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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) Emerging Science Journal Insect (Informatics and Security) : Jurnal Teknik Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS Literasi Hukum Buletin Ilmiah Sarjana Teknik Elektro International Journal of Robotics and Control Systems Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia Journal of Advanced Health Informatics Research SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Purwono, Purwono
Universitas Harapan Bangsa
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : International Journal of Robotics and Control Systems

 1 
Evaluation of Stochastic Gradient Descent Optimizer on U-Net Architecture for Brain Tumor Segmentation Purwono Purwono; Iis Setiawan Mangkunegara
International Journal of Robotics and Control Systems Vol 3, No 3 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i3.1104

Abstract

A brain tumor is a type of disease that is quite dangerous in the world. This disease is one of the main causes of human death and has a high risk of recurrence. There are several types of brain tumor locations such as edema, necrosis to elevation. Segmenting the location of this disease is important to do to support faster recovery efforts. The Convolutional Neural Network (CNN) algorithm, which is part of the deep learning method, can be an alternative to this segmentation effort. The U-Net architecture is part of the CNN algorithm which specifically works on medical image segmentation. This study experimented to build a special U-Net architecture for medical image segmentation that had been optimized with SGD. The data used is BraTS2020O which contains a collection of MRI data. This optimization aims to improve the performance of the U-net architecture for segmenting brain tumor images. The results of the study show that the SGD optimization carried out has succeeded in providing better performance than previous studies. This can be seen from the performance value obtained at 0.9879. This accuracy value indicates an increase in accuracy from previous studies. High accuracy indicates that the SGD-optimized model has good segmentation prediction performance.
Implementation of LoRa Wireless Communication in Smart Diabetic Shoes Design Purwono Purwono; Asmat Burhan; Lutviana Lutviana
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1152

Abstract

Diabetes is a widespread medical condition affecting a substantial portion of the global population. It is a metabolic disorder known as diabetes mellitus, characterized by severe fluctuations in blood glucose levels due to inadequate insulin production in the human body. The effective monitoring of diabetes is of paramount importance to researchers as it holds the potential to enhance the quality of healthcare services. Among the challenges faced by individuals with diabetes, one prevalent issue is the development of ulcers, which can be challenging to detect promptly. Technological advancements offer a promising avenue for cost-effective and continuous monitoring of chronic diseases like diabetes. This study centers on the development of IoT-based intelligent diabetic footwear that incorporates pressure sensors and temperature monitoring for individuals with diabetic feet. In the evaluation of SX1278 (Ra-02) Lora Module communication, deployed at eight different locations, it was observed that two of these points achieved a flawless transmission success rate of 100%. Conversely, the communication failed at points 6 and 7, with a success rate falling below 50%. Some of these failures can be attributed to signal obstructions, including natural elements such as trees and terrain, as well as man-made structures like buildings and machinery workshops, which hinder the efficient transmission of signals from the end node to the gateway. The results of this research provide positive implications in the form of developing IoT-based diabetes shoes that can be applied with alternative Lora communication connections in areas with poor internet signal.
Co-Authors Agung Budi Prasetio Agung Pangestu Ahmad Toha Alfian Ma’arif Amanah Wulandari Anggit Wirasto Anggit Wirasto Ariefah Khairina Islahati Arif Setia Sandi A. Asmat Burhan Asmat Burhan Bala Putra Dewa Bala Putra Dewa Bala Putra Dewa Bala Putra Dewa Barlian Kristanto Burhanuddin bin Mohd Aboobaider Deny Nugroho Triwibowo Dewi Astria Faroek Dimas Febri Kuncoro Dimas Herjuno Eko Ariyanto Elsa Wulandari, Annastasya Nabila Endang Setyawati Hadi Jayusman Hesti Ayu Wahyuni Iin Dyah Indrawati Iis Setiawan Mangkunegara Iis Setyawan Mangku Negara Imam Ahmad Ashari Imam Ahmad Ashari, Imam Ahmad Imam Riadi Imam Riadi Jatmiko Indriyanto Jihad Rahmawan Khoirun Nisa Khoirun Nisa Khoirun Nisa Lutviana Lutviana Lutviana Mangku Negara, Iis Setiawan Mangkunegara, Iis Setiawan Marlia Hafny Afrilies Maya Ruhtiani Muchammad Naseer Muhammad Baballe Ahmad Muhammad Haikal Satria Muntiari, Novita Ranti Musafa Widagdo Pramesti Dewi Qazi Mazhar ul Haq Rahmadhani, Berlina Riska Suryani Riyanarto Sarno Rosyid Ridlo Al-Hakim Rubaeah, Siti Rusydi Umar Safar Dwi Kurniawan Sandi Najib Iskandar Sharkawy , Abdel-Nasser Slamet Slamet Sony Kartika Wibisono Sony Kartika Wibisono Sony Kartika Wibisono Sony Kartika Wibisono Supriyatin Supriyatin Tohari Ahmad Tusaria Tri Wahyu Ningrum Wahyu Rahmaniar Windu Gata Yanuar Zulardiansyah Arief Yudhistira , Aimar Yuris Tri Naili Yuris Tri Naili Yuslena Sari, Yuslena Yusuf Fadlila Rahman