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All Journal JURNAL PENDIDIKAN TAMBUSAI Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Decode: Jurnal Pendidikan Teknologi Informasi JOURNAL OF ICT APLICATIONS AND SYSTEM Jurnal Informatika Ekonomi Bisnis
Prasiwiningrum, Elyandri
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Journal : JOURNAL OF ICT APLICATIONS AND SYSTEM

 1 
Application Method Certainty Factor in Electrical Damage Zulkifli, Akhmad; Riandini, Meisarah; Hayadi, B. Herawan; Prasiwiningrum, Elyandri
Journal of ICT Applications System Vol 2 No 1 (2023): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v2i1.236

Abstract

Electricity is need main For life people human . Electricity is used man For various type activity human . Electricity plays a big role for life , like For lighting , cooking , and so on . Almost all activity daily use electricity . Almost every home in Indonesia, both in the city nor village Already trellis with electricity . For stream and distribute electricity to each home , office nor distant institutions _ away , then needed Transformer Distribution . Transformer Distribution This own objective use special that is, to lower voltage tall to voltage low , so that the voltage used in accordance with equipment ratings electricity customer or load in general . For help in handle problem damage Transformer distribution , then one is needed branch from Knowledge computer that is System Expert . System Expert is system based computer that uses knowledge , facts , and techniques reasoning in solve problem , which usually is only can completed by one expert in field certain . (Putri, 2020). The method used in research _ This is Certainty Factor. Study This apply certainty factor method For role in diagnose damage to electricity . Based on results discussion on with choose one _ damage namely P1 ( Oil transformer go out from the transformer body ) on the study case obtained decision level accuracy that is as big That's 5.650198%. means system expert certainty factor method can overcome damage and deliver results diagnosis good at damage electricity
Projection and Visualization of Health Worker Data in Indonesia (2015–2017) Using Google Looker Studio Prasiwiningrum, Elyandri
Journal of ICT Applications System Vol 2 No 2 (2023): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v2i2.398

Abstract

Projection of health worker data in Indonesia during 2015–2017 highlights the need for efficient workforce planning to address rising healthcare demands. This study examines the distribution and projection of ten healthcare professions, including nurses, midwives, pharmacists, and general practitioners, using Google Looker Studio for visualization. Secondary data analysis was utilized to process information obtained from credible sources, ensuring relevance and accuracy. The challenge lies in understanding workforce disparities and predicting future needs to optimize hospital operations effectively. Google Looker Studio was employed to create interactive dashboards, simplifying data interpretation and enhancing decision-making capabilities. Results indicate a continuous increase in healthcare personnel over time, with 2,172 professionals recorded during the observed period. Visualization provides insights into workload distribution and the adequacy of healthcare workers across regions. This research offers a scalable solution for projecting workforce trends and supports long-term healthcare planning in Indonesia
Automatic Food Label Detection in Images Using Convolutional Neural Network with Food-101 Dataset Natasya, Ccely; Aisyah, Nur; Prasiwiningrum, Elyandri; Yulfita Aini
Journal of ICT Applications System Vol 4 No 1 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i1.432

Abstract

automatic detection of food labels from digital images has emerged as a crucial application in dietary analysis, nutrition monitoring, and smart culinary systems. This study presents the implementation of a Convolutional Neural Network (CNN) model for food label recognition using the Food-101 dataset, which consists of over 101,000 images from 101 distinct food categories. The proposed system follows a systematic pipeline that includes image resizing, normalization, and data augmentation to enhance model robustness and performance. The CNN architecture is designed with multiple convolutional and pooling layers, followed by dense and softmax output layers for final classification. The training was conducted using the Adam optimizer with a learning rate of 0.0001, batch size of 32, and dropout regularization to prevent overfitting. Experimental results demonstrate a classification accuracy of 24.45% after one training epoch, highlighting both the capability and limitations of the baseline CNN model. Despite moderate accuracy, the model successfully identifies visually distinguishable food items and sets a foundation for future improvements through transfer learning and fine-tuning. This research confirms the potential of CNN-based models for food label detection and provides insights for the development of more accurate food recognition systems in health, dietary, and culinary applications
Co-Authors Adyanata Lubis Akhmad Zulkifli Ari Aprilia Dwiana B. Herawan Hayadi Fifto Nugroho Muslim Muslim Natasya, Ccely Nur Aisyah Putu Doddy Heka Ardana Qurrotul Aini Rasna, Rasna Riandini, Meisarah Yulfita Aini