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All Journal Jurnal Teknologi dan Manajemen Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika
Sumargo, Ruly
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Automatic OEE Data Collection and Alert System for Food Industry Sumargo, Ruly; Makmur, Amelia
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12953

Abstract

The constant demand for food and beverages to sustain human life drives fierce competition among manufacturers, focusing on product excellence in terms of timeliness, quality, and pricing. The key to competitiveness depends in optimizing manufacturing processes by efficiently utilizing company resources. To ensure the overall optimization and reliable flow of manufacturing processes, a systematic evaluation process must be used, Overall Equipment Efficiency (OEE) stands out as a prominent performance measurement metric in manufacturing process efficiency. OEE serves as a valuable diagnostic tool, exposing areas for improvement and losses transparently. Accurate OEE measurement necessitates the implementation of an automated data collection system with minimum human dependencies, human intervention, and conducting on-the-fly calculations to informed the stakeholder/user. Data quality and accuracy in OEE measurement is very critical. Low quality and accuracy data could lead to false decision. OEE categorizes losses into six groups loss to pinpoint significant factors for potential improvement. Once OEE could be maintain at high level with high data accuracy and right improvement point, an optimum manufacturing process, and cost effective in manufacturing expenses will be achieve. Base on the result comparison for OEE result before and after the system implementation, positive improvement in OEE could reach 8.06%. This scenario be adopted by other company, and could become a model for 1st phase journey in company digital transformation.
Deep Learning for Exchange Rate Prediction Within Time Constrain Sumargo, Ruly; Wasito, Ito
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13633

Abstract

The implementation of an open economic system in Indonesia since 1969 has significant impact to the national economic growth. The high demand and supply of goods from within the country involved in international trade demonstrate a close correlation between export and import activities with the exchange rate of the rupiah. Economic stability is measured through the stability of the rupiah exchange rate against foreign currencies. The balance between demand and supply in the global market is considered crucial for creating a stable economy. History has recorded the Indonesian economic crisis in 1998, where the exchange rate of the rupiah against the US dollar drastically raises and causing challenges to the domestic production cost. This research aiming to make predictions using data science approach based on historical (time series) data. GRU, LSTM, and RNN algorithm being assess to perform the prediction. Results show that RNN algorithms generally outperform GRU and LSTM in making the prediction, particularly with limited time series data. Although RNN is typically superior, in one instance, GRU achieved slightly higher accuracy (0.047% difference) for the CNY to IDR pair over five years. Furthermore, the research highlights the substantial impact of batch size on algorithm accuracy, considering external factors such as interest rates. These findings offer valuable insights for economic decision-making and policy formulation.
Boosting Electronics Manufacturing Efficiency with Automated Data Mining and OEE Process Analytic Sumargo, Ruly; Santoso, Handri
Jurnal Teknologi dan Manajemen Informatika Vol. 10 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v10i1.11377

Abstract

In the last few decades, the industrial sector has experienced rapid growth, driven by increasing demand and intense competition among manufacturers, especially in the electronics sector. This competition focuses on providing superior products with competitive prices, maintained quality, and optimal delivery times. Optimizing manufacturing processes and effectively utilizing company resources have become key to competitiveness in the manufacturing industry. To ensure comprehensive optimization and smooth manufacturing workflows, it is crucial to engage in systematic evaluation and analytical processes. One of the key performance metrics in assessing manufacturing process efficiency is Overall Equipment Efficiency (OEE), which is used to uncover improvement opportunities and inefficient areas. Accurate OEE measurement requires a data mining systems with automated quantitative data collection methods and real-time calculations. These systems visualize process losses in six (pareto) groups, aiding users in analyzing processes and determining process improvements. The implementation of OEE and alert systems for management can bring an 11.82% increase in overall production efficiency. This achievement can serve as a model for other companies embarking on the initial stages of digital transformation processes.
Co-Authors Ito Wasito Santoso, Handri