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All Journal Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Muhammad Rafi Solakhudin
Shipbuilding Institute of Polytechnic Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Implementation OEE in Integrating Siemens S7-1200 Data with Odoo ERP Muhammad Rafi Solakhudin; Muhammad Khoirul Hasin; Ii Munadhif
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 11 No 2 (2024): Jurnal Ecotipe, October 2024
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v11i2.4491

Abstract

The integration of perational Technology (OT) and Information Technology (IT) is crucial for enterprise-level decision-making regarding company effectiveness and productivity. This integration is achieved by developing an integrator system in the form of an API service, which facilitates the connection between Programmable Logic Controller (PLC) and Odoo Enterprise Resource Planning (ERP). Integration allows IT systems synergize with OT systems to processing data obtained from the performance of industrial machines. Analysis was conducted using the Overall Equipment Effectiveness (OEE) method to monitor the company’s production outcomes. A prototype sorting system with PLC for controlling was created as an OEE implementation. The API service acquires data from the plant and performs preliminary analysis in Odoo ERP. The analysis in Odoo ERP is conducted through the development of a manufacturing module that applies the OEE method calculations to monitor the effectiveness and efficiency of production procces. Once the calculation result is obtained, these data are monitored on Grafana in the form of pie charts and graphs. This research yielded insight into the design and implementation of an integration system for the bottle sorting process. The result also could be monitored in real-time using Odoo ERP and Grafana. Furthermore, the OEE analysis was matched with manual calculations in order to ensure its validity. Thus, this research enables the production process to be optimized and provides significant benefits to the company.
Testing Smoker Detection Using Google Cloud Services and Infrastructure Muhammad Mustajib; Sri Gunawan; Aldo Lovely Arief Suyoso; Hendro Margono; Muhammad Rafi Solakhudin
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 11 No 2 (2024): Jurnal Ecotipe, October 2024
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v11i2.4499

Abstract

Smoking remains a significant public health challenge globally, contributing to a wide range of detrimental health outcomes including cardiovascular diseases, cancer, and respiratory disorders. Despite concerted efforts to curb smoking rates through policy interventions, effective monitoring and enforcement remain complex and resource-intensive tasks for health authorities and organizations. Innovative approaches leveraging advanced technologies such as visual detection systems powered by deep learning offer promising solutions to enhance smoking behavior detection and monitoring. Integrating the Google Cloud Vision API enables real-time identification of smoking indicators and discrimination from complex visual backgrounds. This capability not only supports proactive health monitoring but also strengthens the enforcement of public health policies aimed at reducing smoking prevalence. The research methodology utilizes a dataset of 600 images sourced from the Kaggle platform, encompassing diverse scenarios to optimize model training. Techniques such as image segmentation, feature extraction, and machine learning-based classification are employed to achieve high levels of precision and recall in identifying smokers and cigarette smoke. Despite the advantages of scalability, robust infrastructure, and high availability facilitated by cloud computing, the study acknowledges challenges such as bandwidth constraints and security risks associated with handling sensitive health data. Nevertheless, technological innovations in visual detection systems and cloud services are underscored as pivotal in mitigating the health impacts of smoking and advancing public health initiatives.
Co-Authors Aldo Lovely Arief Suyoso Hendro Margono Ii Munadhif Muhammad Khoirul Hasin Muhammad Mustajib Solly Aryza