains dan Teknologi
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/bst.v3i02.600

Abstract

The integration of unmanned aerial vehicles (UAVs) in logistics operations has attracted significant attention due to their potential to revolutionize delivery processes. This abstract provides an overview of the trends and advancements in utilizing UAVs for logistics. The study explores the current research landscape, identifies key challenges, and offers insights into the future applications of UAVs in logistics. By analyzing a range of scholarly articles, this abstract aims to illuminate the evolving role of UAVs in optimizing delivery efficiency, reducing costs, and addressing logistical complexities. Additionally, the abstract highlights the necessity for further research to address emerging issues and maximize the benefits of UAV technology in the logistics sector.
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 Hajad, Makbul Ihwan Ghazali Nguyen, Thi Thao Uyen Nguyen, Vu Anh Duy Rabbani, Haidar Safitri, Tari Hardiani Sutoyo, Edi Wangi Pandan Sari