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All Journal Jurnal Admmirasi Journal of Student Research International Journal of Mechanical, Industrial and Control Systems Engineering
Siti Shofiah
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Public Health Social Sciences

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Patient and Specimen Identification in Laboratory Unit of PKU Muhammadiyah Gamping Hospital Siti Shofiah; Sri Sundari; Qurratul Aini
Jurnal Admmirasi Vol 3 No 2 (2018): Desember
Publisher : Program Studi Magister Manajemen Rumah Sakit, Jenjang Pasca Sarjana (S2), Pasca Sarjana Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47638/admmirasi.v3i2.37

Abstract

Laboratory is one of the main supporting departemen in hospital services.This study aims to determine the implementation of patient and specimens identification based on SOP (standard operating procerude) in laboratory departemen. This research used mixed methods research which are quantitative and qualitative method. Quantitative data obtained by moment observation using check list and qualitative data obtained by interview. The implementation of patient identification and specimen in the laboratory of PKU Muhammadiyah Gamping Hospital has not fully complied with SOP. According to 100 moment observation, patient data on the laboratory request form is 77% incomplete, 74% laboratory officers confirmed the patient's identity correctly, 84% laboratory officers verified the name and date of birth of the patient and only 45% laboratory officers placed the verified labels on the specimen tube simultaneously with the patient's presence. Barriers in the implementation are less of culture in the patient safety especially patient and specimen identification, uncomplete in the request form, the number of requests for laboratory examination and less of evaluation. Hospitals should provide maximum support to the application of patient safety culture, provide adequate numbers of health personnel, improvement of facilities and infrastructure, policy improvement, training, and evaluation.
Pendekatan Machine Learning untuk Analisis dan Visualisasi Data Jembatan Timbang Siti Shofiah; Faris Humami; M. Iman Nur Hakim; Azimatun Lissyifa; Agus Siswono
Journal of Student Research Vol. 2 No. 1 (2024): Januari: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v2i1.2666

Abstract

In this research, a machine learning approach, especially a decision tree model, is implemented to improve the analysis and visualization of weighbridge data in Indonesia. The evaluation results show that the decision tree model provides better insight in predicting the carrying capacity, dimensions and loading procedures of vehicles. The advantage of this model lies in its combination of low Mean Squared Error (MSE) and high R-squared, indicating its effectiveness in capturing data variance and providing accurate predictions. The use of decision tree models can be a valuable tool in improving the visualization of bridge weighing data, allowing users to gain additional insights and understand the complex dynamics within the data. In addition, the model's adaptability to various types of data makes it a versatile analysis tool. The positive implications of using this model open up opportunities to understand more deeply the logic of predictions and make more informed decisions. As a suggestion, increasing the number and quality of weighing equipment, wider application of information and communication technology, human resource training, and cross-sector collaboration can further strengthen weighbridge management in Indonesia.
Integrated Digital Twin and Physics Informed Machine Learning Model for Real Time Performance Prediction of Industrial Mechanical Systems Irlon Irlon; Siti Shofiah; Helmi Wibowo; Erick Fernando; Genrawan Hoendarto; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 2 (2025): June :IJMICSE: International Journal of Mechanical, Industrial and Control Syst
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i2.404

Abstract

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.
Co-Authors Agus Siswono Azimatun Lissyifa Erick Fernando Faris Humami Genrawan Hoendarto Helmi Wibowo Irlon Irlon M. Iman Nur Hakim Mursalim Mursalim Qurratul Aini Sri Sundari