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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) The IJICS (International Journal of Informatics and Computer Science)
Sahputra, Fajar
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Materials Science & Nanotechnology Physics

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Performance Analysis of CNN (Convolutional Neural Network) in Nominal Classification of Rupiah Emissions 2022 Sahputra, Fajar; Sitorus, Zulham; Iqbal, Muhammad; Marlina, Leni; Nasution, Darmeli
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8903

Abstract

This study aims to analyze the performance of Convolutional Neural Network (CNN) algorithm in classifying the nominal of Rupiah banknotes issued in 2022. Three test models are developed, namely two CNN architectures with different optimizers (Adam and RMSprop), and one transfer learning model using VGG16. The dataset used consists of 1,848 banknote images of seven denominations: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. The data was collected using a smartphone camera and processed through augmentation, normalization, and classification stages. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that CNN with Adam's optimizer achieves a validation accuracy of 98.97%, while CNN with RMSprop reaches 99.59%. Meanwhile, the VGG16 model achieved perfect validation accuracy of 100%, with precision, recall, and F1-score values of 1.00 each. These results show that the transfer learning approach provides the best performance compared to conventional CNN models. This research supports the development of an accurate and efficient banknote recognition automation system for digital finance applications.
Analysis of Implementation of Infection Prevention and Control at Royal Prima Marelan General Hospital Sahputra, Fajar; Girsang, Ermi; Napiah Nasution, Ali
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.5421

Abstract

Nosocomial infections or Healthcare-Associated Infections (HAIs) are a serious problem for global public health, including Indonesia. HAIs are infections experienced by patients during treatment and health care procedures ≥ 48 hours and ≤ 30 days after leaving the health facility. This study aims to analyze the implementation of infection prevention and control at the Royal Prima Marelan General Hospital. This research is a quantitative study with an observational analytical approach with a Cross Sectional Study design, namely in the form of data collection aimed at analyzing the implementation of infection prevention and control at the Royal Prima Marelan General Hospital for the period July to August 2023. The population in this study was 100 people who included hospital management who were considered competent in providing information. The population sample for this study that met the inclusion and exclusion criteria was 50 people. The results of bivariate analysis show that the variables related to infection prevention and control are knowledge, motivation, supervision and workload. The results of multivariate analysis show that the variable that partially influences the prevention and control of nosocomial infections in hospitals is knowledge.
Co-Authors Girsang, Ermi Leni Marlina Muhammad Iqbal Napiah Nasution, Ali Nasution, Darmeli Sitorus, Zulham