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All Journal JAIS (Journal of Applied Intelligent System) JAREE (Journal on Advanced Research in Electrical Engineering) Journal of Soft Computing Exploration Industrika : Jurnal Ilmiah Teknik Industri Andalasian International Journal of Applied Science, Engineering, and Technology
Setiawan, Fachruddin Ari
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Journal of Soft Computing Exploration

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Breast tumor classification using adam and optuna model optimization based on CNN architecture Sari, Christy Atika; Rachmawanto, Eko Hari; Daniati, Erna; Setiawan, Fachruddin Ari; Hyperastuty, Agoes Santika; Mintorini, Ery
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.373

Abstract

Breast cancer presents a significant challenge due to its complexity and the urgency of the intervention required to prevent metastasis and potential fatality. This article highlights the innovative application of Convolutional Neural Networks (CNN) in breast tumor classification, marking substantial progress in the field. The key to this advancement is the collaboration among medical professionals, scientists, and artificial intelligence experts, which maximizes the potential of technology. The research involved three phases of training with varying proportions of training data. The first training phase achieved the highest accuracy rate of 99.72%, with an average accuracy of 99.05% in all three phases. Metrics such as precision, recall, and F1 score were also highly satisfactory, underscoring the model's efficacy in accurately classifying breast tumors. Future research aims to develop more complex and precise predictive models by incorporating larger and more representative datasets. This progression promises to improve understanding, prevention, and management of breast cancer, offering hope for significant advances in 2024 and beyond.
Design of smart baby incubator for low-birth-weight newborns Pradana, Dio Alif; Mukhammad, Yanuar; Suharto, Idola Perdana Sulistyoning; Setiawan, Fachruddin Ari
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.494

Abstract

The newborns mortality rate in Indonesia is still quite high, indicated by the neonatal mortality rate (AKN) of 15 per 1000 Live Births, where the target is only below 10 per 1000 Live Births. This mortality rate can be caused by Low-Birth-Weight (BBLR) cases that leads to death. One form of handling for these cases is using a Baby Incubator for intensive cares, which requires monitoring manually and requires the presence of a nurse around the baby incubator so that the condition of the baby incubator room remains stable. Several studies have been conducted and produced a smart incubator system to address these shortcomings. However, most of the smart incubators only focused on monitoring the condition of the incubator room without observing the condition of the baby inside. Based on this, a study was conducted that focused to producing a smart baby incubator that is capable of real-time monitoring of of room conditions (temperature, humidity, and oxygen levels) and baby conditions (temperature, heart rate, oxygen saturation, baby crying, and baby visuals) by applying the Internet of Things (IoT). The results of this study have the largest number of parameters monitored compared to previous studies.
Co-Authors Christy Atika Sari Eko Hari Rachmawanto Erna Daniati Ery Mintorini Hyperastuty, A. Santika Hyperastuty, Agoes Santika Inayah, Lailatul Muhammad Fuad Nandang, Iim Pradana, Dio Alif Puspitasari, Rahma Ajeng Rahma, Naufa Aulia Sulistyoning Suharto, Idola Perdana Trihastuti Agustinah winarti, eko Yanuar Mukhammad