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All Journal Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Widyo Laksono, Pringgo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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Discrete Event Simulation Approach for Pharmaceutical Industry Calibration Laboratory Service Digital Twin Model Amrih, Pitoyo; Widyo Laksono, Pringgo
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4230

Abstract

This research aims to develop a simulation theory using discrete event simulation as a convincing tool for building a digital twin models in the pharmaceutical industry calibration department as part of the supporting components enabler for industry 4.0. The Methodology used are define the Calibration Laboratory service model, then analyze the big data collected from the service performance parameters. The analyzed data will be used to continue the development of discrete event simulation models for calibration laboratory service systems using ProModel2016 software. The simulation output data will be verified with real event data to ensure similarity. The study finds that the Discrete Event Simulation approach can be used as a convincing tool to develop digital twin models as virtual replicas of Calibration Laboratory Services in the Pharmaceutical Industry so that improvement planning can be analyzed efficiently. There is a limitation of this research that the digital twin model can only be verified for the Pharmaceutical Industry Calibration Laboratory Services as a case study object. Further research needs to be carried out to expand the possibilities of using this discrete event simulation approach for Digital Twin Model in every aspect of industrial activities.
Discrete Event Simulation Approach for Pharmaceutical Industry Calibration Laboratory Service Digital Twin Model Amrih, Pitoyo; Widyo Laksono, Pringgo
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4230

Abstract

This research aims to develop a simulation theory using discrete event simulation as a convincing tool for building a digital twin models in the pharmaceutical industry calibration department as part of the supporting components enabler for industry 4.0. The Methodology used are define the Calibration Laboratory service model, then analyze the big data collected from the service performance parameters. The analyzed data will be used to continue the development of discrete event simulation models for calibration laboratory service systems using ProModel2016 software. The simulation output data will be verified with real event data to ensure similarity. The study finds that the Discrete Event Simulation approach can be used as a convincing tool to develop digital twin models as virtual replicas of Calibration Laboratory Services in the Pharmaceutical Industry so that improvement planning can be analyzed efficiently. There is a limitation of this research that the digital twin model can only be verified for the Pharmaceutical Industry Calibration Laboratory Services as a case study object. Further research needs to be carried out to expand the possibilities of using this discrete event simulation approach for Digital Twin Model in every aspect of industrial activities.
Deep Learning Approach for Palm Oil Fresh Fruit Bunches Harvest Decision Athallah Adriyansyah, Yusuf; Adriyanto, Feri; Widyo Laksono, Pringgo
Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 1 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.7.1.100897

Abstract

The efficiency of palm oil harvesting is crucial to ensuring optimal yield and quality of fresh fruit bunches (FFB). Traditional manual harvesting methods often result in inconsistent outcomes due to human error and subjectivity in ripeness evaluation. This study proposes an intelligent, image-based harvesting decision system that utilizes Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) to automate the classification of palm oil FFB ripeness. High-resolution images of palm fruit are processed using Python-based frameworks (Google Colab 3.10.12, YOLOv8) to extract features such as color and texture, which are then used to train the CNN and SVM models. The system architecture includes stages for image acquisition, preprocessing, feature extraction, classification, and decision-making. Both CNN and SVM were evaluated for performance using accuracy, precision, recall, and F1-score. The experimental results demonstrated high classification accuracy, with CNN achieving an average of 0.97 and the highest result recorded at 0.89. The system significantly enhances harvesting decision accuracy and reduces dependence on manual inspection. This study demonstrates the viability of using deep learning and machine learning algorithms for real-time agricultural decision-making. The integration of CNN and SVM not only improves productivity but also contributes to sustainable practices by reducing waste and labor intensity. The proposed system offers a scalable solution that can be adapted for broader smart farming applications, supporting national goals of digital transformation and energy efficiency in agriculture.
Co-Authors Adriyanto, Feri Athallah Adriyansyah, Yusuf Pitoyo Amrih